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	<title>bioRxiv Channel: UK Biobank</title>
	<link>https://biorxiv.org</link>
	<description>
	This feed contains articles for bioRxiv Channel "UK Biobank"
	</description>

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	<prism:publicationName>bioRxiv</prism:publicationName>
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	<title>bioRxiv</title>
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	<link>https://biorxiv.org</link>
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	<item rdf:about="https://biorxiv.org/cgi/content/short/031120v1?rss=1">
<title>
<![CDATA[
Shared genetic aetiology between cognitive functions and physical and mental health in UK Biobank (N = 112 151) and 24 GWAS consortia. 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/031120v1?rss=1"
</link>
<description><![CDATA[
The causes of the known associations between poorer cognitive function and many adverse neuropsychiatric outcomes, poorer physical health, and earlier death remain unknown. We used linkage disequilibrium regression and polygenic profile scoring to test for shared genetic aetiology between cognitive functions and neuropsychiatric disorders and physical health. Using information provided by many published genome-wide association study consortia, we created polygenic profile scores for 24 vascular-metabolic, neuropsychiatric, physiological-anthropometric, and cognitive traits in the participants of UK Biobank, a very large population-based sample (N = 112 151). Pleiotropy between cognitive and health traits was quantified by deriving genetic correlations using summary genome-wide association study statistics applied to the method of linkage disequilibrium regression. Substantial and significant genetic correlations were observed between cognitive test scores in the UK Biobank sample and many of the mental and physical health-related traits and disorders assessed here. In addition, highly significant associations were observed between the cognitive test scores in the UK Biobank sample and many polygenic profile scores, including coronary artery disease, stroke, Alzheimer's disease, schizophrenia, autism, major depressive disorder, BMI, intracranial volume, infant head circumference, and childhood cognitive ability. Where disease diagnosis was available for UK Biobank participants we were able to show that these results were not confounded by those who had the relevant disease. These findings indicate that a substantial level of pleiotropy exists between cognitive abilities and many human mental and physical health disorders and traits and that it can be used to predict phenotypic variance across samples.
]]></description>
<dc:creator>Saskia P Hagenaars</dc:creator>
<dc:creator>Sarah E Harris</dc:creator>
<dc:creator>Gail Davies</dc:creator>
<dc:creator>William David Hill</dc:creator>
<dc:creator>David CM Liewald</dc:creator>
<dc:creator>Stuart J Ritchie</dc:creator>
<dc:creator>Riccardo E Marioni</dc:creator>
<dc:creator>Chloe Fawns-Ritchie</dc:creator>
<dc:creator>Breda Cullen</dc:creator>
<dc:creator>Rainer Malik</dc:creator>
<dc:creator>METASTROKE consortium</dc:creator>
<dc:creator>International Consortium for Blood Pressure GWAS</dc:creator>
<dc:creator>SpiroMeta consortium</dc:creator>
<dc:creator>CHARGE consortium Pulmonary Group</dc:creator>
<dc:creator>CHARGE consortium Aging and Longevity Group</dc:creator>
<dc:creator>Bradford B Worrall</dc:creator>
<dc:creator>Cathie LM Sudlow</dc:creator>
<dc:creator>Joanna M Wardlaw</dc:creator>
<dc:creator>John Gallacher</dc:creator>
<dc:creator>Jill Pell</dc:creator>
<dc:creator>Andrew M McIntosh</dc:creator>
<dc:creator>Daniel J Smith</dc:creator>
<dc:creator>Catharine R Gale</dc:creator>
<dc:creator>Ian J Deary</dc:creator>
<dc:creator></dc:creator>
<dc:date>2015-11-10</dc:date>
<dc:identifier>doi:10.1101/031120</dc:identifier>
<dc:title><![CDATA[Shared genetic aetiology between cognitive functions and physical and mental health in UK Biobank (N = 112 151) and 24 GWAS consortia.]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2015-11-10</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/031138v1?rss=1">
<title>
<![CDATA[
Pleiotropy between neuroticism and physical and mental health: findings from 108 038 men and women in UK Biobank 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/031138v1?rss=1"
</link>
<description><![CDATA[
There is considerable evidence that people with higher levels of the personality trait neuroticism have an increased risk of several types of mental disorder. Higher neuroticism has also been associated, less consistently, with increased risk of various physical health outcomes. We hypothesised that these associations may, in part, be due to shared genetic influences. We tested for pleiotropy between neuroticism and 12 mental and physical diseases or health traits using linkage disequilibrium regression and polygenic profile scoring. Genetic correlations were derived between neuroticism scores in 108 038 people in UK Biobank and health-related measures from 12 large genome-wide association studies (GWAS). Summary information for the 12 GWAS was used to create polygenic risk scores for the health-related measures in the UK Biobank participants. Associations between the health-related polygenic scores and neuroticism were examined using regression, adjusting for age, sex, genotyping batch, genotyping array, assessment centre, and population stratification. Genetic correlations were identified between neuroticism and anorexia nervosa (rg = 0.17), major depressive disorder (rg = 0.66) and schizophrenia (rg = 0.21). Polygenic risk for several health-related measures were associated with neuroticism, in a positive direction in the case of bipolar disorder ({beta} = 0.017), major depressive disorder ({beta} = 0.036), schizophrenia ({beta} = 0.036), and coronary artery disease ({beta} = 0.011), and in a negative direction in the case of BMI ({beta} = -0.0095). These findings indicate that a high level of pleiotropy exists between neuroticism and some measures of mental and physical health, particularly major depressive disorder and schizophrenia.
]]></description>
<dc:creator>Catharine Gale</dc:creator>
<dc:creator>Saskia P Hagenaars</dc:creator>
<dc:creator>Gail Davies</dc:creator>
<dc:creator>W David Hill</dc:creator>
<dc:creator>David CM Liewald</dc:creator>
<dc:creator>Breda Cullen</dc:creator>
<dc:creator>International Consortium for Blood Pressure GWAS</dc:creator>
<dc:creator>CHARGE consortium Aging and Longevity Group</dc:creator>
<dc:creator>Jill Pell</dc:creator>
<dc:creator>Andrew M McIntosh</dc:creator>
<dc:creator>Daniel J Smith</dc:creator>
<dc:creator>Ian J Deary</dc:creator>
<dc:creator>Sarah Harris</dc:creator>
<dc:creator></dc:creator>
<dc:date>2015-11-10</dc:date>
<dc:identifier>doi:10.1101/031138</dc:identifier>
<dc:title><![CDATA[Pleiotropy between neuroticism and physical and mental health: findings from 108 038 men and women in UK Biobank]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2015-11-10</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/039545v1?rss=1">
<title>
<![CDATA[
Intelligence and neuroticism in relation to depression and psychological distress: evidence of interaction using data from Generation Scotland: Scottish Family Health Study and UK Biobank 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/039545v1?rss=1"
</link>
<description><![CDATA[
BackgroundNeuroticism is a risk factor for selected mental and physical illnesses and is inversely associated with intelligence. Intelligence appears to interact with neuroticism and mitigate its detrimental effects on physical health and mortality. However, the inter-relationships of neuroticism and intelligence for major depressive disorder (MDD) and psychological distress has not been well examined.nnMethodsAssociations and interactions between neuroticism and general intelligence (g) on MDD and psychological distress were examined in two population-based cohorts: Generation Scotland: Scottish Family Health Study (GS:SFHS, N=19,200) and UK Biobank (N=90,529). The Eysenck Personality Scale Short Form-Revised measured neuroticism and g was extracted from multiple cognitive ability tests in each cohort. Family structure was adjusted for in GS:SFHS.nnResultsNeuroticism was associated with MDD and psychological distress in both samples. A significant interaction between neuroticism and g in predicting MDD status was found in UK Biobank (OR = 0.96, p < .01), suggesting that higher g ameliorated the adverse effects of neuroticism on the likelihood of having MDD. This interaction was not found in GS:SFHS. In both samples, higher neuroticism and lower intelligence were associated with increased psychological distress. A significant interaction was also found in both cohorts (GS:SFHS: {beta} = -0.05, p < .01; UK Biobank: {beta} = -0.02, p < .01), such that intelligence protected against the deleterious effect of neuroticism on psychological distress.nnConclusionsFrom two large cohort studies, our findings suggest intelligence acts a protective factor in mitigating the effects of neuroticism on risk for depressive illness and psychological distress.
]]></description>
<dc:creator>Lauren B Navrady</dc:creator>
<dc:creator>Stuart J Ritchie</dc:creator>
<dc:creator>Stella WY Chan</dc:creator>
<dc:creator>Daniel M Kerr</dc:creator>
<dc:creator>Mark J Adams</dc:creator>
<dc:creator>Emma Hawkins</dc:creator>
<dc:creator>David J Porteous</dc:creator>
<dc:creator>Ian J Deary</dc:creator>
<dc:creator>Catherine R Gale</dc:creator>
<dc:creator>David G Batty</dc:creator>
<dc:creator>Andrew M McIntosh</dc:creator>
<dc:creator></dc:creator>
<dc:date>2016-02-12</dc:date>
<dc:identifier>doi:10.1101/039545</dc:identifier>
<dc:title><![CDATA[Intelligence and neuroticism in relation to depression and psychological distress: evidence of interaction using data from Generation Scotland: Scottish Family Health Study and UK Biobank]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2016-02-12</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/080283v1?rss=1">
<title>
<![CDATA[
Association of polygenic risk for major psychiatric illness with subcortical volumes and white matter integrity in UK Biobank 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/080283v1?rss=1"
</link>
<description><![CDATA[
Major depressive disorder (MDD), schizophrenia (SCZ) and bipolar disorder (BP) are common, disabling and heritable psychiatric diseases with a complex overlapping polygenic architecture. Individuals with these disorders, as well as their unaffected relatives, show widespread structural differences in corticostriatal and limbic networks. Structural variation in many of these brain regions is also heritable and polygenic but whether their genetic architecture overlaps with major psychiatric disorders is unknown. We sought to address this issue by examining the impact of polygenic risk of MDD, SCZ, and BP on subcortical brain volumes and white matter (WM) microstructure in a large single sample of neuroimaging data; the UK Biobank Imaging study. The first release of UK Biobank imaging data compromised participants with overlapping genetic data and subcortical volumes (N = 978) and WM measures (N = 816). Our, findings however, indicated no statistically significant associations between either subcortical volumes or WM microstructure, and polygenic risk for MDD, SCZ or BP. In the current study, we found little or no evidence for genetic overlap between major psychiatric disorders and structural brain measures. These findings suggest that subcortical brain volumes and WM microstructure may not be closely linked to the genetic mechanisms of major psychiatric disorders.
]]></description>
<dc:creator>Lianne M Reus</dc:creator>
<dc:creator>Xueyi Shen</dc:creator>
<dc:creator>Jude Gibson</dc:creator>
<dc:creator>Ella Wigmore</dc:creator>
<dc:creator>Lannie Ligthart</dc:creator>
<dc:creator>Mark J Adams</dc:creator>
<dc:creator>Gail Davies</dc:creator>
<dc:creator>Simon R Cox</dc:creator>
<dc:creator>Saskia P Hagenaars</dc:creator>
<dc:creator>Mark E Bastin</dc:creator>
<dc:creator>Ian J Deary</dc:creator>
<dc:creator>Heather C Whalley</dc:creator>
<dc:creator>Andrew M McIntosh</dc:creator>
<dc:creator></dc:creator>
<dc:date>2016-10-12</dc:date>
<dc:identifier>doi:10.1101/080283</dc:identifier>
<dc:title><![CDATA[Association of polygenic risk for major psychiatric illness with subcortical volumes and white matter integrity in UK Biobank]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2016-10-12</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/059352v1?rss=1">
<title>
<![CDATA[
Do Regional Brain Volumes and Major Depressive Disorder Share Genetic Architecture: a study in Generation Scotland (n=19,762), UK Biobank (n=24,048) and the English Longitudinal Study of Ageing (n=5,766) 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/059352v1?rss=1"
</link>
<description><![CDATA[
Major depressive disorder (MDD) is a heritable and highly debilitating condition. It is commonly associated with subcortical volumetric abnormalities, the most replicated of these being reduced hippocampal volume. Using the most recent published data from ENIGMA consortiums genome-wide association study (GWAS) of regional brain volume, we sought to test whether there is shared genetic architecture between 8 subcortical brain volumes and MDD. Using LD score regression utilising summary statistics from ENIGMA and the Psychiatric Genomics Consortium, we demonstrated that hippocampal volume was positively genetically correlated with MDD (rG=0.46, P=0.02), although this did not survive multiple comparison testing. None of other six brain regions studied were genetically correlated and amygdala volume heritability was too low for analysis. We also generated polygenic risk scores (PRS) to assess potential pleiotropy between regional brain volumes and MDD in three cohorts (Generation Scotland; Scottish Family Health Study (n=19,762), UK Biobank (n=24,048) and the English Longitudinal Study of Ageing (n=5,766). We used logistic regression to examine volumetric PRS and MDD and performed a meta-analysis across the three cohorts. No regional volumetric PRS demonstrated significant association with MDD or recurrent MDD. In this study we provide some evidence that hippocampal volume and MDD may share genetic architecture, albeit this did not survive multiple testing correction and was in the opposite direction to most reported phenotypic correlations. We therefore found no evidence to support a shared genetic architecture for MDD and regional subcortical volumes.
]]></description>
<dc:creator>Eleanor M Wigmore</dc:creator>
<dc:creator>Toni-Kim Clarke</dc:creator>
<dc:creator>Mark J Adams</dc:creator>
<dc:creator>Ana M Fernandez-Pujals</dc:creator>
<dc:creator>Jude Gibson</dc:creator>
<dc:creator>Gail Davies</dc:creator>
<dc:creator>Lynsey S Hall</dc:creator>
<dc:creator>Yanni Zeng</dc:creator>
<dc:creator>Philippa A Thomson</dc:creator>
<dc:creator>Caroline Hayward</dc:creator>
<dc:creator>Blair H Smith</dc:creator>
<dc:creator>Lynne J Hocking</dc:creator>
<dc:creator>Sandosh Padmanabhan</dc:creator>
<dc:creator>Ian J Deary</dc:creator>
<dc:creator>David J Porteous</dc:creator>
<dc:creator>Kristin K Nicodemus</dc:creator>
<dc:creator>Andrew M McIntosh</dc:creator>
<dc:creator></dc:creator>
<dc:date>2016-06-16</dc:date>
<dc:identifier>doi:10.1101/059352</dc:identifier>
<dc:title><![CDATA[Do Regional Brain Volumes and Major Depressive Disorder Share Genetic Architecture: a study in Generation Scotland (n=19,762), UK Biobank (n=24,048) and the English Longitudinal Study of Ageing (n=5,766)]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2016-06-16</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/032417v1?rss=1">
<title>
<![CDATA[
Genome-wide analysis of over 106,000 individuals identifies 9 neuroticism-associated loci 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/032417v1?rss=1"
</link>
<description><![CDATA[
Neuroticism is a personality trait of fundamental importance for psychological wellbeing and public health. It is strongly associated with major depressive disorder (MDD) and several other psychiatric conditions. Although neuroticism is heritable, attempts to identify the alleles involved in previous studies have been limited by relatively small sample sizes and heterogeneity in the measurement of neuroticism. Here we report a genome-wide association study of neuroticism in 91,370 participants of the UK Biobank cohort and a combined meta-analysis which includes a further 7,197 participants from the Generation Scotland Scottish Family Health Study (GS:SFHS) and 8,687 participants from a Queensland Institute of Medical Research (QIMR) cohort. All participants were assessed using the same neuroticism instrument, the Eysenck Personality Questionnaire-Revised (EPQ-R-S) Short Forms Neuroticism scale. We found a SNP-based heritability estimate for neuroticism of approximately 15% (SE = 0.7%). Meta-analysis identified 9 novel loci associated with neuroticism. The strongest evidence for association was at a locus on chromosome 8 (p = 1.28x10-15) spanning 4 Mb and containing at least 36 genes. Other associated loci included genes of interest on chromosome 1 (GRIK3, glutamate receptor ionotropic kainate 3), chromosome 4 (KLHL2, Kelch-like protein 2), chromosome 17 (CRHR1, corticotropin-releasing hormone receptor 1 and MAPT, microtubule-associated protein Tau), and on chromosome 18 (CELF4, CUGBP elav-like family member 4). We found no evidence for genetic differences in the common allelic architecture of neuroticism by sex. By comparing our findings with those of the Psychiatric Genetics Consortia, we identified a large genetic correlation between neuroticism and MDD (0.64) and a smaller genetic correlation with schizophrenia (0.22) but not with bipolar disorder. Polygenic scores derived from the primary UK Biobank sample captured about 1% of the variance in trait liability to neuroticism. Overall, our findings confirm a polygenic basis for neuroticism and substantial shared genetic architecture between neuroticism and MDD. The identification of 9 new neuroticism-associated loci will drive forward future work on the neurobiology of neuroticism and related phenotypes.
]]></description>
<dc:creator>Daniel J Smith</dc:creator>
<dc:creator>Valentina Escott-Price</dc:creator>
<dc:creator>Gail Davies</dc:creator>
<dc:creator>Mark ES Bailey</dc:creator>
<dc:creator>Lucia Colodro Conde</dc:creator>
<dc:creator>Joey Ward</dc:creator>
<dc:creator>Alexey Vedernikov</dc:creator>
<dc:creator>Riccardo Marioni</dc:creator>
<dc:creator>Breda Cullen</dc:creator>
<dc:creator>Donald Lyall</dc:creator>
<dc:creator>Saskia P Hagenaars</dc:creator>
<dc:creator>David CM Liewald</dc:creator>
<dc:creator>Michelle Luciano</dc:creator>
<dc:creator>Catharine R Gale</dc:creator>
<dc:creator>Stuart J Ritchie</dc:creator>
<dc:creator>Caroline Hayward</dc:creator>
<dc:creator>Barbara Nicholl</dc:creator>
<dc:creator>Brendan Bulik-Sullivan</dc:creator>
<dc:creator>Mark Adams</dc:creator>
<dc:creator>Baptiste Couvy-Duchesne</dc:creator>
<dc:creator>Nicholas Graham</dc:creator>
<dc:creator>Daniel Mackay</dc:creator>
<dc:creator>Jonathan Evans</dc:creator>
<dc:creator>Blair H Smith</dc:creator>
<dc:creator>David J Porteous</dc:creator>
<dc:creator>Sarah Medland</dc:creator>
<dc:creator>Nick Martin</dc:creator>
<dc:creator>Peter Holmans</dc:creator>
<dc:creator>Andrew M McIntosh</dc:creator>
<dc:creator>Jill P Pell</dc:creator>
<dc:creator>Ian J Deary</dc:creator>
<dc:creator>Michael O'Donovan</dc:creator>
<dc:creator></dc:creator>
<dc:date>2015-11-20</dc:date>
<dc:identifier>doi:10.1101/032417</dc:identifier>
<dc:title><![CDATA[Genome-wide analysis of over 106,000 individuals identifies 9 neuroticism-associated loci]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2015-11-20</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/043000v1?rss=1">
<title>
<![CDATA[
Molecular genetic contributions to social deprivation and household income in UK Biobank (n = 112,151) 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/043000v1?rss=1"
</link>
<description><![CDATA[
Individuals with lower socio-economic status (SES) are at increased risk of physical and mental illnesses and tend to die at an earlier age [1-3]. Explanations for the association between SES and health typically focus on factors that are environmental in origin [4]. However, common single nucleotide polymorphisms (SNPs) have been found collectively to explain around 18% (SE = 5%) of the phenotypic variance of an area-based social deprivation measure of SES [5]. Molecular genetic studies have also shown that physical and psychiatric diseases are at least partly heritable [6]. It is possible, therefore, that phenotypic associations between SES and health arise partly due to a shared genetic etiology. We conducted a genome-wide association study (GWAS) on social deprivation and on household income using the 112,151 participants of UK Biobank. We find that common SNPs explain 21% (SE = 0.5%) of the variation in social deprivation and 11% (SE = 0.7%) in household income. Two independent SNPs attained genome-wide significance for household income, rs187848990 on chromosome 2, and rs8100891 on chromosome 19. Genes in the regions of these SNPs have been associated with intellectual disabilities, schizophrenia, and synaptic plasticity. Extensive genetic correlations were found between both measures of socioeconomic status and illnesses, anthropometric variables, psychiatric disorders, and cognitive ability. These findings show that some SNPs associated with SES are involved in the brain and central nervous system. The genetic associations with SES are probably mediated via other partly-heritable variables, including cognitive ability, education, personality, and health.
]]></description>
<dc:creator>William David Hill</dc:creator>
<dc:creator>Saskia P Hagenaars</dc:creator>
<dc:creator>Riccardo E Marioni</dc:creator>
<dc:creator>Sarah E Harris</dc:creator>
<dc:creator>David CM Liewald</dc:creator>
<dc:creator>Gail Davies</dc:creator>
<dc:creator>International Consortium for Blood Pressure</dc:creator>
<dc:creator>Andrew M McIntosh</dc:creator>
<dc:creator>Catharine R Gale</dc:creator>
<dc:creator>Ian J Deary</dc:creator>
<dc:creator></dc:creator>
<dc:date>2016-03-09</dc:date>
<dc:identifier>doi:10.1101/043000</dc:identifier>
<dc:title><![CDATA[Molecular genetic contributions to social deprivation and household income in UK Biobank (n = 112,151)]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2016-03-09</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/051771v1?rss=1">
<title>
<![CDATA[
Age differences in brain white matter microstructure in UK Biobank (N = 3,513) 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/051771v1?rss=1"
</link>
<description><![CDATA[
Quantifying the microstructural properties of the human brains connections is necessary for understanding normal ageing and disease states. We examined brain white matter MRI data in 3,513 generally healthy people aged 45-75 years from the UK Biobank sample. Using conventional water diffusion measures and newer, as-yet rarely-studied indices from neurite orientation dispersion and density imaging (NODDI), we document large age differences in white matter microstructure. Mean diffusivity was the most age-sensitive diffusion measure, with negative age associations strongest in the thalamic radiation and association fibres. Inter-individual differences in white matter microstructure across brain tracts become increasingly correlated in older age. This connectivity  de-differentiation may reflect an age-related aggregation of systemic detrimental effects on the brain. We report several other novel results, including comparative age associations with volumetric indices and associations with hemisphere and sex. Results from this unusually large, single-scanner sample provide one of the most definitive characterisations to date of age differences in major white matter tracts in the human brain.nnAbbreviations
]]></description>
<dc:creator>Simon R Cox</dc:creator>
<dc:creator>Stuart J Ritchie</dc:creator>
<dc:creator>Elliot M Tucker-Drob</dc:creator>
<dc:creator>David C Liewald</dc:creator>
<dc:creator>Saskia P Hagenaars</dc:creator>
<dc:creator>Gail Davies</dc:creator>
<dc:creator>Joanna M Wardlaw</dc:creator>
<dc:creator>Catharine R Gale</dc:creator>
<dc:creator>Mark E Bastin</dc:creator>
<dc:creator>Ian J Deary</dc:creator>
<dc:creator></dc:creator>
<dc:date>2016-05-04</dc:date>
<dc:identifier>doi:10.1101/051771</dc:identifier>
<dc:title><![CDATA[Age differences in brain white matter microstructure in UK Biobank (N = 3,513)]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2016-05-04</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/028001v1?rss=1">
<title>
<![CDATA[
Genetic evidence that lower circulating FSH levels lengthen menstrual cycle, increase age at menopause, and impact reproductive health: a UK Biobank study 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/028001v1?rss=1"
</link>
<description><![CDATA[
Study question: How does a genetic variant altering follicle stimulating hormone (FSH) levels, which we identified as associated with length of menstrual cycle, more widely impact reproductive health?nnSummary answer: The T allele of the FSHB promoter polymorphism (rs10835638) results in longer menstrual cycles and later menopause and, while having detrimental effects on fertility, is protective against endometriosis.nnWhat is known already: The FSHB promoter polymorphism (rs10835638) affects levels of FSHB transcription and, as a result, levels of FSH. FSH is required for normal fertility and genetic variants at the FSHB locus are associated with age at menopause and polycystic ovary syndrome (PCOS).nnStudy design, size, duration: We conducted a genetic association study using cross-sectional data from the UK Biobank.nnParticipants/materials, setting, methods: We included white British individuals aged 40-69 years in 2006-2010, included in the May 2015 release of genetic data from UK Biobank. We conducted a genome-wide association study (GWAS) in 9,534 individuals to identify genetic variants associated with length of menstrual cycle. We tested the FSH lowering T allele of the FSHB promoter polymorphism (rs10835638) for associations with 29 reproductive phenotypes in up to 63,350 individuals.nnMain results and the role of chance: In the GWAS for menstrual cycle length, only variants near the FSHB gene reached genome-wide significance (P<5x10-8). The FSH-lowering T allele of the FSHB promoter polymorphism (rs10835638G>T; MAF 0.16) was associated with longer menstrual cycles (0.16 s.d. (approx. 1 day) per minor allele; 95% CI 0.12-0.20; P=6x10-16), later age at menopause (0.13 years per minor allele; 95% CI 0.04-0.22; P=5.7x10-3), greater female nulliparity (OR=1.06; 95% CI 1.02-1.11; P=4.8x10-3) and lower risk of endometriosis (OR=0.79; 95% CI 0.69-0.90; P=4.1x10-4). The FSH-lowering T allele was not associated more generally with other reproductive illnesses or conditions and we did not replicate associations with male infertility or PCOS.nnLimitations, reasons for caution: The data included might be affected by recall bias. Women with a cycle length recorded were aged over 40 and were approaching menopause, however we did not find evidence that this affected the results. Many of the illnesses had relatively small sample sizes and so we may have been under-powered to detect an effect.nnWider implications of the findings: We found a strong novel association between a genetic variant that lowers FSH levels and longer menstrual cycles, at a locus previously robustly associated with age at menopause. The variant was also associated with nulliparity and endometriosis risk. We conclude that lifetime differences in circulating levels of FSH between individuals can influence menstrual cycle length and a range of reproductive outcomes, including menopause timing, infertility, endometriosis and PCOS.
]]></description>
<dc:creator>Katherine S Ruth</dc:creator>
<dc:creator>Robin N Beaumont</dc:creator>
<dc:creator>Jessica Tyrrell</dc:creator>
<dc:creator>Samuel E Jones</dc:creator>
<dc:creator>Marcus A Tuke</dc:creator>
<dc:creator>Hanieh Yaghootkar</dc:creator>
<dc:creator>Andrew R Wood</dc:creator>
<dc:creator>Rachel M Freathy</dc:creator>
<dc:creator>Michael N Weedon</dc:creator>
<dc:creator>Timothy M Frayling</dc:creator>
<dc:creator>Anna Murray</dc:creator>
<dc:creator></dc:creator>
<dc:date>2015-10-01</dc:date>
<dc:identifier>doi:10.1101/028001</dc:identifier>
<dc:title><![CDATA[Genetic evidence that lower circulating FSH levels lengthen menstrual cycle, increase age at menopause, and impact reproductive health: a UK Biobank study]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2015-10-01</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/008755v1?rss=1">
<title>
<![CDATA[
Mixed Model with Correction for Case-Control Ascertainment Increases Association Power 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/008755v1?rss=1"
</link>
<description><![CDATA[
We introduce a Liability Threshold Mixed Linear Model (LTMLM) association statistic for ascertained case-control studies that increases power vs. existing mixed model methods, with a well-controlled false-positive rate. Recent work has shown that existing mixed model methods suffer a loss in power under case-control ascertainment, but no solution has been proposed. Here, we solve this problem using a chi-square score statistic computed from posterior mean liabilities (PML) under the liability threshold model. Each individuals PML is conditional not only on that individuals case-control status, but also on every individuals case-control status and on the genetic relationship matrix obtained from the data. The PML are estimated using a multivariate Gibbs sampler, with the liability-scale phenotypic covariance matrix based on the genetic relationship matrix (GRM) and a heritability parameter estimated via Haseman-Elston regression on case-control phenotypes followed by transformation to liability scale. In simulations of unrelated individuals, the LTMLM statistic was correctly calibrated and achieved higher power than existing mixed model methods in all scenarios tested, with the magnitude of the improvement depending on sample size and severity of case-control ascertainment. In a WTCCC2 multiple sclerosis data set with >10,000 samples, LTMLM was correctly calibrated and attained a 4.1% improvement (P = 0.007) in chi-square statistics (vs. existing mixed model methods) at 75 known associated SNPs, consistent with simulations. Larger increases in power are expected at larger sample sizes. In conclusion, an increase in power over existing mixed model methods is available for ascertained case-control studies of diseases with low prevalence.
]]></description>
<dc:creator>tristan hayeck</dc:creator>
<dc:creator>Noah Zaitlen</dc:creator>
<dc:creator>Po-Ru Loh</dc:creator>
<dc:creator>Bjarni Vilhjalmsson</dc:creator>
<dc:creator>Samuela Pollack</dc:creator>
<dc:creator>Alexander Gusev</dc:creator>
<dc:creator>Jian Yang</dc:creator>
<dc:creator>Guo-Bo Chen</dc:creator>
<dc:creator>Michael E. Goddard</dc:creator>
<dc:creator>Peter M. Visscher</dc:creator>
<dc:creator>Nick Patterson</dc:creator>
<dc:creator>Alkes Price</dc:creator>
<dc:creator></dc:creator>
<dc:date>2014-09-04</dc:date>
<dc:identifier>doi:10.1101/008755</dc:identifier>
<dc:title><![CDATA[Mixed Model with Correction for Case-Control Ascertainment Increases Association Power]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2014-09-04</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/070912v1?rss=1">
<title>
<![CDATA[
Subcortical volume and white matter integrity abnormalities in major depressive disorder: findings from UK Biobank (N=4446) 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/070912v1?rss=1"
</link>
<description><![CDATA[
BackgroundPrevious reports of altered grey and white matter structure in Major Depressive Disorder (MDD) have been inconsistent. Recent meta-analyses have, however, reported reduced hippocampal grey matter volume in MDD and reduced white matter integrity in several brain regions. The use of different diagnostic criteria, different scanners and imaging sequences may, however, obscure further anatomical differences.nnMethodsIn this study, we tested for differences in subcortical grey matter volume and white matter integrity between depressed individuals and controls in a large sample of subjects from the first data release of the UK Biobank imaging study of 4446 individuals, which used consistent diagnostic criteria at a single assessment centre, with a single MRI scanner and protocol.nnResultsWhilst we found no significant differences in subcortical volumes, we report significant reductions in depressed individuals versus controls in global white matter integrity, as measured by fractional anisotropy (FA) ({beta} = -0.187, p = 0.017). We also report reductions in FA in association/commissural fibres ({beta} = -0.184, p = 0.019) and thalamic radiations ({beta} = -0.175, p = 0.027). Examining tracts individually, we report tract-specific FA reductions in the left superior longitudinal fasciculus ({beta} = -0.218, pcorrected = 0.012) and superior thalamic radiation ({beta} = -0.258, pcorrected = 0.010) in subjects with depression.nnConclusionsOur findings highlight the need for further large adequately-powered studies of depression and provide further evidence for disrupted white matter integrity in the disorder. Future studies would focus on exploring the typical neuro-phenotype in homogenous subgroups of depression.
]]></description>
<dc:creator>Xueyi Shen</dc:creator>
<dc:creator>Lianne Reus</dc:creator>
<dc:creator>Mark Adams</dc:creator>
<dc:creator>Simon Cox</dc:creator>
<dc:creator>Ian Deary</dc:creator>
<dc:creator>David Liewald</dc:creator>
<dc:creator>Mark Bastin</dc:creator>
<dc:creator>Daniel Smith</dc:creator>
<dc:creator>Heather Whalley</dc:creator>
<dc:creator>Andrew McIntosh</dc:creator>
<dc:creator></dc:creator>
<dc:date>2016-08-22</dc:date>
<dc:identifier>doi:10.1101/070912</dc:identifier>
<dc:title><![CDATA[Subcortical volume and white matter integrity abnormalities in major depressive disorder: findings from UK Biobank (N=4446)]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2016-08-22</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/055855v1?rss=1">
<title>
<![CDATA[
Population structure of UK Biobank and ancient Eurasians reveals adaptation at genes influencing blood pressure 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/055855v1?rss=1"
</link>
<description><![CDATA[
Analyzing genetic differences between closely related populations can be a powerful way to detect recent adaptation. The very large sample size of the UK Biobank is ideal for detecting selection using population differentiation, and enables an analysis of UK population structure at fine resolution. In analyses of 113,851 UK Biobank samples, population structure in the UK is dominated by 5 principal components (PCs) spanning 6 clusters: Northern Ireland, Scotland, northern England, southern England, and two Welsh clusters. Analyses with ancient Eurasians show that populations in the northern UK have higher levels of Steppe ancestry, and that UK population structure cannot be explained as a simple mixture of Celts and Saxons. A scan for unusual population differentiation along top PCs identified a genome-wide significant signal of selection at the coding variant rs601338 in FUT2 (p = 9.16 x 10-9). In addition, by combining evidence of unusual differentiation within the UK with evidence from ancient Eurasians, we identified new genome-wide significant (p < 5 x 10-8) signals of recent selection at two additional loci: CYP1A2/CSK and F12. We detected strong associations to diastolic blood pressure in the UK Biobank for the variants with new selection signals at CYP1A2/CSK (p = 1.10 x 10-19)) and for variants with ancient Eurasian selection signals in the ATXN2/SH2B3 locus (p = 8.00 x 10-33), implicating recent adaptation related to blood pressure.
]]></description>
<dc:creator>Kevin Galinsky</dc:creator>
<dc:creator>Po-Ru Loh</dc:creator>
<dc:creator>Swapan Mallick</dc:creator>
<dc:creator>Nick J Patterson</dc:creator>
<dc:creator>Alkes L Price</dc:creator>
<dc:creator></dc:creator>
<dc:date>2016-05-27</dc:date>
<dc:identifier>doi:10.1101/055855</dc:identifier>
<dc:title><![CDATA[Population structure of UK Biobank and ancient Eurasians reveals adaptation at genes influencing blood pressure]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2016-05-27</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/028282v1?rss=1">
<title>
<![CDATA[
Fast and accurate long-range phasing in a UK Biobank cohort 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/028282v1?rss=1"
</link>
<description><![CDATA[
Recent work has leveraged the extensive genotyping of the Icelandic population to perform long-range phasing (LRP), enabling accurate imputation and association analysis of rare variants in target samples typed on genotyping arrays. Here, we develop a fast and accurate LRP method, Eagle, that extends this paradigm to populations with much smaller proportions of genotyped samples by harnessing long (>4cM) identical-by-descent (IBD) tracts shared among distantly related individuals. We applied Eagle to N=150K samples (0.2% of the British population) from the UK Biobank, and we determined that it is 1-2 orders of magnitude faster than existing methods while achieving similar or better phasing accuracy (switch error rate {approx}0.3%, corresponding to perfect phase in most 10Mb segments). We also observed that when used within an imputation pipeline, Eagle pre-phasing improved downstream imputation accuracy compared to pre-phasing in batches using existing methods (as necessary to achieve comparable computational cost).
]]></description>
<dc:creator>Po-Ru Loh</dc:creator>
<dc:creator>Pier Francesco Palamara</dc:creator>
<dc:creator>Alkes L Price</dc:creator>
<dc:creator></dc:creator>
<dc:date>2015-10-04</dc:date>
<dc:identifier>doi:10.1101/028282</dc:identifier>
<dc:title><![CDATA[Fast and accurate long-range phasing in a UK Biobank cohort]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2015-10-04</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/054973v1?rss=1">
<title>
<![CDATA[
Dissection of Major Depressive Disorder using polygenic risk scores for Schizophrenia in two independent cohort. 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/054973v1?rss=1"
</link>
<description><![CDATA[
Major depressive disorder (MDD) is known for its substantial clinical and suspected causal heterogeneity. It is characterised by low mood, psychomotor slowing, and increased levels of the personality trait neuroticism; factors which are also associated with schizophrenia (SCZ). It is possible that some cases of MDD may have a substantial genetic loading for SCZ. A sign of the presence of SCZ-like MDD sub-groups would be indicated by an interaction between MDD status and polygenic risk of SCZ on cognitive, personality and mood measures. In the current study, we hypothesised that higher SCZ-polygenic risk would define larger MDD case-control differences in cognitive ability, and smaller differences in distress and neuroticism. Polygenic risk scores (PGRS) for SCZ and their association with cognitive variables, neuroticism, mood, and psychological distress were estimated in a large population-based cohort (Generation Scotland: Scottish Family Health Study, GS:SFHS). Individuals were divided into those with, and without, depression (n=2587 & n=16,764 respectively) to test whether there was an interaction between MDD status and schizophrenia risk. Replication was sought in UK Biobank (n=33,525). In both GS:SFHS and UK Biobank we found significant interactions between SCZ-PGRS and MDD status for measures of psychological distress and neuroticism. In both cohorts there was a reduction of case-control differences on a background of higher genetic risk of SCZ. These findings suggest that depression on a background of high genetic risk for SCZ may show attenuated associations with distress and neuroticism. This may represent a causally distinct form of MDD more closely related to SCZ.
]]></description>
<dc:creator>Heather C Whalley</dc:creator>
<dc:creator>Mark J Adams</dc:creator>
<dc:creator>Lynsey Hall</dc:creator>
<dc:creator>Toni-Kim Clarke</dc:creator>
<dc:creator>Ana Maria Fernandez-Pujals</dc:creator>
<dc:creator>Jude Gibson</dc:creator>
<dc:creator>Ella Wigmore</dc:creator>
<dc:creator>Jonathan Hafferty</dc:creator>
<dc:creator>Saskia P Hagenaars</dc:creator>
<dc:creator>Gail Davies</dc:creator>
<dc:creator>Archie Campbell</dc:creator>
<dc:creator>Caroline Hayward</dc:creator>
<dc:creator>Stephen M Lawrie</dc:creator>
<dc:creator>David Porteous</dc:creator>
<dc:creator>Ian Deary</dc:creator>
<dc:creator>Andrew M McIntosh</dc:creator>
<dc:creator></dc:creator>
<dc:date>2016-05-24</dc:date>
<dc:identifier>doi:10.1101/054973</dc:identifier>
<dc:title><![CDATA[Dissection of Major Depressive Disorder using polygenic risk scores for Schizophrenia in two independent cohort.]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2016-05-24</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/037457v1?rss=1">
<title>
<![CDATA[
Genetic and environmental risk for chronic pain and the contribution of risk variants for psychiatric disorders. Results from Generation Scotland: Scottish Family Health Study and UK Biobank 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/037457v1?rss=1"
</link>
<description><![CDATA[
BackgroundChronic pain is highly prevalent worldwide and a significant source of disability, yet its genetic and environmental risk factors are poorly understood. Its relationship with psychiatric illness, and major depressive disorder (MDD) in particular, is of particular importance. We sought to test the contribution of genetic factors and shared and unique environment to risk of chronic pain and its correlation with MDD in Generation Scotland: Scottish Family Health Study (GS:SFHS). We then sought to replicate any significant findings in the UK Biobank study.nnMethodsUsing family-based mixed-model analyses, we examined the contribution of genetics and environment to chronic pain using spouse, sibling and household groups as measures of shared environment. We then examined the correlation between chronic pain and MDD and estimated the contribution of genetic factors and shared environment. Finally, we used data from two independent genome-wide association studies to test whether chronic pain has a polygenic risk architecture and examine whether genomic risk of psychiatric disorder predicted chronic pain and whether genomic risk of chronic pain predicted MDD.nnResultsChronic pain is a moderately heritable trait (narrow sense heritability = 38.4%) which is more likely to be concordant in spouses and partners (variance explained 18.7%). Chronic pain is positively correlated with depression (rho = 0.13, p = 2.72x10-68) and it shows a tendency to cluster within families for genetic reasons (genetic correlation rho = 0.51, p = 8.24x10-19). Polygenic risk profiles for pain, generated using independent GWAS data, predicted chronic pain in both GS:SFHS (maximum {beta} = 6.18x10-2, p = 4.3x10-4) and UK Biobank (maximum {beta} = 5.68 x 10-2, p < 3x10-4). Genomic risk of MDD is also significantly associated with chronic pain in both GS:SFHS (maximum {beta} = 6.62x10-2, p = 4.3x10-4) and UK Biobank (maximum {beta} = 2.56x10-2, p < 3x10-4).nnConclusionsGenetic factors and chronic pain in a partner or spouse contribute substantially to the risk of chronic pain in the general population. Chronic pain is genetically correlated with MDD, has a polygenic architecture and is predicted by polygenic risk of MDD.
]]></description>
<dc:creator>Andrew M McIntosh</dc:creator>
<dc:creator>Lynsey S Hall</dc:creator>
<dc:creator>Yanni Zeng</dc:creator>
<dc:creator>Mark James Adams</dc:creator>
<dc:creator>Jude Gibson</dc:creator>
<dc:creator>Ella Wigmore</dc:creator>
<dc:creator>Maria Pujils-Fernandez</dc:creator>
<dc:creator>Archie I Campbell</dc:creator>
<dc:creator>Toni-Kim Clarke</dc:creator>
<dc:creator>Caroline Hayward</dc:creator>
<dc:creator>Christopher Haley</dc:creator>
<dc:creator>David Porteous</dc:creator>
<dc:creator>Ian Deary</dc:creator>
<dc:creator>Daniel Smith</dc:creator>
<dc:creator>Barbara Nicholl</dc:creator>
<dc:creator>David A Hinds</dc:creator>
<dc:creator>Amy V Jones</dc:creator>
<dc:creator>Serena Scollen</dc:creator>
<dc:creator>Weihua Meng</dc:creator>
<dc:creator>Blair Smith</dc:creator>
<dc:creator>Lynne J Hocking</dc:creator>
<dc:creator></dc:creator>
<dc:date>2016-01-21</dc:date>
<dc:identifier>doi:10.1101/037457</dc:identifier>
<dc:title><![CDATA[Genetic and environmental risk for chronic pain and the contribution of risk variants for psychiatric disorders. Results from Generation Scotland: Scottish Family Health Study and UK Biobank]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2016-01-21</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/038430v1?rss=1">
<title>
<![CDATA[
HUMAN LONGEVITY IS INFLUENCED BY MANY GENETIC VARIANTS: EVIDENCE FROM 75,000 UK BIOBANK PARTICIPANTS 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/038430v1?rss=1"
</link>
<description><![CDATA[
Variation in human lifespan is 20 to 30% heritable but few genetic variants have been identified. We undertook a Genome Wide Association Study (GWAS) using age at death of parents of middle-aged UK Biobank participants of European decent (n=75,244 with fathers and/or mothers data). Genetic risk scores for 19 phenotypes (n=777 proven variants) were also tested.nnGenotyped variants (n=845,997) explained 10.2% (SD=1.3%) of combined parental longevity. In GWAS, a locus in the nicotine receptor CHRNA3 - previously associated with increased smoking and lung cancer - was associated with paternal age at death, with each protective allele (rs1051730[G]) being associated with 0.03 years later age at fathers death (p=3x10-8). Offspring of longer lived parents had more protective alleles (lower genetic risk scores) for coronary artery disease, systolic blood pressure, body mass index, cholesterol and triglyceride levels, type-1 diabetes, inflammatory bowel disease and Alzheimers disease. In candidate gene analyses, variants in the TOMM40/APOE locus were associated with longevity (including rs429358, p=3x10-5), but FOXO variants were not associated.nnThese results support a multiple protective factors model for achieving longer lifespans in humans, with a prominent role for cardiovascular-related pathways. Several of these genetically influenced risks, including blood pressure and tobacco exposure, are potentially modifiable.
]]></description>
<dc:creator>Luke C Pilling</dc:creator>
<dc:creator>Janice L Atkins</dc:creator>
<dc:creator>Kirsty Bowman</dc:creator>
<dc:creator>Samuel E Jones</dc:creator>
<dc:creator>Jessica Tyrrell</dc:creator>
<dc:creator>Robin N Beaumont</dc:creator>
<dc:creator>Katherine S Ruth</dc:creator>
<dc:creator>Marcus A Tuke</dc:creator>
<dc:creator>Hanieh Yaghootkar</dc:creator>
<dc:creator>Andrew R Wood</dc:creator>
<dc:creator>Rachel M Freathy</dc:creator>
<dc:creator>Anna Murray</dc:creator>
<dc:creator>Michael N Weedon</dc:creator>
<dc:creator>Luting Xue</dc:creator>
<dc:creator>Kathryn Lunetta</dc:creator>
<dc:creator>Joanne M Murabito</dc:creator>
<dc:creator>Lorna W Harries</dc:creator>
<dc:creator>Jean-Marie Robine</dc:creator>
<dc:creator>Carol Brayne</dc:creator>
<dc:creator>George A Kuchel</dc:creator>
<dc:creator>Luigi Ferrucci</dc:creator>
<dc:creator>Timothy M Frayling</dc:creator>
<dc:creator>David Melzer</dc:creator>
<dc:creator></dc:creator>
<dc:date>2016-02-01</dc:date>
<dc:identifier>doi:10.1101/038430</dc:identifier>
<dc:title><![CDATA[HUMAN LONGEVITY IS INFLUENCED BY MANY GENETIC VARIANTS: EVIDENCE FROM 75,000 UK BIOBANK PARTICIPANTS]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2016-02-01</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/031369v1?rss=1">
<title>
<![CDATA[
Genome-wide association analyses in &amp;gt;119,000 individuals identifies thirteen morningness and two sleep duration loci 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/031369v1?rss=1"
</link>
<description><![CDATA[
Disrupted circadian rhythms and reduced sleep duration are associated with several human diseases, particularly obesity and type 2 diabetes, but little is known about the genetic factors influencing these heritable traits. We performed genome-wide association studies of self-reported chronotype (morning/evening person) and self-reported sleep duration in 128,266 White British individuals from the UK Biobank study. Sixteen variants were associated with chronotype (P<5x10-8), including variants near the known circadian rhythm genes RGS16 (1.21 odds of morningness [95%CI 1.15, 1.27], P=3x10-12) and PER2 (1.09 odds of morningness [95%CI 1.06, 1.12], P=4x10-10). The PER2 signal has previously been associated with iris function. We sought replication using self-reported data from 89,823 23andMe participants; thirteen of the chronotype signals remained significant at P<5x10-8 on meta-analysis and eleven of these reached P<0.05 in the same direction in the 23andMe study. For sleep duration, we replicated one known signal in PAX8 (2.6 [95%CIs 1.9, 3.2] minutes per allele P=5.7x10-16) and identified and replicated two novel associations at VRK2 (2.0 [95% CI: 1.3, 2.7] minutes per allele, P=1.2x10-9; and 1.6 [95% CI: 1.1, 2.2] minutes per allele, P=7.6x10-9). Although we found genetic correlation between chronotype and BMI (rG=0.056, P=0.048); undersleeping and BMI (rG=0.147, P=1x10-5) and oversleeping and BMI (rG=0.097, P=0.039), Mendelian Randomisation analyses provided no consistent evidence of causal associations between BMI or type 2 diabetes and chronotype or sleep duration. Our study provides new insights into the biology of sleep and circadian rhythms in humans.
]]></description>
<dc:creator>Samuel E Jones</dc:creator>
<dc:creator>Jessica Tyrrell</dc:creator>
<dc:creator>Andrew R Wood</dc:creator>
<dc:creator>Robin N Beaumont</dc:creator>
<dc:creator>Katherine S Ruth</dc:creator>
<dc:creator>Marcus A Tuke</dc:creator>
<dc:creator>Hanieh Yaghootkar</dc:creator>
<dc:creator>Youna Hu</dc:creator>
<dc:creator>Maris Teder-Laving</dc:creator>
<dc:creator>Caroline Hayward</dc:creator>
<dc:creator>Till Roenneberg</dc:creator>
<dc:creator>James F Wilson</dc:creator>
<dc:creator>Fabiola Del Greco</dc:creator>
<dc:creator>Andrew A Hicks</dc:creator>
<dc:creator>Chol Shin</dc:creator>
<dc:creator>Chang-Ho Yun</dc:creator>
<dc:creator>Seung Ku Lee</dc:creator>
<dc:creator>Andres Metspalu</dc:creator>
<dc:creator>Enda M Byrne</dc:creator>
<dc:creator>Philip R Gehrman</dc:creator>
<dc:creator>Henning Tiemeier</dc:creator>
<dc:creator>Karla V Allebrandt</dc:creator>
<dc:creator>Rachel M Freathy</dc:creator>
<dc:creator>Anna Murray</dc:creator>
<dc:creator>David A Hinds</dc:creator>
<dc:creator>Timothy M Frayling</dc:creator>
<dc:creator>Michael N Weedon</dc:creator>
<dc:creator></dc:creator>
<dc:date>2016-02-02</dc:date>
<dc:identifier>doi:10.1101/031369</dc:identifier>
<dc:title><![CDATA[Genome-wide association analyses in &amp;gt;119,000 individuals identifies thirteen morningness and two sleep duration loci]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2016-02-02</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/047290v1?rss=1">
<title>
<![CDATA[
Genetic contributions to self-reported tiredness 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/047290v1?rss=1"
</link>
<description><![CDATA[
Self-reported tiredness and low energy, often called fatigue, is associated with poorer physical and mental health. Twin studies have indicated that this has a heritability between 6% and 50%. In the UK Biobank sample (N = 108 976) we carried out a genome-wide association study of responses to the question, "Over the last two weeks, how often have you felt tired or had little energy?" Univariate GCTA-GREML found that the proportion of variance explained by all common SNPs for this tiredness question was 8.4% (SE = 0.6%). GWAS identified one genome-wide significant hit (Affymetrix id 1:64178756_C_T; p = 1.36 x 10-11). LD score regression and polygenic profile analysis were used to test for pleiotropy between tiredness and up to 28 physical and mental health traits from GWAS consortia. Significant genetic correlations were identified between tiredness and BMI, HDL cholesterol, forced expiratory volume, grip strength, HbA1c, longevity, obesity, self-rated health, smoking status, triglycerides, type 2 diabetes, waist-hip ratio, ADHD, bipolar disorder, major depressive disorder, neuroticism, schizophrenia, and verbal-numerical reasoning (absolute rg effect sizes between 0.11 and 0.78). Significant associations were identified between tiredness phenotypic scores and polygenic profile scores for BMI, HDL cholesterol, LDL cholesterol, coronary artery disease, HbA1c, height, obesity, smoking status, triglycerides, type 2 diabetes, and waist-hip ratio, childhood cognitive ability, neuroticism, bipolar disorder, major depressive disorder, and schizophrenia (standardised {beta}s between -0.016 and 0.03). These results suggest that tiredness is a partly-heritable, heterogeneous and complex phenomenon that is phenotypically and genetically associated with affective, cognitive, personality, and physiological processes.nn"Hech, sirs! But Im wabbit, Im back frae the toon;nnI haena dune pechin--jist let me sit doon.nnFrom GlescannBy William Dixon Cocker (1882-1970)
]]></description>
<dc:creator>Vincent Deary</dc:creator>
<dc:creator>Saskia P Hagenaars</dc:creator>
<dc:creator>Sarah E Harris</dc:creator>
<dc:creator>W David Hill</dc:creator>
<dc:creator>Gail Davies</dc:creator>
<dc:creator>David CM Liewald</dc:creator>
<dc:creator>International Consortium for Blood Pressure GWAS</dc:creator>
<dc:creator>CHARGE consortium Aging and Longevity Group</dc:creator>
<dc:creator>Andrew M McIntosh</dc:creator>
<dc:creator>Catharine R Gale</dc:creator>
<dc:creator>Ian J Deary</dc:creator>
<dc:creator></dc:creator>
<dc:date>2016-04-05</dc:date>
<dc:identifier>doi:10.1101/047290</dc:identifier>
<dc:title><![CDATA[Genetic contributions to self-reported tiredness]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2016-04-05</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/045831v1?rss=1">
<title>
<![CDATA[
Case-control association mapping without cases 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/045831v1?rss=1"
</link>
<description><![CDATA[
The case-control association study is a powerful method for identifying genetic variants that influence disease risk. However, the collection of cases can be time-consuming and expensive; if a disease occurs late in life or is rapidly lethal, it may be more practical to identify family members of cases. Here, we show that replacing cases with their first-degree relatives enables genome-wide association studies by proxy (GWAX). In randomly-ascertained cohorts, this approach enables previously infeasible studies of diseases that are absent (or nearly absent) in the cohort. As an illustration, we performed GWAX of 12 common diseases in 116,196 individuals from the UK Biobank. By combining these results with published GWAS summary statistics in a meta-analysis, we replicated established risk loci and identified 17 newly associated risk loci: four in Alzheimers disease, eight in coronary artery disease, and five in type 2 diabetes. In addition to informing disease biology, our results demonstrate the utility of association mapping using family history of disease as a phenotype to be mapped. We anticipate that this approach will prove useful in future genetic studies of complex traits in large population cohorts.
]]></description>
<dc:creator>Jimmy Z Liu</dc:creator>
<dc:creator>Yaniv Erlich</dc:creator>
<dc:creator>Joseph K Pickrell</dc:creator>
<dc:creator></dc:creator>
<dc:date>2016-03-25</dc:date>
<dc:identifier>doi:10.1101/045831</dc:identifier>
<dc:title><![CDATA[Case-control association mapping without cases]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2016-03-25</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/029504v1?rss=1">
<title>
<![CDATA[
Molecular genetic contributions to self-rated health 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/029504v1?rss=1"
</link>
<description><![CDATA[
BackgroundPoorer self-rated health (SRH) predicts worse health outcomes, even when adjusted for objective measures of disease at time of rating. Twin studies indicate SRH has a heritability of up to 60% and that its genetic architecture may overlap with that of personality and cognition.nnMethodsWe carried out a genome-wide association study (GWAS) of SRH on 111 749 members of the UK Biobank sample. Univariate genome-wide complex trait analysis (GCTA)-GREML analyses were used to estimate the proportion of variance explained by all common autosomal SNPs for SRH. Linkage Disequilibrium (LD) score regression and polygenic risk scoring, two complementary methods, were used to investigate pleiotropy between SRH in UK Biobank and up to 21 health-related and personality and cognitive traits from published GWAS consortia.nnResultsThe GWAS identified 13 independent signals associated with SRH, including several in regions previously associated with diseases or disease-related traits. The strongest signal was on chromosome 2 (rs2360675, p = 1.77x10-10) close to KLF7, which has previously been associated with obesity and type 2 diabetes. A second strong peak was identified on chromosome 6 in the major histocompatibility region (rs76380179, p = 6.15x10-10). The proportion of variance in SRH that was explained by all common genetic variants was 13%. Polygenic scores for the following traits and disorders were associated with SRH: cognitive ability, education, neuroticism, BMI, longevity, ADHD, major depressive disorder, schizophrenia, lung function, blood pressure, coronary artery disease, large vessel disease stroke, and type 2 diabetes.nnConclusionsIndividual differences in how people respond to a single item on SRH are partly explained by their genetic propensity to many common psychiatric and physical disorders and psychological traits.nnKey MessagesO_LIGenetic variants associated with common diseases and psychological traits are associated with self-rated health.nC_LIO_LIThe SNP-based heritability of self-rated health is 0.13 (SE 0.006).nC_LIO_LIThere is pleiotropy between self-rated health and psychiatric and physical diseases and psychological traits.nC_LI
]]></description>
<dc:creator>Sarah E Harris</dc:creator>
<dc:creator>Saskia P Hagenaars</dc:creator>
<dc:creator>Gail Davies</dc:creator>
<dc:creator>William David Hill</dc:creator>
<dc:creator>David CM Liewald</dc:creator>
<dc:creator>Stuart J Ritchie</dc:creator>
<dc:creator>Riccardo E Marioni</dc:creator>
<dc:creator>METASTROKE consortium</dc:creator>
<dc:creator>International Consortium for Blood Pressure</dc:creator>
<dc:creator>CHARGE consortium Aging and Longevity Group</dc:creator>
<dc:creator>CHARGE consortium Cognitive Group</dc:creator>
<dc:creator>Cathie LM Sudlow</dc:creator>
<dc:creator>Joanna M Wardlaw</dc:creator>
<dc:creator>Andrew M McIntosh</dc:creator>
<dc:creator>Catharine R Gale</dc:creator>
<dc:creator>Ian J Deary</dc:creator>
<dc:creator></dc:creator>
<dc:date>2015-10-20</dc:date>
<dc:identifier>doi:10.1101/029504</dc:identifier>
<dc:title><![CDATA[Molecular genetic contributions to self-rated health]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2015-10-20</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/218388v1?rss=1">
<title>
<![CDATA[
Genome-wide polygenic score to identify a monogenic risk-equivalent for coronary disease 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/218388v1?rss=1"
</link>
<description><![CDATA[
Identification of individuals at increased genetic risk for a complex disorder such as coronary disease can facilitate treatments or enhanced screening strategies. A rare monogenic mutation associated with increased cholesterol is present in ~1:250 carriers and confers an up to 4-fold increase in coronary risk when compared with non-carriers. Although individual common polymorphisms have modest predictive capacity, their cumulative impact can be aggregated into a polygenic score. Here, we develop a new, genome-wide polygenic score that aggregates information from 6.6 million common polymorphisms and show that this score can similarly identify individuals with a 4-fold increased risk for coronary disease. In >400,000 participants from UK Biobank, the score conforms to a normal distribution and those in the top 2.5% of the distribution are at 4-fold increased risk compared to the remaining 97.5%. Similar patterns are observed with genome-wide polygenic scores for two additional diseases - breast cancer and severe obesity.nnOne Sentence SummaryA genome-wide polygenic score identifies 2.5% of the population born with a 4-fold increased risk for coronary artery disease.
]]></description>
<dc:creator>Khera, A. V.</dc:creator>
<dc:creator>Chaffin, M.</dc:creator>
<dc:creator>Aragam, K.</dc:creator>
<dc:creator>Emdin, C. A.</dc:creator>
<dc:creator>Klarin, D.</dc:creator>
<dc:creator>Haas, M.</dc:creator>
<dc:creator>Roselli, C.</dc:creator>
<dc:creator>Natarajan, P.</dc:creator>
<dc:creator>Kathiresan, S.</dc:creator>
<dc:date>2017-11-15</dc:date>
<dc:identifier>doi:10.1101/218388</dc:identifier>
<dc:title><![CDATA[Genome-wide polygenic score to identify a monogenic risk-equivalent for coronary disease]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-11-15</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/219345v1?rss=1">
<title>
<![CDATA[
Genetic risk for neurodegenerative disorders, and its overlap with cognitive ability and physical function 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/219345v1?rss=1"
</link>
<description><![CDATA[
INTRODUCTIONIt is unclear whether polygenic risk for neurodegenerative disease is associated with cognitive performance and physical health.nnMETHODSThis study tested whether polygenic scores for Alzheimers disease (AD), Amyotrophic Lateral Sclerosis (ALS), or frontotemporal dementia (FTD) are associated with cognitive performance and physical health. Group-based analyses were performed to compare associations with cognitive and physical function outcomes in the top and bottom 10% for the three neurodegenerative polygenic risk scores.nnRESULTSHigher polygenic risk scores for AD, ALS, and FTD were associated with lower cognitive performance. Higher polygenic risk scores for FTD was also associated with increased forced expiratory volume in 1s and peak expiratory flow. A significant group difference was observed on the symbol digit substitution task between individuals with high polygenic risk for FTD and high polygenic risk for ALS.nnDISCUSSIONOur results suggest overlap between polygenic risk for neurodegenerative disorders, cognitive function and physical health.
]]></description>
<dc:creator>Hagenaars, S.</dc:creator>
<dc:creator>Radakovic, R.</dc:creator>
<dc:creator>Crockford, C.</dc:creator>
<dc:creator>Fawns-Ritchie, C.</dc:creator>
<dc:creator>International FTD-Genomics Consortium,</dc:creator>
<dc:creator>Harris, S. E.</dc:creator>
<dc:creator>Gale, C. R.</dc:creator>
<dc:creator>Deary, I. J.</dc:creator>
<dc:date>2017-11-14</dc:date>
<dc:identifier>doi:10.1101/219345</dc:identifier>
<dc:title><![CDATA[Genetic risk for neurodegenerative disorders, and its overlap with cognitive ability and physical function]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-11-14</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/176834v1?rss=1">
<title>
<![CDATA[
An atlas of genetic associations in UK Biobank 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/176834v1?rss=1"
</link>
<description><![CDATA[
Genome-wide association studies have revealed many loci contributing to the variation of complex traits, yet the majority of loci that contribute to the heritability of complex traits remain elusive. Large study populations with sufficient statistical power are required to detect the small effect sizes of the yet unidentified genetic variants. However, the analysis of huge cohorts, like UK Biobank, is complicated by incidental structure present when collecting such large cohorts. For instance, UK Biobank comprises 107,162 third degree or closer related participants. Traditionally, GWAS have removed related individuals because they comprised an insignificant proportion of the overall sample size, however, removing related individuals in UK Biobank would entail a substantial loss of power. Furthermore, modelling such structure using linear mixed models is computationally expensive, which requires a computational infrastructure that may not be accessible to all researchers. Here we present an atlas of genetic associations for 118 non-binary and 599 binary traits of 408,455 related and unrelated UK Biobank participants of White-British descent. Results are compiled in a publicly accessible database that allows querying genome-wide association summary results for 623,944 genotyped and HapMap2 imputed SNPs, as well downloading whole GWAS summary statistics for over 30 million imputed SNPs from the Haplotype Reference Consortium panel. Our atlas of associations (GeneATLAS, http://geneatlas.roslin.ed.ac.uk) will help researchers to query UK Biobank results in an easy way without the need to incur in high computational costs.
]]></description>
<dc:creator>Canela-Xandri, O.</dc:creator>
<dc:creator>Rawlik, K.</dc:creator>
<dc:creator>Tenesa, A.</dc:creator>
<dc:date>2017-08-16</dc:date>
<dc:identifier>doi:10.1101/176834</dc:identifier>
<dc:title><![CDATA[An atlas of genetic associations in UK Biobank]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-08-16</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/107037v1?rss=1">
<title>
<![CDATA[
Cigarette smoking increases coffee consumption: findings from a Mendelian randomisation analysis 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/107037v1?rss=1"
</link>
<description><![CDATA[
BackgroundSmokers tend to consume more coffee than non-smokers and there is evidence for a positive relationship between cigarette and coffee consumption in smokers. Cigarette smoke increases the metabolism of caffeine, so this association may represent a causal effect of smoking on caffeine intake.nnMethodsWe performed a Mendelian randomisation analysis in 114,029 individuals from the UK Biobank, 56,664 from the Norwegian HUNT study and 78,650 from the Copenhagen General Population Study. We used a genetic variant in the CHRNA5 nicotinic receptor (rs16969968) as a proxy for smoking heaviness. Coffee and tea consumption were self-reported. Analyses were conducted using linear regression and meta-analysed across studies.nnResultsEach additional cigarette per day consumed by current smokers was associated with higher coffee consumption (0.10 cups per day, 95% CI:0.03,0.17). There was weak evidence for an increase in tea consumption per additional cigarette smoked per day (0.04 cups per day, 95% CI:-0.002,0.07). There was strong evidence that each additional copy of the minor allele of rs16969968 (which increases daily cigarette consumption) in current smokers was associated with higher coffee consumption (0.15 cups per day, 95% CI:0.11,0.20), but only weak evidence for an association with tea consumption (0.04 cups per day, 95% CI:- 0.01,0.09). There was no clear evidence that rs16969968 was associated with coffee or tea consumption in never or former smokers.nnConclusionThese findings suggest that higher cigarette consumption causally increases coffee intake. This is consistent with faster metabolism of caffeine by smokers, but may also reflect behavioural links between smoking and coffee.
]]></description>
<dc:creator>Bjorngaard, J.</dc:creator>
<dc:creator>Nordestgaard, A.</dc:creator>
<dc:creator>Taylor, A.</dc:creator>
<dc:creator>Treur, J.</dc:creator>
<dc:creator>Gabrielsen, M.</dc:creator>
<dc:creator>Munafo, M. R.</dc:creator>
<dc:creator>Nordestgaard, B.</dc:creator>
<dc:creator>Asvold, B.</dc:creator>
<dc:creator>Romundstad, P.</dc:creator>
<dc:creator>Davey Smith, G.</dc:creator>
<dc:date>2017-02-08</dc:date>
<dc:identifier>doi:10.1101/107037</dc:identifier>
<dc:title><![CDATA[Cigarette smoking increases coffee consumption: findings from a Mendelian randomisation analysis]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-02-08</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/230524v1?rss=1">
<title>
<![CDATA[
Is there association between APOE e4 genotype and structural brain ageing phenotypes, and does that association increase in older age in UK Biobank? (N = 8,395) 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/230524v1?rss=1"
</link>
<description><![CDATA[
Apolipoprotein (APOE) e4 genotype is a purported risk factor for accelerated cognitive ageing and dementia, though its neurostructural substrates are unclear. The deleterious effects of this genotype on brain structure may increase in magnitude into older age. This study aimed to investigate in UK Biobank the association between APOE e4 allele presence vs. absence and brain imaging variables that have been associated with worse cognitive abilities; and whether this association varies by cross-sectional age. We used brain magnetic resonance imaging (MRI) and genetic data from a general-population cohort: the UK Biobank (N=8,395). We adjusted for the covariates of age in years, sex, Townsend social deprivation scores, smoking history and cardiometabolic diseases. There was a statistically significant association between APOE e4 genotype and increased (i.e. worse) white matter (WM) hyperintensity volumes (standardised beta = 0.088, 95 confidence intervals = 0.036 to 0.139, P = 0.001), a marker of poorer cerebrovascular health. There were no associations with left or right hippocampal, total grey matter (GM) or WM volumes, or WM tract integrity indexed by fractional anisotropy (FA) and mean diffusivity (MD). There were no statistically significant interactions with age. Future research in UK Biobank utilising intermediate phenotypes and longitudinal imaging hold significant promise for this area, particularly pertaining to APOE e4s potential link with cerebrovascular contributions to cognitive ageing.
]]></description>
<dc:creator>Lyall, D. M.</dc:creator>
<dc:creator>Cox, S. R.</dc:creator>
<dc:creator>Lyall, L. M.</dc:creator>
<dc:creator>Celis-Morales, C.</dc:creator>
<dc:creator>Cullen, B.</dc:creator>
<dc:creator>Mackay, D. F.</dc:creator>
<dc:creator>Ward, J.</dc:creator>
<dc:creator>Strawbridge, R. J.</dc:creator>
<dc:creator>McIntosh, A. M.</dc:creator>
<dc:creator>Sattar, N.</dc:creator>
<dc:creator>Smith, D. J.</dc:creator>
<dc:creator>Cavanagh, J.</dc:creator>
<dc:creator>Deary, I. J.</dc:creator>
<dc:creator>Pell, J. P.</dc:creator>
<dc:date>2017-12-08</dc:date>
<dc:identifier>doi:10.1101/230524</dc:identifier>
<dc:title><![CDATA[Is there association between APOE e4 genotype and structural brain ageing phenotypes, and does that association increase in older age in UK Biobank? (N = 8,395)]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-12-08</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/103119v1?rss=1">
<title>
<![CDATA[
Genetic contributions to trail making test performance in UK Biobank 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/103119v1?rss=1"
</link>
<description><![CDATA[
The Trail Making Test is a widely used test of executive function and has been thought to be strongly associated with general cognitive function. We examined the genetic architecture of the trail making test and its shared genetic aetiology with other tests of cognitive function in 23 821 participants from UK Biobank. The SNP-based heritability estimates for trail-making measures were 7.9 % (part A), 22.4 % (part B), and 17.6 % (part B - part A). Significant genetic correlations were identified between trail-making measures and verbal-numerical reasoning (rg > 0.6), general cognitive function (rg > 0.6), processing speed (rg > 0.7), and memory (rg > 0.3). Polygenic profile analysis indicated considerable shared genetic aetiology between trail making, general cognitive function, processing speed, and memory (standardized {beta} between 0.03 and 0.08). These results suggest that trail making is both phenotypically and genetically strongly associated with general cognitive function and processing speed.
]]></description>
<dc:creator>Hagenaars, S.</dc:creator>
<dc:creator>Cox, S. R.</dc:creator>
<dc:creator>Hill, W. D.</dc:creator>
<dc:creator>Davies, G.</dc:creator>
<dc:creator>Liewald, D. C.</dc:creator>
<dc:creator>CHARGE consortium Cognitive Working Group,</dc:creator>
<dc:creator>Harris, S. E.</dc:creator>
<dc:creator>McIntosh, A. M.</dc:creator>
<dc:creator>Gale, C. R.</dc:creator>
<dc:creator>Deary, I.</dc:creator>
<dc:date>2017-01-25</dc:date>
<dc:identifier>doi:10.1101/103119</dc:identifier>
<dc:title><![CDATA[Genetic contributions to trail making test performance in UK Biobank]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-01-25</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/168906v1?rss=1">
<title>
<![CDATA[
116 independent genetic variants influence the neuroticism personality trait in over 329,000 UK Biobank individuals. 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/168906v1?rss=1"
</link>
<description><![CDATA[
Neuroticism is a stable personality trait 1; twin studies report heritability between 30% and 50% 2, and SNP-based heritability is about 15% 3. Higher levels of neuroticism are associated with poorer mental and physical health 4,5, and the economic burden of neuroticism for societies is high 6. To date, genome-wide association (GWA) studies of neuroticism have identified up to 11 genetic loci 3,7. Here we report 116 significant independent genetic loci from a GWA of neuroticism in 329,821 UK Biobank participants, with replication available in a GWA meta-analysis of neuroticism in 122,867 individuals. Genetic signals for neuroticism were enriched in neuronal genesis and differentiation pathways, and substantial genetic correlations were found between neuroticism and depressive symptoms (rg = .82, SE=.03), major depressive disorder (rg = .69, SE=.07) and subjective wellbeing (rg = -.68, SE=.03) alongside other mental health traits. These discoveries significantly advance our understanding of neuroticism and its association with major depressive disorder.
]]></description>
<dc:creator>Luciano, M.</dc:creator>
<dc:creator>Hagenaars, S. P.</dc:creator>
<dc:creator>Davies, G.</dc:creator>
<dc:creator>Hill, W. D.</dc:creator>
<dc:creator>Clarke, T.-K.</dc:creator>
<dc:creator>Shirali, M.</dc:creator>
<dc:creator>Marioni, R.</dc:creator>
<dc:creator>Harris, S. E.</dc:creator>
<dc:creator>Liewald, D. C.</dc:creator>
<dc:creator>Fawns-Ritchie, C.</dc:creator>
<dc:creator>Adams, M. J.</dc:creator>
<dc:creator>Howard, D. M.</dc:creator>
<dc:creator>Lewis, C. M.</dc:creator>
<dc:creator>Gale, C. R.</dc:creator>
<dc:creator>McIntosh, A. M.</dc:creator>
<dc:creator>Deary, I. J.</dc:creator>
<dc:date>2017-07-28</dc:date>
<dc:identifier>doi:10.1101/168906</dc:identifier>
<dc:title><![CDATA[116 independent genetic variants influence the neuroticism personality trait in over 329,000 UK Biobank individuals.]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-07-28</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/194076v1?rss=1">
<title>
<![CDATA[
Age at first birth in women is genetically associated with increased risk of schizophrenia 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/194076v1?rss=1"
</link>
<description><![CDATA[
Previous studies have shown an increased risk for a range of mental health issues in children born to both younger and older parents compared to children of average-aged parents. However, until recently, it was not clear if these increased risks are due to psychosocial factors associated with age or if parents at higher genetic risk for psychiatric disorders tend to have children at an earlier or later age. We previously used a novel design to reveal a latent mechanism of genetic association between schizophrenia and age of mothers at the birth of their first child (AFB). Here, we use independent data from the UK Biobank (N=38,892) to replicate the finding of an association between predicted genetic risk of schizophrenia and AFB in women, end to estimate the genetic correlation between schizophrenia and AFB in women stratified into younger and older groups. We find evidence for an association between predicted genetic risk of schizophrenia and AFB in women (P-value=1.12E-05), and we show genetic heterogeneity between younger and older AFB groups (P-value=3.45E-03). The genetic correlation between schizophrenia and AFB in the younger AFB group is -0.16 (SE=0.04) while that between schizophrenia and AFB in the older AFB group is 0.14 (SE=0.08). Our results suggest that early, and perhaps also late, age at first birth in women is associated with increased genetic risk for schizophrenia. These findings contribute new insights into factors contributing to the complex bio-social risk architecture underpinning the association between parental age and offspring mental health.
]]></description>
<dc:creator>Ni, G.</dc:creator>
<dc:creator>Gratten, J.</dc:creator>
<dc:creator>Schizophrenia Working Group of the Psychiatric Gen,</dc:creator>
<dc:creator>Wray, N. R.</dc:creator>
<dc:creator>Lee, S. H.</dc:creator>
<dc:date>2017-09-27</dc:date>
<dc:identifier>doi:10.1101/194076</dc:identifier>
<dc:title><![CDATA[Age at first birth in women is genetically associated with increased risk of schizophrenia]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-09-27</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/222265v1?rss=1">
<title>
<![CDATA[
Leveraging polygenic functional enrichment to improve GWAS power 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/222265v1?rss=1"
</link>
<description><![CDATA[
Functional genomics data has the potential to increase GWAS power by identifying SNPs that have a higher prior probability of association. Here, we introduce a method that leverages polygenic functional enrichment to incorporate coding, conserved, regulatory and LD-related genomic annotations into association analyses. We show via simulations with real genotypes that the method, Functionally Informed Novel Discovery Of Risk loci (FINDOR), correctly controls the false-positive rate at null loci and attains a 9-38% increase in the number of independent associations detected at causal loci, depending on trait polygenicity and sample size. We applied FINDOR to 27 independent complex traits and diseases from the interim UK Biobank release (average N=130K). Averaged across traits, we attained a 13% increase in genome-wide significant loci detected (including a 20% increase for disease traits) compared to un-weighted raw p-values that do not use functional data. We replicated the novel loci in independent UK Biobank and non-UK Biobank data, yielding a highly statistically significant replication slope (0.66-0.69) in each case. Finally, we applied FINDOR to the full UK Biobank release (average N=416K), attaining smaller relative improvements (consistent with simulations) but larger absolute improvements, detecting an additional 583 GWAS loci. In conclusion, leveraging functional enrichment using our method robustly increases GWAS power.
]]></description>
<dc:creator>Kichaev, G.</dc:creator>
<dc:creator>Bhatia, G.</dc:creator>
<dc:creator>Loh, P.-R.</dc:creator>
<dc:creator>Gazal, S.</dc:creator>
<dc:creator>Burch, K.</dc:creator>
<dc:creator>Freund, M.</dc:creator>
<dc:creator>Scoech, A.</dc:creator>
<dc:creator>Pasaniuc, B.</dc:creator>
<dc:creator>Price, A.</dc:creator>
<dc:date>2017-11-20</dc:date>
<dc:identifier>doi:10.1101/222265</dc:identifier>
<dc:title><![CDATA[Leveraging polygenic functional enrichment to improve GWAS power]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-11-20</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/214700v1?rss=1">
<title>
<![CDATA[
A common allele in FGF21 associated with preference for sugar consumption lowers body fat in the lower body and increases blood pressure 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/214700v1?rss=1"
</link>
<description><![CDATA[
Fibroblast Growth Factor 21 (FGF21) is a hormone that induces weight loss in model organisms. These findings have led to trials in humans of FGF21 analogues with some showing weight loss and lipid lowering effects. Recent genetic studies have shown that a common allele in the FGF21 gene alters the balance of macronutrients consumed but there was little evidence of an effect on metabolic traits. We studied a common FGF21 allele (A:rs838133) in 451,099 people from the UK Biobank study. We replicated the association between the A allele and higher percentage carbohydrate intake. We then showed that this allele is more strongly associated with body fat distribution, with less fat in the lower body, and higher blood pressure, than it is with BMI, where there is only nominal evidence of an effect. These human phenotypes of naturally occurring variation in the FGF21 gene will inform decisions about FGF21s therapeutic potential.
]]></description>
<dc:creator>Frayling, T. M.</dc:creator>
<dc:creator>Beaumont, R.</dc:creator>
<dc:creator>Jones, S. E.</dc:creator>
<dc:creator>Yaghootkar, H.</dc:creator>
<dc:creator>Tuke, M. A.</dc:creator>
<dc:creator>Ruth, K. S.</dc:creator>
<dc:creator>Casanova, F.</dc:creator>
<dc:creator>West, B.</dc:creator>
<dc:creator>Locke, J.</dc:creator>
<dc:creator>Sharp, S.</dc:creator>
<dc:creator>Jie, Y.</dc:creator>
<dc:creator>Thompson, W. D.</dc:creator>
<dc:creator>Harrison, J. W.</dc:creator>
<dc:creator>Lindgren, C. M.</dc:creator>
<dc:creator>Grarup, N.</dc:creator>
<dc:creator>Murray, A.</dc:creator>
<dc:creator>Freathy, R. M.</dc:creator>
<dc:creator>Weedon, M. N.</dc:creator>
<dc:creator>Tyrrell, J.</dc:creator>
<dc:creator>Wood, A. R.</dc:creator>
<dc:date>2017-11-06</dc:date>
<dc:identifier>doi:10.1101/214700</dc:identifier>
<dc:title><![CDATA[A common allele in FGF21 associated with preference for sugar consumption lowers body fat in the lower body and increases blood pressure]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-11-06</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/203844v1?rss=1">
<title>
<![CDATA[
The Common Genetic Architecture of Anxiety Disorders 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/203844v1?rss=1"
</link>
<description><![CDATA[
Anxiety disorders are common, complex psychiatric disorders with twin heritabilities of 30-60%. We conducted a genome-wide association study of Lifetime Anxiety Disorder (n = 83 565) and an additional Current Anxiety Symptoms (n= 77 125) analysis. The liability scale common variant heritability estimate for Lifetime Anxiety Disorder was 26%, and for Current Anxiety Symptoms was 31%. Five novel genome-wide significant loci were identified including an intergenic region on chromosome 9 that has previously been associated with neuroticism, and a locus overlapping the BDNF receptor gene, NTRK2. Anxiety showed significant genetic correlations with depression and insomnia as well as coronary artery disease, mirroring findings from epidemiological studies. We conclude that common genetic variation accounts for a substantive proportion of the genetic architecture underlying anxiety.
]]></description>
<dc:creator>Purves, K. L.</dc:creator>
<dc:creator>Coleman, J. R. I.</dc:creator>
<dc:creator>Rayner, C.</dc:creator>
<dc:creator>Hettema, J. M.</dc:creator>
<dc:creator>Deckert, J.</dc:creator>
<dc:creator>McIntosh, A. M.</dc:creator>
<dc:creator>Nicodemus, K. K.</dc:creator>
<dc:creator>Breen, G.</dc:creator>
<dc:creator>Eley, T. C.</dc:creator>
<dc:date>2017-10-16</dc:date>
<dc:identifier>doi:10.1101/203844</dc:identifier>
<dc:title><![CDATA[The Common Genetic Architecture of Anxiety Disorders]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-10-16</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/028035v1?rss=1">
<title>
<![CDATA[
A robust example of collider bias in a genetic association study 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/028035v1?rss=1"
</link>
<description><![CDATA[
A recent paper by Aschard et al described the potential for "collider bias" when adjusting for heritable covariates in genetic association studies1. However, in their examples the authors acknowledged that they could not exclude the possibility of a true biological explanation for the genetic association seen only in the adjusted model. Furthermore, the extent to which this bias could create a completely spurious genetic association, rather than just modify the magnitude of the effect,2 remains unclear.nnCollider bias describes the artificial association created between two uncorrelated exposures (A and B) when a shared outcome (X) is included in the model as a covariate (Figure 1). We sought to definitively illustrate collider bias by deliberately inducing it to generate a biologically implausible SNP-phenotype association. Both sex (A) and autosomal genetic determinants ...
]]></description>
<dc:creator>Felix Day</dc:creator>
<dc:creator>Robert Scott</dc:creator>
<dc:creator>Ken Ong</dc:creator>
<dc:creator>John Perry</dc:creator>
<dc:creator></dc:creator>
<dc:date>2015-10-01</dc:date>
<dc:identifier>doi:10.1101/028035</dc:identifier>
<dc:title><![CDATA[A robust example of collider bias in a genetic association study]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2015-10-01</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/084798v1?rss=1">
<title>
<![CDATA[
Cognitive ability and physical health: a Mendelian randomization study 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/084798v1?rss=1"
</link>
<description><![CDATA[
Causes of the association between cognitive ability and health remain unknown, but may reflect a shared genetic aetiology. This study examines the causal genetic associations between cognitive ability and physical health. We carried out two-sample Mendelian randomization analyses using the inverse-variance weighted method to test for causality between later life cognitive ability, educational attainment (as a proxy for cognitive ability in youth), BMI, height, systolic blood pressure, coronary artery disease, and type 2 diabetes using data from six independent GWAS consortia and the UK Biobank sample (N = 112 151). BMI, systolic blood pressure, coronary artery disease and type 2 diabetes showed negative associations with cognitive ability; height was positively associated with cognitive ability. The analyses provided no evidence for casual associations from health to cognitive ability. In the other direction, higher educational attainment predicted lower BMI, systolic blood pressure, coronary artery disease, type 2 diabetes, and taller stature. The analyses indicated no causal association from educational attainment to physical health. The lack of evidence for causal associations between cognitive ability, educational attainment, and physical health could be explained by weak instrumental variables, poorly measured outcomes, or the small number of disease cases.
]]></description>
<dc:creator>Hagenaars, S.</dc:creator>
<dc:creator>Gale, C. R.</dc:creator>
<dc:creator>Deary, I. J.</dc:creator>
<dc:creator>Harris, S. E.</dc:creator>
<dc:date>2016-11-01</dc:date>
<dc:identifier>doi:10.1101/084798</dc:identifier>
<dc:title><![CDATA[Cognitive ability and physical health: a Mendelian randomization study]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2016-11-01</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/096214v1?rss=1">
<title>
<![CDATA[
Associations of coffee genetic risk scores with coffee, tea and other beverages in the UK Biobank 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/096214v1?rss=1"
</link>
<description><![CDATA[
BackgroundGenetic variants which determine amount of coffee consumed have been identified in genome-wide association studies (GWAS) of coffee consumption; these may help to further understanding of the effects of coffee on health outcomes. However, there is limited information about how these variants relate to caffeinated beverage consumption more generally.nnAimsTo improve phenotype definition for coffee consumption related genetic risk scores by testing their association with coffee, tea and other beverages.nnMethodsWe tested the associations of genetic risk scores for coffee consumption with beverage consumption in 114,316 individuals of European ancestry from the UK Biobank. Drinks were self-reported in a baseline questionnaire and in detailed 24 dietary recall questionnaires in a subset.nnResultsGenetic risk scores including two and eight single nucleotide polymorphisms (SNPs) explained up to 0.39%, 0.19% and 0.77% of the variance in coffee, tea and combined coffee and tea consumption respectively. A one standard deviation increase in the 8 SNP genetic risk score was associated with a 0.13 cup per day (95% CI: 0.12, 0.14), 0.12 cup per day (95%CI: 0.11, 0.14) and 0.25 cup per day (95% CI: 0.24, 0.27) increase in coffee, tea and combined tea and coffee consumption, respectively. Genetic risk scores also demonstrated positive associations with both caffeinated and decaffeinated coffee and tea consumption. In 48,692 individuals with dietary recall data, the genetic risk scores were positively associated with coffee and tea, (apart from herbal teas) consumption, but did not show clear evidence for positive associations with other beverages. However, there was evidence that the genetic risk scores were associated with lower daily water consumption and lower overall drink consumption.nnConclusionsGenetic risk scores created from variants identified in coffee consumption GWAS associate more broadly with caffeinated beverage consumption and also with decaffeinated coffee and tea consumption.
]]></description>
<dc:creator>Taylor, A. E.</dc:creator>
<dc:creator>Munafo, M. R.</dc:creator>
<dc:date>2016-12-22</dc:date>
<dc:identifier>doi:10.1101/096214</dc:identifier>
<dc:title><![CDATA[Associations of coffee genetic risk scores with coffee, tea and other beverages in the UK Biobank]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2016-12-22</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/177014v1?rss=1">
<title>
<![CDATA[
Genome-wide analysis of risk-taking behaviour and cross-disorder genetic correlations in 116,255 individuals from the UK Biobank cohort 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/177014v1?rss=1"
</link>
<description><![CDATA[
Risk-taking behaviour is a key component of several psychiatric disorders and could influence lifestyle choices such as smoking, alcohol use and diet. As a phenotype, risk-taking behaviour therefore fits within a Research Domain Criteria (RDoC) approach, whereby identifying genetic determinants of this trait has the potential to improve our understanding across different psychiatric disorders. Here we report a genome wide association study in 116 255 UK Biobank participants who responded yes/no to the question "Would you consider yourself a risk-taker?" Risk-takers (compared to controls) were more likely to be men, smokers and have a history of psychiatric disorder. Genetic loci associated with risk-taking behaviour were identified on chromosomes 3 (rs13084531) and 6 (rs9379971). The effects of both lead SNPs were comparable between men and women. The chromosome 3 locus highlights CADM2, previously implicated in cognitive and executive functions, but the chromosome 6 locus is challenging to interpret due to the complexity of the HLA region. Risk-taking behaviour shared significant genetic risk with schizophrenia, bipolar disorder, attention deficit hyperactivity disorder and post-traumatic stress disorder, as well as with smoking and total obesity. Despite being based on only a single question, this study furthers our understanding of the biology of risk-taking behaviour, a trait which has a major impact on a range of common physical and mental health disorders.
]]></description>
<dc:creator>Strawbridge, R. J.</dc:creator>
<dc:creator>Ward, J.</dc:creator>
<dc:creator>Cullen, B.</dc:creator>
<dc:creator>Tunbridge, E. M.</dc:creator>
<dc:creator>Hartz, S.</dc:creator>
<dc:creator>Bierut, L.</dc:creator>
<dc:creator>Horton, A.</dc:creator>
<dc:creator>Bailey, M. E. S.</dc:creator>
<dc:creator>Graham, N.</dc:creator>
<dc:creator>Ferguson, A.</dc:creator>
<dc:creator>Lyall, D. M.</dc:creator>
<dc:creator>Mackay, D.</dc:creator>
<dc:creator>Pidgeon, L. M.</dc:creator>
<dc:creator>Cavanagh, J.</dc:creator>
<dc:creator>Pell, J. P.</dc:creator>
<dc:creator>O'Donovan, M.</dc:creator>
<dc:creator>Escott-Price, V.</dc:creator>
<dc:creator>Harrison, P. J.</dc:creator>
<dc:creator>Smith, D. J.</dc:creator>
<dc:date>2017-08-16</dc:date>
<dc:identifier>doi:10.1101/177014</dc:identifier>
<dc:title><![CDATA[Genome-wide analysis of risk-taking behaviour and cross-disorder genetic correlations in 116,255 individuals from the UK Biobank cohort]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-08-16</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/117796v1?rss=1">
<title>
<![CDATA[
Genome-Wide Analysis Of 113,968 Individuals In UK Biobank Identifies Four Loci Associated With Mood Instability. 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/117796v1?rss=1"
</link>
<description><![CDATA[
Mood instability is a core clinical feature of affective and psychotic disorders. In keeping with the Research Domain Criteria (RDoC) approach, it may be a useful construct for identifying biology that cuts across psychiatric categories. We aimed to investigate the biological validity of a simple measure of mood instability and evaluate its genetic relationship with several psychiatric disorders, including major depressive disorder (MDD), bipolar disorder (BD), schizophrenia, attention deficit hyperactivity disorder (ADHD), anxiety disorder and post-traumatic stress disorder (PTSD). We conducted a genome-wide association study (GWAS) of mood instability in 53,525 cases and 60,443 controls from UK Biobank, identifying four independently-associated loci (on chromosomes eight, nine, 14 and 18), and a common single nucleotide polymorphism (SNP)-based heritability estimate of approximately 8%. We found a strong genetic correlation between mood instability and MDD (rg=0.60, SE=0.07, p=8.95 x 10-17) and a small but significant genetic correlation with both schizophrenia (rg=0.11, SE=0.04, p=0.01) and anxiety disorders (rg=0.28, SE=0.14, p=0.04), although no genetic correlation with BD, ADHD or PTSD. Several genes at the associated loci may have a role in mood instability, including the DCC netrin 1 receptor (DCC) gene, eukaryotic translation initiation factor 2B subunit beta (eIF2B2), placental growth factor (PGF), and protein tyrosine phosphatase, receptor type D (PTPRD). Strengths of this study include the very large sample size, but our measure of mood instability may be limited by the use of a single question. Overall, this work suggests a polygenic basis for mood instability. This simple measure can be obtained in very large samples; our findings suggest that doing so may offer the opportunity to illuminate the fundamental biology of mood regulation.
]]></description>
<dc:creator>Ward, J.</dc:creator>
<dc:creator>Strawbridge, R.</dc:creator>
<dc:creator>Graham, N.</dc:creator>
<dc:creator>Bailey, M.</dc:creator>
<dc:creator>Freguson, A.</dc:creator>
<dc:creator>Lyall, D.</dc:creator>
<dc:creator>Cullen, B.</dc:creator>
<dc:creator>Pidegon, L.</dc:creator>
<dc:creator>Cavanagh, J.</dc:creator>
<dc:creator>Mackay, D.</dc:creator>
<dc:creator>Pell, J.</dc:creator>
<dc:creator>O'Donovan, M.</dc:creator>
<dc:creator>Escott-Price, V.</dc:creator>
<dc:creator>Smith, D. J.</dc:creator>
<dc:date>2017-03-17</dc:date>
<dc:identifier>doi:10.1101/117796</dc:identifier>
<dc:title><![CDATA[Genome-Wide Analysis Of 113,968 Individuals In UK Biobank Identifies Four Loci Associated With Mood Instability.]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-03-17</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/185207v1?rss=1">
<title>
<![CDATA[
Indirect assortative mating for human disease and longevity 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/185207v1?rss=1"
</link>
<description><![CDATA[
Phenotypic correlations of couples for phenotypes evident at the time of mate choice, like height, are well documented. Similarly, phenotypic correlations among partners for traits not directly observable at the time of mate choice, like longevity or late-onset disease status, have been reported. Partner correlations for longevity and late-onset disease are comparable in magnitude to correlations in 1st degree relatives. These correlations could arise as a consequence of convergence after mate choice, due to initial assortment on observable correlates of one or more risk factors (e.g. BMI), referred to as indirect assortative mating, or both. Using couples from the UK Biobank cohort, we show that longevity and disease history of the parents of white British couples is correlated. The correlations in parental longevity are replicated in the FamiLinx cohort. These correlations exceed what would be expected due to variations in lifespan based on year and location of birth. This suggests the presence of assortment on factors correlated with disease and lifespan, which show correlations across generations. Birth year, birth location, Townsend Deprivation Index, height, waist to hip ratio, BMI and smoking history of UK Biobank couples explained ~70% of the couple correlation in parental lifespan. For cardiovascular diseases, in particular hypertension, we find significant correlations in genetic values among partners, which support a model where partners assort for risk factors genetically correlated with cardiovascular disease. Identifying the factors that mediate indirect assortment on longevity and human disease risk will help to unravel what factors affect human disease and ultimately longevity.
]]></description>
<dc:creator>Rawlik, K.</dc:creator>
<dc:creator>Canela-Xandri, O.</dc:creator>
<dc:creator>Tenesa, A.</dc:creator>
<dc:date>2017-09-07</dc:date>
<dc:identifier>doi:10.1101/185207</dc:identifier>
<dc:title><![CDATA[Indirect assortative mating for human disease and longevity]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-09-07</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/168732v1?rss=1">
<title>
<![CDATA[
Genome-wide association study of depression phenotypes in UK Biobank (n = 322,580) identifies the enrichment of variants in excitatory synaptic pathways 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/168732v1?rss=1"
</link>
<description><![CDATA[
Depression is a polygenic trait that causes extensive periods of disability and increases the risk of suicide, a leading cause of death in young people. Previous genetic studies have identified a number of common risk variants which have increased in number in line with increasing sample sizes. We conducted a genome-wide association study (GWAS) in the largest single population-based cohort to date, UK Biobank. This allowed us to estimate the effects of {approx} 8 million genetic variants in 320,000 people for three depression phenotypes: broad depression, probable major depressive disorder (MDD), and International Classification of Diseases (ICD, version 9 or 10)-coded MDD. Each phenotype was found to be significantly genetically correlated with the results from a previous independent study of clinically defined MDD. We identified 14 independent loci that were significantly associated (P < 5 x 10-8) with broad depression, two independent variants for probable MDD, and one independent variant for ICD-coded MDD. Gene-based analysis of our GWAS results with MAGMA revealed 46 regions significantly associated (P < 2.77 x 10-6) with broad depression, two significant regions for probable MDD and one significant region for ICD-coded MDD. Gene region-based analysis of our GWAS results with MAGMA revealed 59 regions significantly associated (P < 6.02 x 10-6) with broad depression, of which 27 were also detected by gene-based analysis. Variants for broad depression were enriched in pathways for excitatory neurotransmission, mechanosensory behavior, postsynapse, neuron spine and dendrite. This study provides a number of novel genetic risk variants that can be leveraged to elucidate the mechanisms of MDD and low mood.
]]></description>
<dc:creator>Howard, D. M.</dc:creator>
<dc:creator>Adams, M. J.</dc:creator>
<dc:creator>Shirali, M.</dc:creator>
<dc:creator>Clarke, T.-K.</dc:creator>
<dc:creator>Marioni, R. E.</dc:creator>
<dc:creator>Davies, G.</dc:creator>
<dc:creator>Coleman, J. R. I.</dc:creator>
<dc:creator>Alloza, C.</dc:creator>
<dc:creator>Shen, X.</dc:creator>
<dc:creator>Barbu, M. C.</dc:creator>
<dc:creator>Wigmore, E. M.</dc:creator>
<dc:creator>Hagenaars, S.</dc:creator>
<dc:creator>Lewis, C. M.</dc:creator>
<dc:creator>Smith, D. J.</dc:creator>
<dc:creator>Sullivan, P. F.</dc:creator>
<dc:creator>Haley, C. S.</dc:creator>
<dc:creator>Breen, G.</dc:creator>
<dc:creator>Deary, I. J.</dc:creator>
<dc:creator>McIntosh, A. M.</dc:creator>
<dc:date>2017-07-27</dc:date>
<dc:identifier>doi:10.1101/168732</dc:identifier>
<dc:title><![CDATA[Genome-wide association study of depression phenotypes in UK Biobank (n = 322,580) identifies the enrichment of variants in excitatory synaptic pathways]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-07-27</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/033134v1?rss=1">
<title>
<![CDATA[
Accurate genetic profiling of anthropometric traits using a big data approach 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/033134v1?rss=1"
</link>
<description><![CDATA[
Genome-wide association studies (GWAS) promised to translate their findings into clinically beneficial improvements of patient management by tailoring disease management to the individual through the prediction of disease risk1,2. However, the ability to translate genetic findings from GWAS into predictive tools that are of clinical utility and which may inform clinical practice has, so far, been encouraging but limited1,2. Here we propose to use a more powerful statistical approach that enables the prediction of multiple medically relevant phenotypes without the costs associated with developing a genetic test for each of them. As a proof of principle, we used a common panel of 319,038 SNPs to train the prediction models in 114,264 unrelated White-British for height and four obesity related traits (body mass index, basal metabolic rate, body fat percentage, and waist-to-hip ratio). We obtained prediction accuracies that ranged between 46% and 75% of the maximum achievable given their explained heritable component. This represents an improvement of up to 75% over the phenotypic variance explained by the predictors developed through large collaborations3, which used more than twice as many training samples. Across-population predictions in White nonBritish individuals were similar to those of White-British whilst those in Asian and Black individuals were informative but less accurate. The genotyping of circa 500,000 UK Biobank4 participants will yield predictions ranging between 66% and 83% of the maximum. We anticipate that our models and a common panel of genetic markers, which can be used across multiple traits and diseases, will be the starting point to tailor disease management to the individual. Ultimately, we will be able to capitalise on whole-genome sequence and environmental risk factors to realise the full potential of genomic medicine.
]]></description>
<dc:creator>Oriol Canela-Xandri</dc:creator>
<dc:creator>Konrad Rawlik</dc:creator>
<dc:creator>John A. Woolliams</dc:creator>
<dc:creator>Albert Tenesa</dc:creator>
<dc:creator></dc:creator>
<dc:date>2015-12-01</dc:date>
<dc:identifier>doi:10.1101/033134</dc:identifier>
<dc:title><![CDATA[Accurate genetic profiling of anthropometric traits using a big data approach]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2015-12-01</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/236182v1?rss=1">
<title>
<![CDATA[
Searching for the causal effects of BMI in over 300 000 individuals, using Mendelian randomization 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/236182v1?rss=1"
</link>
<description><![CDATA[
Mendelian randomization (MR) has been used to estimate the causal effect of body mass index (BMI) on particular traits thought to be affected by BMI. However, BMI may also be a modifiable, causal risk factor for outcomes where there is no prior reason to suggest that a causal effect exists. We perform a MR phenome-wide association study (MR-pheWAS) to search for the causal effects of BMI in UK Biobank (n=334 968), using the PHESANT open-source phenome scan tool. Of the 20 461 tests performed, our MR-pheWAS identified 519 associations below a stringent P value threshold corresponding to a 5% estimated false discovery rate, including many previously identified causal effects. We also identified several novel effects, including protective effects of higher BMI on a set of psychosocial traits, identified initially in our preliminary MR-pheWAS and replicated in an independent subset of UK Biobank. Such associations need replicating in an independent sample.
]]></description>
<dc:creator>Millard, L. A. C.</dc:creator>
<dc:creator>Davies, N. M.</dc:creator>
<dc:creator>Tilling, K.</dc:creator>
<dc:creator>Gaunt, T. R.</dc:creator>
<dc:creator>Davey Smith, G.</dc:creator>
<dc:date>2017-12-19</dc:date>
<dc:identifier>doi:10.1101/236182</dc:identifier>
<dc:title><![CDATA[Searching for the causal effects of BMI in over 300 000 individuals, using Mendelian randomization]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-12-19</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/158113v1?rss=1">
<title>
<![CDATA[
ukbtools: An R package to manage and query UK Biobank data 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/158113v1?rss=1"
</link>
<description><![CDATA[
SummaryThe UK Biobank is a resource that includes detailed health-related data on about 500,000 individuals and is available to the research community. ukbtools removes all the upfront data wrangling required to get a single dataset for statistical analysis, and provides tools to assist in quality control, query of disease diagnoses, and retrieval of genetic metadata.nnAvailabilityThe package is available for installation from the Comprehensive R Archive Network (CRAN), and includes a vignette describing the use of all functionality.nnContact ken.b.hanscombe@kcl.ac.uk, https://github.com/kenhanscombe/ukbtools
]]></description>
<dc:creator>Hanscombe, K. B.</dc:creator>
<dc:creator>Coleman, J. R. I.</dc:creator>
<dc:creator>Traylor, M.</dc:creator>
<dc:creator>Lewis, C. M.</dc:creator>
<dc:date>2017-06-30</dc:date>
<dc:identifier>doi:10.1101/158113</dc:identifier>
<dc:title><![CDATA[ukbtools: An R package to manage and query UK Biobank data]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-06-30</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/146787v1?rss=1">
<title>
<![CDATA[
Genetic contribution to two factors of neuroticism is associated with affluence, better health, and longer life 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/146787v1?rss=1"
</link>
<description><![CDATA[
Neuroticism is a personality trait that describes the tendency to experience negative emotions. Individual differences in neuroticism are moderately stable across much of the life course1; the trait is heritable2-5, and higher levels are associated with psychiatric disorders6-8, and have been estimated to have an economic burden to society greater than that of substance abuse, mood, or anxiety disorders9. Understanding the genetic architecture of neuroticism therefore has the potential to offer insight into the causes of psychiatric disorders, general wellbeing10, and longevity. The broad trait of neuroticism is composed of narrower traits, or factors. It was recently discovered that, whereas higher scores on the broad trait of neuroticism are associated with earlier death, higher scores on a  worry/vulnerability factor are associated with living longer11. Here, we examine the genetic architectures of two neuroticism factors--worry/vulnerability and anxiety/tension--and how they contrast with the architecture of the general factor of neuroticism. We show that, whereas the polygenic load for general factor of neuroticism is associated with an increased risk of coronary artery disease (CAD), major depressive disorder, and poorer self-rated health, the genetic variants associated with high levels of the anxiety/tension and worry/vulnerability factors are associated with affluence, higher cognitive ability, better self-rated health, and longer life. We also identify the first genes associated with factors of neuroticism that are linked with these positive outcomes that show no relationship with the general factor of neuroticism.
]]></description>
<dc:creator>Hill, W. D.</dc:creator>
<dc:creator>Weiss, A.</dc:creator>
<dc:creator>McIntosh, A. M.</dc:creator>
<dc:creator>Gale, C. R.</dc:creator>
<dc:creator>Deary, I. J.</dc:creator>
<dc:date>2017-06-06</dc:date>
<dc:identifier>doi:10.1101/146787</dc:identifier>
<dc:title><![CDATA[Genetic contribution to two factors of neuroticism is associated with affluence, better health, and longer life]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-06-06</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/027490v1?rss=1">
<title>
<![CDATA[
Variants in the FTO and CDKAL1 loci have recessive effects on risk of obesity and type 2 diabetes respectively 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/027490v1?rss=1"
</link>
<description><![CDATA[
Genome-wide association studies have identified hundreds of common genetic variants associated with obesity and Type 2 diabetes. These studies have focused on additive association tests. Identifying deviations from additivity may provide new biological insights and explain some of the missing heritability for these diseases.nnTo identify non-additive associations we performed a genome-wide association study using a dominance deviation model for BMI, obesity and Type 2 diabetes (4,040 cases) in 120,286 individuals of British ancestry from the UK Biobank study.nnKnown obesity-associated variants in FTO showed strong evidence for deviation from additivity (P=3x10-5) through a recessive effect of the BMI-increasing allele. The average BMI of individuals carrying 0, 1 or 2 BMI-raising alleles was 27.27kg/m2 (95% CI:27.22-27.31), 27.54kg/m2 (95% CI:27.50-27.58), and 28.07kg/m2 (95% CI:28.0-28.14), respectively. A similar effect was observed in 105,643 individuals from the GIANT consortium (P=0.003; Pmeta-analysis=1x10-7). We also detected a recessive effect (Pdomdev=5x10-4) at CDKAL1 for Type 2 diabetes risk. Homozygous risk allele carriers had an OR=1.48 (95% CI:1.32-1.65) in comparison to the heterozygous group that had an OR=1.06 (95% CI:0.99-1.14), a result consistent with a previous study. We did not identify any novel genome-wide associations.nnIn conclusion, although we find no evidence for widespread non-additive effects contributing to the genetic risk of obesity and Type 2 diabetes, we find robust examples of recessive effects at the FTO and CDKAL1 loci.
]]></description>
<dc:creator>Andrew R Wood</dc:creator>
<dc:creator>Jessica Tyrell</dc:creator>
<dc:creator>Robin Beaumont</dc:creator>
<dc:creator>Samuel E Jones</dc:creator>
<dc:creator>Marcus A Tuke</dc:creator>
<dc:creator>Katherine S Ruth</dc:creator>
<dc:creator>The GIANT consortium</dc:creator>
<dc:creator>Hanieh Yaghootkar</dc:creator>
<dc:creator>Rachel Freathy</dc:creator>
<dc:creator>Anna Murray</dc:creator>
<dc:creator>Timothy M Frayling</dc:creator>
<dc:creator>Michael N Weedon</dc:creator>
<dc:creator></dc:creator>
<dc:date>2015-09-24</dc:date>
<dc:identifier>doi:10.1101/027490</dc:identifier>
<dc:title><![CDATA[Variants in the FTO and CDKAL1 loci have recessive effects on risk of obesity and type 2 diabetes respectively]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2015-09-24</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/172247v1?rss=1">
<title>
<![CDATA[
The Impact of Education on Myopia: A bidirectional Mendelian randomisation analysis in UK Biobank 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/172247v1?rss=1"
</link>
<description><![CDATA[
Myopia, or short-sightedness, is one of the leading causes of visual disability in the World. The prevalence of myopia has risen steadily over recent decades, reaching epidemic levels in Southeast Asia. Observational studies have reported associations between educational attainment and myopia. Whether education causes myopia, myopic children are more intelligent, or another factor, like higher socioeconomic status, causes both is unclear since observational studies are prone to confounding and randomised trials of education are unethical. Using bidirectional Mendelian Randomisation, a form of instrumental variable (IV) analysis free from confounding, we show that every additional year in education leads to an increase in myopic refractive error, but that myopia does not lead to higher educational attainment. Our results suggest that current educational methods contribute to the global burden of myopia, and argue that educational policies and practices should take account of this to reduce future visual disability in the population.
]]></description>
<dc:creator>Mountjoy, E. J.</dc:creator>
<dc:creator>Davies, N. M.</dc:creator>
<dc:creator>Plotnikov, D.</dc:creator>
<dc:creator>Davey Smith, G.</dc:creator>
<dc:creator>Rodriguez, S.</dc:creator>
<dc:creator>Williams, C.</dc:creator>
<dc:creator>Guggenheim, J. A.</dc:creator>
<dc:creator>Atan, D.</dc:creator>
<dc:date>2017-08-04</dc:date>
<dc:identifier>doi:10.1101/172247</dc:identifier>
<dc:title><![CDATA[The Impact of Education on Myopia: A bidirectional Mendelian randomisation analysis in UK Biobank]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-08-04</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/068643v1?rss=1">
<title>
<![CDATA[
A genome-wide haplotype association analysis of major depressive disorder identifies two genome-wide significant haplotypes 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/068643v1?rss=1"
</link>
<description><![CDATA[
Genome-wide association studies using genotype data have had limited success in the identification of variants associated with major depressive disorder (MDD). Haplotype data provide an alternative method for detecting associations between variants in weak linkage disequilibrium with genotyped variants and a given trait of interest. A genome-wide haplotype association study for MDD was undertaken utilising a family-based population cohort, Generation Scotland: Scottish Family Health Study (n = 18 773), as a discovery cohort with UK Biobank used as a population-based cohort replication cohort (n = 25 035). Fine mapping of haplotype boundaries was used to account for overlapping haplotypes potentially tagging the same causal variant. Within the discovery cohort, two haplotypes exceeded genome-wide significance (P < 5 x 10-8) for an association with MDD. One of these haplotypes was nominally significant in the replication cohort (P < 0.05) and was located in 6q21, a region which has been previously associated with bipolar disorder, a psychiatric disorder that is phenotypically and genetically correlated with MDD. Several haplotypes with P < 10-7 in the discovery cohort were located within gene coding regions associated with diseases that are comorbid with MDD. Using such haplotypes to highlight regions for sequencing may lead to the identification of the underlying causal variants.
]]></description>
<dc:creator>David M Howard</dc:creator>
<dc:creator>Lynsey S Hall</dc:creator>
<dc:creator>Jonathan D Hafferty</dc:creator>
<dc:creator>Yanni Zeng</dc:creator>
<dc:creator>Mark J Adams</dc:creator>
<dc:creator>Toni-Kim Clarke</dc:creator>
<dc:creator>David J Porteous</dc:creator>
<dc:creator>Caroline Hayward</dc:creator>
<dc:creator>Blair H Smith</dc:creator>
<dc:creator>Alison D Murray</dc:creator>
<dc:creator>Niamh M Ryan</dc:creator>
<dc:creator>Kathryn L Evans</dc:creator>
<dc:creator>Chris S Haley</dc:creator>
<dc:creator>Ian J Deary</dc:creator>
<dc:creator>Pippa A Thomson</dc:creator>
<dc:creator>Andrew M McIntosh</dc:creator>
<dc:creator></dc:creator>
<dc:date>2016-08-22</dc:date>
<dc:identifier>doi:10.1101/068643</dc:identifier>
<dc:title><![CDATA[A genome-wide haplotype association analysis of major depressive disorder identifies two genome-wide significant haplotypes]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2016-08-22</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/217786v1?rss=1">
<title>
<![CDATA[
A genome-wide association study finds novel genetic associations with broadly-defined headache in UK Biobank (N = 223,773) 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/217786v1?rss=1"
</link>
<description><![CDATA[
Headache is the most common neurological symptom and a leading cause of years lived with disability. We sought to identify the genetic variants associated with a broadly-defined headache phenotype in 223,773 subjects from the UK Biobank cohort. We defined headache based on a specific question answered by the UK Biobank participants. We performed a genome-wide association study of headache as a single entity, using 74,461 cases and 149,312 controls. We identified 3,343 SNPs which reached the genome-wide significance level of P < 5 x 10-8. The SNPs were located in 28 loci, with the top SNP of rs11172113 in the LRP1 gene having a P value of 4.92 x 10-47. Of the 28 loci, 14 have previously been associated with migraine. Among 14 new loci, rs77804065 with a P value of 5.87 x 10-15 in the LINC02210-CRHR1 gene was the top SNP.nnPositive relationships (P < 0.001) between multiple brain tissues and genetic associations were identified through tissue expression analysis, whereas no vascular related tissues showed significant relationships. We identified several significant positive genetic correlations between headache and other psychological traits including neuroticism, depressive symptoms, insomnia, and major depressive disorder.nnOur results suggest that brain function is closely related to broadly-defined headache. In addition, we also found that many psychological traits have genetic correlations with headache.
]]></description>
<dc:creator>Meng, W.</dc:creator>
<dc:creator>Adams, M.</dc:creator>
<dc:creator>Hebert, H.</dc:creator>
<dc:creator>Deary, I.</dc:creator>
<dc:creator>McIntosh, A.</dc:creator>
<dc:creator>Smith, B.</dc:creator>
<dc:date>2017-11-10</dc:date>
<dc:identifier>doi:10.1101/217786</dc:identifier>
<dc:title><![CDATA[A genome-wide association study finds novel genetic associations with broadly-defined headache in UK Biobank (N = 223,773)]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-11-10</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/212357v1?rss=1">
<title>
<![CDATA[
Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/212357v1?rss=1"
</link>
<description><![CDATA[
In genome-wide association studies (GWAS) for thousands of phenotypes in large biobanks, most binary traits have substantially fewer cases than controls. Both of the widely used approaches, linear mixed model and the recently proposed logistic mixed model, perform poorly - producing large type I error rates - in the analysis of phenotypes with unbalanced case-control ratios. Here we propose a scalable and accurate generalized mixed model association test that uses the saddlepoint approximation (SPA) to calibrate the distribution of score test statistics. This method, SAIGE, provides accurate p-values even when case-control ratios are extremely unbalanced. It utilizes state-of-art optimization strategies to reduce computational time and memory cost of generalized mixed model. The computation cost linearly depends on sample size, and hence can be applicable to GWAS for thousands of phenotypes by large biobanks. Through the analysis of UK Biobank data of 408,961 white British European-ancestry samples for >1400 binary phenotypes, we show that SAIGE can efficiently analyze large sample data, controlling for unbalanced case-control ratios and sample relatedness.
]]></description>
<dc:creator>Zhou, W.</dc:creator>
<dc:creator>Nielsen, J. B.</dc:creator>
<dc:creator>Fritsche, L. G.</dc:creator>
<dc:creator>Dey, R.</dc:creator>
<dc:creator>Elvestad, M. B.</dc:creator>
<dc:creator>Wolford, B. B.</dc:creator>
<dc:creator>LeFaive, J.</dc:creator>
<dc:creator>Lin, M.</dc:creator>
<dc:creator>Hveem, K.</dc:creator>
<dc:creator>Kang, H. M.</dc:creator>
<dc:creator>Abecasis, G. R.</dc:creator>
<dc:creator>Willer, C. J.</dc:creator>
<dc:creator>Lee, S.</dc:creator>
<dc:date>2017-11-01</dc:date>
<dc:identifier>doi:10.1101/212357</dc:identifier>
<dc:title><![CDATA[Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-11-01</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/105122v1?rss=1">
<title>
<![CDATA[
Bayesian analysis of genetic association across tree-structured routine healthcare data in the UK Biobank 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/105122v1?rss=1"
</link>
<description><![CDATA[
Genetic discovery from the multitude of phenotypes extractable from routine healthcare data has the ability to radically transform our understanding of the human phenome, thereby accelerating progress towards precision medicine. However, a critical question when analysing high-dimensional and heterogeneous data is how to interrogate increasingly specific subphenotypes whilst retaining statistical power to detect genetic associations. Here we develop and employ a novel Bayesian analysis framework that exploits the hierarchical structure of diagnosis classifications to jointly analyse genetic variants against UK Biobank healthcare phenotypes. Our method displays a more than 20% increase in power to detect genetic effects over other approaches, such that we uncover the broader burden of genetic variation: we identify associations with over 2,000 diagnostic terms. We find novel associations with common immune-mediated diseases (IMD), we reveal the extent of genetic sharing between specific IMDs, and we expose differences in disease perception or diagnosis with potential clinical implications.
]]></description>
<dc:creator>Cortes, A.</dc:creator>
<dc:creator>Dendrou, C.</dc:creator>
<dc:creator>Motyer, A.</dc:creator>
<dc:creator>Jostins, L.</dc:creator>
<dc:creator>Vukcevic, D.</dc:creator>
<dc:creator>Dilthey, A.</dc:creator>
<dc:creator>Donnelly, P.</dc:creator>
<dc:creator>Leslie, S.</dc:creator>
<dc:creator>Fugger, L.</dc:creator>
<dc:creator>McVean, G.</dc:creator>
<dc:date>2017-02-01</dc:date>
<dc:identifier>doi:10.1101/105122</dc:identifier>
<dc:title><![CDATA[Bayesian analysis of genetic association across tree-structured routine healthcare data in the UK Biobank]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-02-01</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/160044v1?rss=1">
<title>
<![CDATA[
Using Structural Equation Modeling to Jointly Estimate Maternal and Foetal Effects on Birthweight in the UK Biobank 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/160044v1?rss=1"
</link>
<description><![CDATA[
BackgroundTo date, 60 genetic variants have been robustly associated with birthweight. It is unclear whether these associations represent the effect of an individuals own genotype on their birthweight, their mothers genotype, or both.nnMethodsWe demonstrate how structural equation modelling (SEM) can be used to estimate both maternal and foetal effects when phenotype information is present for individuals in two generations and genotype information is available on the older individual. We conduct an extensive simulation study to assess the bias, power and type 1 error rates of the SEM and also apply the SEM to birthweight data in the UK Biobank study.nnResultsUnlike simple regression models, our approach is unbiased when there is both a maternal and foetal effect. The method can be used when either the individuals own phenotype or the phenotype of their offspring is not available, and allows the inclusion of summary statistics from additional cohorts where raw data cannot be shared. We show that the type 1 error rate of the method is appropriate, there is substantial statistical power to detect a genetic variant that has a moderate effect on the phenotype, and reasonable power to detect whether it is a foetal and/or maternal effect. We also identify a subset of birth weight associated SNPs that have opposing maternal and foetal effects in the UK Biobank.nnConclusionsOur results show that SEM can be used to estimate parameters that would be difficult to quantify using simple statistical methods alone.nnKey MessagesO_LIWe describe a structural equation model to estimate both maternal and foetal effects when phenotype information is present for individuals in two generations and genotype information is available on the older individual.nC_LIO_LIUsing simulation, we show that our approach is unbiased when there is both a maternal and foetal effect, unlike simple linear regression models. Additionally, we illustrate that the structural equation model is largely robust to measurement error and missing data for either the individuals own phenotype or the phenotype of their offspring.nC_LIO_LIWe describe how the flexibility of the structural equation modelling framework will allow the inclusion of summary statistics from studies that are unable to share raw data.nC_LIO_LIUsing the structural equation model to estimate the maternal and foetal effects of known birthweight associated loci in the UK Biobank, we identify three loci that have primary effects through the maternal genome and six loci that have opposite effects in the maternal and foetal genomes.nC_LI
]]></description>
<dc:creator>Warrington, N.</dc:creator>
<dc:creator>Freathy, R.</dc:creator>
<dc:creator>Neale, M. C.</dc:creator>
<dc:creator>Evans, D. M.</dc:creator>
<dc:date>2017-07-06</dc:date>
<dc:identifier>doi:10.1101/160044</dc:identifier>
<dc:title><![CDATA[Using Structural Equation Modeling to Jointly Estimate Maternal and Foetal Effects on Birthweight in the UK Biobank]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-07-06</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/177659v1?rss=1">
<title>
<![CDATA[
Phenotypes associated with female X chromosome aneuploidy in UK Biobank: an unselected, adult, population-based cohort 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/177659v1?rss=1"
</link>
<description><![CDATA[
Women with X chromosome aneuploidy such as 45,X (Turner syndrome) or 47,XXX (Triple X syndrome) present with characteristics including differences in stature, increased cardiovascular disease risk and primary ovarian insufficiency. Many women with X chromosome aneuploidy undergo lifetime clinical monitoring for possible complications. However, ascertainment of cases in the clinic may mean that the phenotypic penetrance is overestimated. Studies of prenatally ascertained X chromosome aneuploidy cases have limited follow-up data and so the long-term consequences into adulthood are often not reported. We aimed to characterise the prevalence and phenotypic consequences of X chromosome aneuploidy in a large population of women over 40 years of age. We detected 30 women with 45,X, 186 with mosaic 45,X/46,XX and 110 with 47,XXX among 244,848 UK Biobank women, using SNP array data. The prevalence of non-mosaic 45,X (1/8,162) and 47,XXX (1/2,226) was lower than expected, but was higher for mosaic 45,X/46,XX (1/1,316). The characteristics of women with 45,X were consistent with the characteristics of a clinically recognised Turner syndrome phenotype, including a 17.2cm shorter stature (SD = 5.72cm; P = 1.5 x 10-53) and 16/30 did not report an age at menarche. The phenotype of women with 47,XXX included taller stature (5.3cm; SD = 5.52cm; P = 5.8 x 10-20), earlier menopause age (5.12 years; SD = 5.1 years; P = 1.2 x 10-14) and a lower fluid intelligence score (24%; SD = 29.7%; P = 3.7 x 10-8). In contrast, the characteristics of women with mosaic 45,X/46,XX were much less pronounced than expected. Women with mosaic 45,X/46,XX were less short, had a normal reproductive lifespan and birth rate, and no reported cardiovascular complications. In conclusion, the availability of data from 244,848 women allowed us to assess the phenotypic penetrance of traits associated with X chromosome aneuploidy in an adult population setting. Our results suggest that the clinical management of women with 45,X/46,XX mosaicism should be minimal, particularly those identified incidentally.nnFundingNone
]]></description>
<dc:creator>Tuke, M. A.</dc:creator>
<dc:creator>Ruth, K. S.</dc:creator>
<dc:creator>Wood, A. R.</dc:creator>
<dc:creator>Beaumont, R. N.</dc:creator>
<dc:creator>Tyrrell, J.</dc:creator>
<dc:creator>Jones, S. E.</dc:creator>
<dc:creator>Yaghootkar, H.</dc:creator>
<dc:creator>Turner, C. L. S.</dc:creator>
<dc:creator>Donohoe, M. E.</dc:creator>
<dc:creator>Brooke, A. M.</dc:creator>
<dc:creator>Collinson, M. N.</dc:creator>
<dc:creator>Freathy, R. M.</dc:creator>
<dc:creator>Weedon, M. N.</dc:creator>
<dc:creator>Frayling, T. M.</dc:creator>
<dc:creator>Murray, A.</dc:creator>
<dc:date>2017-08-18</dc:date>
<dc:identifier>doi:10.1101/177659</dc:identifier>
<dc:title><![CDATA[Phenotypes associated with female X chromosome aneuploidy in UK Biobank: an unselected, adult, population-based cohort]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-08-18</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/183483v1?rss=1">
<title>
<![CDATA[
Bioimpedance and new onset heart failure: A longitudinal study of ~500,000 individuals from the general population 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/183483v1?rss=1"
</link>
<description><![CDATA[
ImportanceHeart failure constitutes a high burden on patients and society, but although lifetime risk is high, it is difficult to predict without costly or invasive testing. Knowledge about novel risk factors could enable early diagnosis and possibly preemptive treatment.nnObjectiveTo establish new risk factors for heart failure.nnDesignWe applied supervised machine learning in UK Biobank in an agnostic search of risk factors for heart failure. Novel predictors were then subjected to several in-depth analyses, including multivariable Cox models of incident heart failure, and assessment of discrimination and calibration.nnSettingPopulation-based cohort study.nnParticipants500,451 individuals who volunteered to participate in the UK Biobank cohort study, excluding those with prevalent heart failure.nnExposure3646 variables reflecting different aspects of lifestyle, health and disease-related factors.nnMain OutcomeIncident heart failure hospitalization.nnResultsMachine learning confirmed many known and putative risk factors for heart failure, and identified several novel candidates. Mean reticulocyte volume appeared as one novel factor, and leg bioimpedance another; the latter appearing as the most important new factor. Leg bioimpedance was significantly lower in those who developed heart failure (p=1.1x10-72) during up to 9.8-year follow-up. When adjusting for known heart failure risk factors, leg bioimpedance was inversely related to heart failure (hazard ratio [95%CI], 0.60 [0.48-0.73]) and 0.75 [0.59-0.94], in age- and sex-adjusted and fully adjusted models, respectively, comparing the upper vs. lower quartile). A model including leg bioimpedance, age, sex, and self-reported history of myocardial infarction showed good predictive capacity of future heart failure hospitalization (C-index=0.82) and good calibration.nnConclusions and RelevanceLeg bioimpedance is inversely associated with heart failure incidence in the general population. A simple model of exclusively non-invasive measures, combining leg bioimpedance with history of myocardial infarction, age, and sex provides accurate predictive capacity.nnKey pointsO_ST_ABSQuestionC_ST_ABSWhich are the most important risk factors for incident heart failure?nnFindingsIn this population-based cohort study of ~500,000 individuals, machine learning identified well-established risk factors, but also several novel factors. Among the most important were leg bioimpedance and mean reticulocyte volume. There was a strong inverse relationship between leg bioimpedance and incident heart failure, also in adjusted analyses. A model entailing leg bioimpedance, age, sex, and self-reported history of myocardial infarction showed good predictive capacity of heart failure hospitalization and good calibration.nnMeaningLeg bioimpedance appears to be an important new factor associated with incident heart failure.
]]></description>
<dc:creator>Lindholm, D.</dc:creator>
<dc:creator>Fukaya, E.</dc:creator>
<dc:creator>Leeper, N. J.</dc:creator>
<dc:creator>Ingelsson, E.</dc:creator>
<dc:date>2017-09-04</dc:date>
<dc:identifier>doi:10.1101/183483</dc:identifier>
<dc:title><![CDATA[Bioimpedance and new onset heart failure: A longitudinal study of ~500,000 individuals from the general population]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-09-04</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/179762v1?rss=1">
<title>
<![CDATA[
Medical relevance of protein-truncating variants across 337,208 individuals in the UK Biobank study 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/179762v1?rss=1"
</link>
<description><![CDATA[
Protein-truncating variants can have profound effects on gene function and are critical for clinical genome interpretation and generating therapeutic hypotheses, but their relevance to medical phenotypes has not been systematically assessed. We characterized the effect of 18,228 protein-truncating variants across 135 phenotypes from the UK Biobank and found 27 associations between medical phenotypes and protein-truncating variants in genes outside the major histocompatibility complex. We performed phenome-wide analyses and directly measured the effect of homozygous carriers, commonly referred to as "human knockouts," across medical phenotypes for genes implicated to be protective against disease or associated with at least one phenotype in our study and found several genes with strong pleiotropic or non-additive effects. Our results illustrate the importance of protein-truncating variants in a variety of diseases.
]]></description>
<dc:creator>DeBoever, C.</dc:creator>
<dc:creator>Tanigawa, Y.</dc:creator>
<dc:creator>McInnes, G.</dc:creator>
<dc:creator>Lavertu, A.</dc:creator>
<dc:creator>Chang, C.</dc:creator>
<dc:creator>Bustamante, C. D.</dc:creator>
<dc:creator>Daly, M. J.</dc:creator>
<dc:creator>Rivas, M. A.</dc:creator>
<dc:date>2017-08-23</dc:date>
<dc:identifier>doi:10.1101/179762</dc:identifier>
<dc:title><![CDATA[Medical relevance of protein-truncating variants across 337,208 individuals in the UK Biobank study]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-08-23</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/130385v1?rss=1">
<title>
<![CDATA[
Image Processing and Quality Control for the first 10,000 Brain Imaging Datasets from UK Biobank 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/130385v1?rss=1"
</link>
<description><![CDATA[
UK Biobank is a large-scale prospective epidemiological study with all data accessible to researchers worldwide. It is currently in the process of bringing back 100,000 of the original participants for brain, heart and body MRI, carotid ultrasound and low-dose bone/fat x-ray. The brain imaging component covers 6 modalities (T1, T2 FLAIR, susceptibility weighted MRI, Resting fMRI, Task fMRI and Diffusion MRI). Raw and processed data from the first 10,000 imaged subjects has recently been released for general research access. To help convert this data into useful summary information we have developed an automated processing and QC (Quality Control) pipeline that is available for use by other researchers. In this paper we describe the pipeline in detail, following a brief overview of UK Biobank brain imaging and the acquisition protocol. We also describe several quantitative investigations carried out as part of the development of both the imaging protocol and the processing pipeline.
]]></description>
<dc:creator>Alfaro-Almagro, F.</dc:creator>
<dc:creator>Jenkinson, M.</dc:creator>
<dc:creator>Bangerter, N. K.</dc:creator>
<dc:creator>Andersson, J. L.</dc:creator>
<dc:creator>Griffanti, L.</dc:creator>
<dc:creator>Douaud, G.</dc:creator>
<dc:creator>Sotiropoulos, S.</dc:creator>
<dc:creator>Jbabdi, S.</dc:creator>
<dc:creator>Hernandez Fernandez, M.</dc:creator>
<dc:creator>Vallee, E.</dc:creator>
<dc:creator>Vidaurre, D.</dc:creator>
<dc:creator>Webster, M.</dc:creator>
<dc:creator>McCarthy, P. D.</dc:creator>
<dc:creator>Rorden, C.</dc:creator>
<dc:creator>Daducci, A.</dc:creator>
<dc:creator>Alexander, D.</dc:creator>
<dc:creator>Zhang, H.</dc:creator>
<dc:creator>Dragonu, I.</dc:creator>
<dc:creator>Matthews, P.</dc:creator>
<dc:creator>Miller, K. L.</dc:creator>
<dc:creator>Smith, S. M.</dc:creator>
<dc:date>2017-04-24</dc:date>
<dc:identifier>doi:10.1101/130385</dc:identifier>
<dc:title><![CDATA[Image Processing and Quality Control for the first 10,000 Brain Imaging Datasets from UK Biobank]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-04-24</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/085969v1?rss=1">
<title>
<![CDATA[
Identifying genetic variants that affect viability in large cohorts 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/085969v1?rss=1"
</link>
<description><![CDATA[
A number of open questions in human evolutionary genetics would become tractable if we were able to directly measure evolutionary fitness. As a step towards this goal, we developed a method to examine whether individual genetic variants, or sets of genetic variants, currently influence viability. The approach consists in testing whether the frequency of an allele varies across ages, accounting for variation in ancestry. We applied it to the Genetic Epidemiology Research on Aging (GERA) cohort and to the parents of participants in the UK Biobank. Across the genome, we find only a few common variants with large effects on age-specific mortality: tagging the APOE {varepsilon}4 allele and near CHRNA3. These results suggest that when large, even late onset effects are kept at low frequency by purifying selection. Testing viability effects of sets of genetic variants that jointly influence one of 42 traits, we detect a number of strong signals. In participants of the UK Biobank study of British ancestry, we find that variants that delay puberty timing are enriched in longer-lived parents (P~6x10-6 for fathers and P~2x10-3 for mothers), consistent with epidemiological studies. Similarly, in mothers, variants associated with later age at first birth are associated with a longer lifespan (P~1x10-3). Signals are also observed for variants influencing cholesterol levels, risk of coronary artery disease, body mass index, as well as risk of asthma. These signals exhibit consistent effects in the GERA cohort and among participants of the UK Biobank of non-British ancestry. Moreover, we see marked differences between males and females, most notably at the CHRNA3 locus, and variants associated with risk of coronary artery disease and cholesterol levels. Beyond our findings, the analysis serves as a proof of principle for how upcoming biomedical datasets can be used to learn about selection effects in contemporary humans.
]]></description>
<dc:creator>Mostafavi, H.</dc:creator>
<dc:creator>Berisa, T.</dc:creator>
<dc:creator>Przeworski, M.</dc:creator>
<dc:creator>Pickrell, J. K.</dc:creator>
<dc:date>2016-11-07</dc:date>
<dc:identifier>doi:10.1101/085969</dc:identifier>
<dc:title><![CDATA[Identifying genetic variants that affect viability in large cohorts]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2016-11-07</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/052308v1?rss=1">
<title>
<![CDATA[
Reference-based phasing using the Haplotype Reference Consortium panel 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/052308v1?rss=1"
</link>
<description><![CDATA[
Haplotype phasing is a fundamental problem in medical and population genetics. Phasing is generally performed via statistical phasing within a genotyped cohort, an approach that can attain high accuracy in very large cohorts but attains lower accuracy in smaller cohorts. Here, we instead explore the paradigm of reference-based phasing. We introduce a new phasing algorithm, Eagle2, that attains high accuracy across a broad range of cohort sizes by efficiently leveraging information from large external reference panels (such as the Haplotype Reference Consortium, HRC) using a new data structure based on the positional BurrowsWheeler transform. We demonstrate that Eagle2 attains a {approx}20x speedup and {approx}10% increase in accuracy compared to reference-based phasing using SHAPEIT2. On European-ancestry samples, Eagle2 with the HRC panel achieves >2x the accuracy of 1000 Genomes-based phasing. Eagle2 is open source and freely available for HRC-based phasing via the Sanger Imputation Service and the Michigan Imputation Server.
]]></description>
<dc:creator>Po-Ru Loh</dc:creator>
<dc:creator>Petr Danecek</dc:creator>
<dc:creator>Pier Francesco Palamara</dc:creator>
<dc:creator>Christian Fuchsberger</dc:creator>
<dc:creator>Yakir A Reshef</dc:creator>
<dc:creator>Hilary K Finucane</dc:creator>
<dc:creator>Sebastian Schoenherr</dc:creator>
<dc:creator>Lukas Forer</dc:creator>
<dc:creator>Shane McCarthy</dc:creator>
<dc:creator>Goncalo R Abecasis</dc:creator>
<dc:creator>Richard Durbin</dc:creator>
<dc:creator>Alkes L Price</dc:creator>
<dc:creator></dc:creator>
<dc:date>2016-05-10</dc:date>
<dc:identifier>doi:10.1101/052308</dc:identifier>
<dc:title><![CDATA[Reference-based phasing using the Haplotype Reference Consortium panel]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2016-05-10</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/128330v1?rss=1">
<title>
<![CDATA[
Red Blood Cell Distribution Width: genetic evidence for aging pathways in 116,666 volunteers 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/128330v1?rss=1"
</link>
<description><![CDATA[
Variability in red blood cell volumes (distribution width, RDW) increases with age and is strongly predictive of mortality, incident coronary heart disease and cancer. We investigated inherited genetic variation associated with RDW in 166,666 UK Biobank human volunteers.nnA large proportion RDW is explained by genetic variants (29%), especially in the older group (60+ year olds, 33.8%, <50 year olds, 28.4%). RDW was associated with 194 independent genetic signals; 71 are known for conditions including autoimmune disease, certain cancers, BMI, Alzheimers disease, longevity, age at menopause, bone density, myositis, Parkinsons disease, and age-related macular degeneration. Pathways analysis showed enrichment for telomere maintenance, ribosomal RNA and apoptosis.nnAlthough increased RDW is predictive of cardiovascular outcomes, this was not explained by known CVD or related lipid genetic risks. The predictive value of RDW for a range of negative health outcomes may in part be due to variants influencing fundamental pathways of aging.
]]></description>
<dc:creator>Pilling, L. C.</dc:creator>
<dc:creator>Atkins, J. L.</dc:creator>
<dc:creator>Duff, M. O.</dc:creator>
<dc:creator>Beaumont, R. N.</dc:creator>
<dc:creator>Jones, S. E.</dc:creator>
<dc:creator>Tyrrell, J.</dc:creator>
<dc:creator>Kuo, C.-L.</dc:creator>
<dc:creator>Ruth, K. S.</dc:creator>
<dc:creator>Tuke, M. A.</dc:creator>
<dc:creator>Yaghootkar, H.</dc:creator>
<dc:creator>Freathy, R. M.</dc:creator>
<dc:creator>Wood, A. R.</dc:creator>
<dc:creator>Murray, A.</dc:creator>
<dc:creator>Weedon, M. N.</dc:creator>
<dc:creator>Harries, L. W.</dc:creator>
<dc:creator>Kuchel, G. A.</dc:creator>
<dc:creator>Ferrucci, L.</dc:creator>
<dc:creator>Frayling, T. M.</dc:creator>
<dc:creator>Melzer, D.</dc:creator>
<dc:date>2017-04-18</dc:date>
<dc:identifier>doi:10.1101/128330</dc:identifier>
<dc:title><![CDATA[Red Blood Cell Distribution Width: genetic evidence for aging pathways in 116,666 volunteers]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-04-18</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/176511v1?rss=1">
<title>
<![CDATA[
Ninety-nine independent genetic loci influencing general cognitive function include genes associated with brain health and structure (N = 280,360) 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/176511v1?rss=1"
</link>
<description><![CDATA[
General cognitive function is a prominent human trait associated with many important life outcomes1,2, including longevity3. The substantial heritability of general cognitive function is known to be polygenic, but it has had little explication in terms of the contributing genetic variants4,5,6. Here, we combined cognitive and genetic data from the CHARGE and COGENT consortia, and UK Biobank (total N=280,360; age range = 16 to 102). We found 9,714 genome-wide significant SNPs (P<5 x 10-8) in 99 independent loci. Most showed clear evidence of functional importance. Among many novel genes associated with general cognitive function were SGCZ, ATXN1, MAPT, AUTS2, and P2RY6. Within the novel genetic loci were variants associated with neurodegenerative disorders, neurodevelopmental disorders, physical and psychiatric illnesses, brain structure, and BMI. Gene-based analyses found 536 genes significantly associated with general cognitive function; many were highly expressed in the brain, and associated with neurogenesis and dendrite gene sets. Genetic association results predicted up to 4% of general cognitive function variance in independent samples. There was significant genetic overlap between general cognitive function and information processing speed, as well as many health variables including longevity.
]]></description>
<dc:creator>Davies, G.</dc:creator>
<dc:creator>Lam, M.</dc:creator>
<dc:creator>Harris, S. E.</dc:creator>
<dc:creator>Trampush, J.</dc:creator>
<dc:creator>Luciano, M.</dc:creator>
<dc:creator>Hill, W. D.</dc:creator>
<dc:creator>Hagenaars, S. P.</dc:creator>
<dc:creator>Ritchie, S. J.</dc:creator>
<dc:creator>Marioni, R. E.</dc:creator>
<dc:creator>Fawns-Ritchie, C.</dc:creator>
<dc:creator>Liewald, D. C.</dc:creator>
<dc:creator>Okely, J.</dc:creator>
<dc:creator>Ahola-Olli, A.</dc:creator>
<dc:creator>Barnes, C. L. K.</dc:creator>
<dc:creator>Bertram, L.</dc:creator>
<dc:creator>Bis, J. C.</dc:creator>
<dc:creator>Burdick, K. E.</dc:creator>
<dc:creator>Christoforou, A.</dc:creator>
<dc:creator>DeRosse, P.</dc:creator>
<dc:creator>Djurovic, S.</dc:creator>
<dc:creator>Espeseth, T.</dc:creator>
<dc:creator>Giakoumaki, S.</dc:creator>
<dc:creator>Giddaluru, S.</dc:creator>
<dc:creator>Gustavson, D. E.</dc:creator>
<dc:creator>Hayward, C.</dc:creator>
<dc:creator>Hofer, E.</dc:creator>
<dc:creator>Ikram, M. A.</dc:creator>
<dc:creator>Karlsson, R.</dc:creator>
<dc:creator>Knowles, E.</dc:creator>
<dc:creator>Lahti, J.</dc:creator>
<dc:creator>Leber, M.</dc:creator>
<dc:creator>Li, S.</dc:creator>
<dc:creator>Mather, K. A.</dc:creator>
<dc:creator>Melle, I.</dc:creator>
<dc:creator>Morris, D.</dc:creator>
<dc:creator>Oldmeadow, C.</dc:creator>
<dc:creator>Palviainen, T.</dc:creator>
<dc:creator>Payton, A.</dc:creator>
<dc:creator>Pazoki, R.</dc:creator>
<dc:creator>Petrovic, K.</dc:creator>
<dc:creator>Reynolds, C. A.</dc:creator>
<dc:creator>Sargurupremraj, M.</dc:creator>
<dc:creator>Scholz</dc:creator>
<dc:date>2017-08-17</dc:date>
<dc:identifier>doi:10.1101/176511</dc:identifier>
<dc:title><![CDATA[Ninety-nine independent genetic loci influencing general cognitive function include genes associated with brain health and structure (N = 280,360)]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-08-17</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/079707v1?rss=1">
<title>
<![CDATA[
Collider Scope: How selection bias can induce spurious associations 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/079707v1?rss=1"
</link>
<description><![CDATA[
Large-scale cross-sectional and cohort studies have transformed our understanding of the genetic and environmental determinants of health outcomes. However, the representativeness of these samples may be limited - either through selection into studies, or by attrition from studies over time. Here we explore the potential impact of this selection bias on results obtained from these studies, from the perspective that this amounts to conditioning on a collider (i.e., a form of collider bias). While it is acknowledged that selection bias will have a strong effect on representativeness and prevalence estimates, it is often assumed that it should not have a strong impact on estimates of associations. We argue that because selection can induce collider bias (which occurs when two variables independently influence a third variable, and that third variable is conditioned upon), selection can lead to substantially biased estimates of associations. In particular, selection related to phenotypes can bias associations with genetic variants associated with those phenotypes. In simulations, we show that even modest influences on selection into, or attrition from, a study can generate biased and potentially misleading estimates of both phenotypic and genotypic associations. Our results highlight the value of knowing which population your study sample is representative of. If the factors influencing selection and attrition are known, they can be adjusted for. For example, having DNA available on most participants in a birth cohort study offers the possibility of investigating the extent to which polygenic scores predict subsequent participation, which in turn would enable sensitivity analyses of the extent to which bias might distort estimates.nnKey MessagesSelection bias (including selective attrition) may limit the representativeness of large-scale cross-sectional and cohort studies.nnThis selection bias may induce collider bias (which occurs when two variables independently influence a third variable, and that variable is conditioned upon).nnThis may lead to substantially biased estimates of associations, including of genetic associations, even when selection / attrition is relatively modest.
]]></description>
<dc:creator>Marcus R Munafo</dc:creator>
<dc:creator>Kate Tilling</dc:creator>
<dc:creator>Amy E Taylor</dc:creator>
<dc:creator>David M Evans</dc:creator>
<dc:creator>George Davey Smith</dc:creator>
<dc:creator></dc:creator>
<dc:date>2016-10-07</dc:date>
<dc:identifier>doi:10.1101/079707</dc:identifier>
<dc:title><![CDATA[Collider Scope: How selection bias can induce spurious associations]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2016-10-07</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/204560v1?rss=1">
<title>
<![CDATA[
Evaluation and application of summary statistic imputation to discover new height-associated loci 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/204560v1?rss=1"
</link>
<description><![CDATA[
AbstractAs most of the heritability of complex traits is attributed to common and low frequency genetic variants, imputing them by combining genotyping chips and large sequenced reference panels is the most cost-effective approach to discover the genetic basis of these traits. Association summary statistics from genome-wide meta-analyses are available for hundreds of traits. Updating these to ever-increasing reference panels is very cumbersome as it requires reimputation of the genetic data, rerunning the association scan, and meta-analysing the results. A much more efficient method is to directly impute the summary statistics, termed as summary statistics imputation. Its performance relative to genotype imputation and practical utility has not yet been fully investigated. To this end, we compared the two approaches on real (genotyped and imputed) data from 120K samples from the UK Biobank and show that, while genotype imputation boasts a 2- to 5-fold lower root-mean-square error, summary statistics imputation better distinguishes true associations from null ones: We observed the largest differences in power for variants with low minor allele frequency and low imputation quality. For fixed false positive rates of 0.001, 0.01, 0.05, using summary statistics imputation yielded an increase in statistical power by 15, 10 and 3%, respectively. To test its capacity to discover novel associations, we applied summary statistics imputation to the GIANT height meta-analysis summary statistics covering HapMap variants, and identified 34 novel loci, 19 of which replicated using data in the UK Biobank. Additionally, we successfully replicated 55 out of the 111 variants published in an exome chip study. Our study demonstrates that summary statistics imputation is a very efficient and cost-effective way to identify and fine-map trait-associated loci. Moreover, the ability to impute summary statistics is important for follow-up analyses, such as Mendelian randomisation or LD-score regression.nnAuthor summaryGenome-wide association studies (GWASs) quantify the effect of genetic variants and traits, such as height. Such estimates are called association summary statistics and are typically publicly shared through publication. Typically, GWASs are carried out by genotyping ~ 500'000 SNVs for each individual which are then combined with sequenced reference panels to infer untyped SNVs in each individuals genome. This process of genotype imputation is resource intensive and can therefore be a limitation when combining many GWASs. An alternative approach is to bypass the use of individual data and directly impute summary statistics. In our work we compare the performance of summary statistics imputation to genotype imputation. Although we observe a 2- to 5-fold lower RMSE for genotype imputation compared to summary statistics imputation, summary statistics imputation better distinguishes true associations from null results. Furthermore, we demonstrate the potential of summary statistics imputation by presenting 34 novel height-associated loci, 19 of which were confirmed in UK Biobank. Our study demonstrates that given current reference panels, summary statistics imputation is a very efficient and cost-effective way to identify common or low-frequency trait-associated loci.
]]></description>
<dc:creator>Rüeger, S.</dc:creator>
<dc:creator>McDaid, A.</dc:creator>
<dc:creator>Kutalik, Z.</dc:creator>
<dc:date>2017-10-18</dc:date>
<dc:identifier>doi:10.1101/204560</dc:identifier>
<dc:title><![CDATA[Evaluation and application of summary statistic imputation to discover new height-associated loci]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-10-18</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/223578v1?rss=1">
<title>
<![CDATA[
Mutations in RPL3L and MYZAP increase risk of atrial fibrillation 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/223578v1?rss=1"
</link>
<description><![CDATA[
We performed a meta-analysis of genome-wide association studies on atrial fibrillation (AF) among 14,710 cases and 373,897 controls from Iceland and 14,792 cases and 393,863 controls from the UK Biobank, focusing on low frequency coding and splice mutations, with follow-up in samples from Norway and the US. We observed associations with two missense (OR=1.19 for both) and one splice-donor mutation (OR=1.52) in RPL3L, encoding a ribosomal protein primarily expressed in skeletal muscle and heart. Analysis of 167 RNA samples from the right atrium revealed that the splice donor mutation in RPL3L results in exon skipping. AF is the first disease associated with RPL3L and RPL3L is the first ribosomal gene implicated in AF. This finding is consistent with tissue specialization of ribosomal function. We also found an association with a missense variant in MYZAP (OR=1.37), encoding a component of the intercalated discs of cardiomyocytes, the organelle harbouring most of the mutated proteins involved in arrhythmogenic right ventricular cardiomyopathy. Both discoveries emphasize the close relationship between the mechanical and electrical function of the heart.
]]></description>
<dc:creator>Thorolfsdottir, R. B.</dc:creator>
<dc:creator>Sveinbjornsson, G.</dc:creator>
<dc:creator>Sulem, P.</dc:creator>
<dc:creator>Jonsson, S.</dc:creator>
<dc:creator>Halldorsson, G.</dc:creator>
<dc:creator>Melsted, P.</dc:creator>
<dc:creator>Ivarsdottir, E. V.</dc:creator>
<dc:creator>Davidsson, O. B.</dc:creator>
<dc:creator>Kristjansson, R. B.</dc:creator>
<dc:creator>Thorleifsson, G.</dc:creator>
<dc:creator>Helgadottir, A.</dc:creator>
<dc:creator>Gretarsdottir, S.</dc:creator>
<dc:creator>Norddahl, G.</dc:creator>
<dc:creator>Rajamani, S.</dc:creator>
<dc:creator>Torfason, B.</dc:creator>
<dc:creator>Valgardsson, A. S.</dc:creator>
<dc:creator>Sverrisson, J. T.</dc:creator>
<dc:creator>Tragante, V.</dc:creator>
<dc:creator>Asselbergs, F. W.</dc:creator>
<dc:creator>Roden, D. M.</dc:creator>
<dc:creator>Darbar, D.</dc:creator>
<dc:creator>Pedersen, T. R.</dc:creator>
<dc:creator>Sabatine, M. S.</dc:creator>
<dc:creator>Lochen, M.-L.</dc:creator>
<dc:creator>Halldorsson, B. V.</dc:creator>
<dc:creator>Jonsdottir, I.</dc:creator>
<dc:creator>Arnar, D. O.</dc:creator>
<dc:creator>Thorsteinsdottir, U.</dc:creator>
<dc:creator>Gudbjartsson, D. F.</dc:creator>
<dc:creator>Holm, H.</dc:creator>
<dc:creator>Stefansson, K.</dc:creator>
<dc:date>2017-11-21</dc:date>
<dc:identifier>doi:10.1101/223578</dc:identifier>
<dc:title><![CDATA[Mutations in RPL3L and MYZAP increase risk of atrial fibrillation]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-11-21</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/240283v1?rss=1">
<title>
<![CDATA[
Is a large eye size a risk factor for myopia? A Mendelian randomization study 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/240283v1?rss=1"
</link>
<description><![CDATA[
Myopia (nearsightedness) is an increasingly common cause of irreversible visual impairment. The ocular structures with greatest impact on refractive error are corneal curvature and axial length. Emmetropic eyes range in size within and across species, yet possess a balance between corneal curvature and axial length that is under genetic control. This scaling goes awry in myopia: 1 mm axial elongation is associated with ~3 Dioptres (D) myopia. Evidence that eye size prior to onset is a risk factor for myopia is conflicting. We applied Mendelian randomisation to test for a causal effect of eye size on refractive error. Genetic variants associated with corneal curvature identified in emmetropic eyes (22,180 individuals) were used as instrumental variables and tested for association with refractive error (139,697 individuals). A genetic risk score for the variants was tested for association with corneal curvature and axial length in an independent sample (315 emmetropes). The genetic risk score explained 2.3% (P=0.007) and 2.7% (P=0.002) of the variance in corneal curvature and axial length, respectively, in the independent sample, confirming these variants are predictive of eye size in emmetropes. The estimated causal effect of eye size on refractive error was + 1.41 D (95% CI. 0.65 to 2.16) less myopic refractive error per mm flatter cornea (P<0.001), corresponding to +0.48 D (95% CI. 0.22 to 0.73) more hypermetropic refractive error for an eye with a 1mm longer axial length. These results do not support the hypothesis that a larger eye size is a risk factor for myopia. We conclude the genetic determinants of normal eye size are not shared with those influencing susceptibility to myopia.
]]></description>
<dc:creator>Plotnikov, D.</dc:creator>
<dc:creator>Guggenheim, J.</dc:creator>
<dc:creator>The UK Biobank Eye and Vision Consortium,</dc:creator>
<dc:date>2017-12-29</dc:date>
<dc:identifier>doi:10.1101/240283</dc:identifier>
<dc:title><![CDATA[Is a large eye size a risk factor for myopia? A Mendelian randomization study]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-12-29</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/154609v1?rss=1">
<title>
<![CDATA[
Data Resource Profile: Generation Scotland Electronic Health Record 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/154609v1?rss=1"
</link>
<description><![CDATA[
This paper provides the first detailed demonstration of the research value of the Electronic Health Record (EHR) linked to research data in Generation Scotland Scottish Family Health Study (GS:SFHS) participants, together with how to access this data. The structured, coded variables in the routine biochemistry, prescribing and morbidity records in particular represent highly valuable phenotypic data for a genomics research resource. Access to a wealth of other specialized datasets including cancer, mental health and maternity inpatient information is also possible through the same straightforward and transparent application process. The Electronic Health Record linked dataset is a key component of GS:SFHS, a biobank conceived in 1999 for the purpose of studying the genetics of health areas of current and projected public health importance. Over 24,000 adults were recruited from 2006 to 2011, with broad and enduring written informed consent for biomedical research. Consent was obtained from 23,603 participants for GS:SFHS study data to be linked to their Scottish National Health Service (NHS) records, using their Community Health Index (CHI) number. This identifying number is used for NHS Scotland procedures (registrations, attendances, samples, prescribing and investigations) and allows healthcare records for individuals to be linked across time and location. Here, we describe the NHS EHR dataset on the sub-cohort of 20,032 GS:SFHS participants with consent and mechanism for record linkage plus extensive genetic data. Together with existing study phenotypes, including family history and environmental exposures such as smoking, the EHR is a rich resource of real world data that can be used in research to characterise the health trajectory of participants, available at low cost and a high degree of timeliness, matched to DNA, urine and serum samples and genome-wide genetic information.
]]></description>
<dc:creator>Kerr, S. M.</dc:creator>
<dc:creator>Campbell, A.</dc:creator>
<dc:creator>Marten, J.</dc:creator>
<dc:creator>Vitart, V.</dc:creator>
<dc:creator>McIntosh, A. M.</dc:creator>
<dc:creator>Porteous, D. J.</dc:creator>
<dc:creator>Hayward, C.</dc:creator>
<dc:date>2017-06-23</dc:date>
<dc:identifier>doi:10.1101/154609</dc:identifier>
<dc:title><![CDATA[Data Resource Profile: Generation Scotland Electronic Health Record]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-06-23</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/063636v1?rss=1">
<title>
<![CDATA[
Molecular genetic aetiology of general cognitive function is enriched in evolutionarily conserved regions 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/063636v1?rss=1"
</link>
<description><![CDATA[
Differences in general cognitive function have been shown to be partly heritable and to show genetic correlations with a several psychiatric and physical disease states. However, to date few single nucleotide polymorphisms (SNPs) have demonstrated genome-wide significance, hampering efforts aimed at determining which genetic variants are most important for cognitive function and which regions drive the genetic associations between cognitive function and disease states. Here, we combine multiple large genome-wide association study (GWAS) data sets, from the CHARGE cognitive consortium and UK Biobank, to partition the genome into 52 functional annotations and an additional 10 annotations describing tissue-specific histone marks. Using stratified linkage disequilibrium score regression we show that, in two measures of cognitive function, SNPs associated with cognitive function cluster in regions of the genome that are under evolutionary negative selective pressure. These conserved regions contained ~2.6% of the SNPs from each GWAS but accounted for ~40% of the SNP-based heritability. The results suggest that the search for causal variants associated with cognitive function, and those variants that exert a pleiotropic effect between cognitive function and health, will be facilitated by examining these enriched regions.
]]></description>
<dc:creator>William David Hill</dc:creator>
<dc:creator>Gail Davies</dc:creator>
<dc:creator>Sarah E Harris</dc:creator>
<dc:creator>Saskia P Hagenaars</dc:creator>
<dc:creator>David Liewald</dc:creator>
<dc:creator>Lars Penke</dc:creator>
<dc:creator>Catherine R Gale</dc:creator>
<dc:creator>Ian Deary</dc:creator>
<dc:creator></dc:creator>
<dc:date>2016-07-13</dc:date>
<dc:identifier>doi:10.1101/063636</dc:identifier>
<dc:title><![CDATA[Molecular genetic aetiology of general cognitive function is enriched in evolutionarily conserved regions]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2016-07-13</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/130229v1?rss=1">
<title>
<![CDATA[
Genome-Wide Meta-Analyses Of Stratified Depression In Generation Scotland And UK Biobank 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/130229v1?rss=1"
</link>
<description><![CDATA[
Few replicable genetic associations for Major Depressive Disorder (MDD) have been identified. However recent studies of depression have identified common risk variants by using either a broader phenotype definition in very large samples, or by reducing the phenotypic and ancestral heterogeneity of MDD cases. Here, a range of genetic analyses were applied to data from two large British cohorts, Generation Scotland and UK Biobank, to ascertain whether it is more informative to maximize the sample size by using data from all available cases and controls, or to use a refined subset of the data - stratifying by MDD recurrence or sex. Meta-analysis of GWAS data in males from these two studies yielded one genome-wide significant locus on 3p22.3. Three associated genes within this region (CRTAP, GLB1, and TMPPE) were significantly associated in subsequent gene-based tests. Meta-analyzed MDD, recurrent MDD and female MDD were each genetically correlated with 6 of 200 health-correlated traits, namely neuroticism, depressive symptoms, subjective well-being, MDD, a cross-disorder phenotype and Bipolar Disorder. Meta-analyzed male MDD showed no statistically significant correlations with these traits after correction for multiple testing. Whilst stratified GWAS analysis revealed a genome-wide significant locus for male MDD, the lack of independent replication, the equivalent SNP-based heritability estimates and the consistent pattern of genetic correlation with other health-related traits suggests that phenotypic stratification in currently available sample sizes is currently weakly justified. Based upon existing studies and our findings, the strategy of maximizing sample sizes is likely to provide the greater gain.
]]></description>
<dc:creator>Hall, L. S.</dc:creator>
<dc:creator>Adams, M. J.</dc:creator>
<dc:creator>Arnau-Soler, A.</dc:creator>
<dc:creator>Clarke, T.-K.</dc:creator>
<dc:creator>Howard, D. M.</dc:creator>
<dc:creator>Zeng, Y.</dc:creator>
<dc:creator>Davies, G.</dc:creator>
<dc:creator>Hagenaars, S. P.</dc:creator>
<dc:creator>Fernandez-Pujals, A. M.</dc:creator>
<dc:creator>Gibson, J.</dc:creator>
<dc:creator>Wigmore, E. M.</dc:creator>
<dc:creator>Boutin, T. S.</dc:creator>
<dc:creator>Hayward, C.</dc:creator>
<dc:creator>Generation Scotland,</dc:creator>
<dc:creator>MDD Working Group of the Psychiatric Genomics Cons,</dc:creator>
<dc:creator>Porteous, D. J.</dc:creator>
<dc:creator>Deary, I. J.</dc:creator>
<dc:creator>Thomson, P. A.</dc:creator>
<dc:creator>Haley, C. S.</dc:creator>
<dc:creator>McIntosh, A. M.</dc:creator>
<dc:date>2017-04-24</dc:date>
<dc:identifier>doi:10.1101/130229</dc:identifier>
<dc:title><![CDATA[Genome-Wide Meta-Analyses Of Stratified Depression In Generation Scotland And UK Biobank]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-04-24</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/082792v1?rss=1">
<title>
<![CDATA[
Genome-wide association analyses of sleep disturbance traits identify new loci and highlight shared genetics with neuropsychiatric and metabolic traits 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/082792v1?rss=1"
</link>
<description><![CDATA[
Chronic sleep disturbances, associated with cardio-metabolic diseases, psychiatric disorders and all-cause mortality1,2, affect 25-30% of adults worldwide3. While environmental factors contribute importantly to self-reported habitual sleep duration and disruption, these traits are heritable4-9, and gene identification should improve our understanding of sleep function, mechanisms linking sleep to disease, and development of novel therapies. We report single and multi-trait genome-wide association analyses (GWAS) of self-reported sleep duration, insomnia symptoms including difficulty initiating and/or maintaining sleep, and excessive daytime sleepiness in the UK Biobank (n=112,586), with discovery of loci for insomnia symptoms (near MEIS1, TMEM132E, CYCL1, TGFBI in females and WDR27 in males), excessive daytime sleepiness (near AR/OPHN1) and a composite sleep trait (near INADL and HCRTR2), as well as replication of a locus for sleep duration (at PAX-8). Genetic correlation was observed between longer sleep duration and schizophrenia (rG=0.29, p=1.90x10-13) and between increased excessive daytime sleepiness and increased adiposity traits (BMI rG=0.20, p=3.12x10-09; waist circumference rG=0.20, p=2.12x10-07).
]]></description>
<dc:creator>Lane, J. M.</dc:creator>
<dc:creator>Liang, J.</dc:creator>
<dc:creator>Vlasac, I.</dc:creator>
<dc:creator>Anderson, S. G.</dc:creator>
<dc:creator>Bechtold, D. A.</dc:creator>
<dc:creator>Bowden, J.</dc:creator>
<dc:creator>Emsley, R.</dc:creator>
<dc:creator>Gill, S.</dc:creator>
<dc:creator>Little, M. A.</dc:creator>
<dc:creator>Luik, A. I.</dc:creator>
<dc:creator>Loudon, A.</dc:creator>
<dc:creator>Scheer, F. A. J. L.</dc:creator>
<dc:creator>Purcell, S. M.</dc:creator>
<dc:creator>Kyle, S. D.</dc:creator>
<dc:creator>Lawlor, D. A.</dc:creator>
<dc:creator>Zhu, X.</dc:creator>
<dc:creator>Redline, S.</dc:creator>
<dc:creator>Ray, D. W.</dc:creator>
<dc:creator>Rutter, M. K.</dc:creator>
<dc:creator>Saxena, R.</dc:creator>
<dc:date>2016-10-24</dc:date>
<dc:identifier>doi:10.1101/082792</dc:identifier>
<dc:title><![CDATA[Genome-wide association analyses of sleep disturbance traits identify new loci and highlight shared genetics with neuropsychiatric and metabolic traits]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2016-10-24</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/133322v1?rss=1">
<title>
<![CDATA[
Shared Genetic Architecture Of Asthma With Allergic Diseases: A Genome-wide Cross Trait Analysis Of 112,000 Individuals From UK Biobank 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/133322v1?rss=1"
</link>
<description><![CDATA[
Clinical and epidemiological data suggest that asthma and allergic diseases are associated. And may share a common genetic etiology. We analyzed genome-wide single-nucleotide polymorphism (SNP) data for asthma and allergic diseases in 35,783 cases and 76,768 controls of European ancestry from the UK Biobank. Two publicly available independent genome wide association studies (GWAS) were used for replication. We have found a strong genome-wide genetic correlation between asthma and allergic diseases (rg = 0.75, P = 6.84x10-62). Cross trait analysis identified 38 genome-wide significant loci, including novel loci such as D2HGDH and GAL2ST2. Computational analysis showed that shared genetic loci are enriched in immune/inflammatory systems and tissues with epithelium cells. Our work identifies common genetic architectures shared between asthma and allergy and will help to advance our understanding of the molecular mechanisms underlying co-morbid asthma and allergic diseases.
]]></description>
<dc:creator>Zhu, Z.</dc:creator>
<dc:creator>Lee, P. H.</dc:creator>
<dc:creator>Chaffin, M. D.</dc:creator>
<dc:creator>Chung, W.</dc:creator>
<dc:creator>Loh, P.-R.</dc:creator>
<dc:creator>Lu, Q.</dc:creator>
<dc:creator>Christiani, D. C.</dc:creator>
<dc:creator>Liang, L.</dc:creator>
<dc:date>2017-05-26</dc:date>
<dc:identifier>doi:10.1101/133322</dc:identifier>
<dc:title><![CDATA[Shared Genetic Architecture Of Asthma With Allergic Diseases: A Genome-wide Cross Trait Analysis Of 112,000 Individuals From UK Biobank]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-05-26</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/070177v1?rss=1">
<title>
<![CDATA[
Phenome-wide Heritability Analysis of the UK Biobank 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/070177v1?rss=1"
</link>
<description><![CDATA[
Heritability estimation provides important information about the relative contribution of genetic and environmental factors to phenotypic variation, and provides an upper bound for the utility of genetic risk prediction models. Recent technological and statistical advances have enabled the estimation of additive heritability attributable to common genetic variants (SNP heritability) across a broad phenotypic spectrum. However, assessing the comparative heritability of multiple traits estimated in different cohorts may be misleading due to the population-specific nature of heritability. Here we report the SNP heritability for 551 complex traits derived from the large-scale, population-based UK Biobank, comprising both quantitative phenotypes and disease codes, and examine the moderating effect of three major demographic variables (age, sex and socioeconomic status) on the heritability estimates. Our study represents the first comprehensive phenome-wide heritability analysis in the UK Biobank, and underscores the importance of considering population characteristics in comparing and interpreting heritability.
]]></description>
<dc:creator>Tian Ge</dc:creator>
<dc:creator>Chia-Yen Chen</dc:creator>
<dc:creator>Benjamin M. Neale</dc:creator>
<dc:creator>Mert R. Sabuncu</dc:creator>
<dc:creator>Jordan W. Smoller</dc:creator>
<dc:creator></dc:creator>
<dc:date>2016-08-18</dc:date>
<dc:identifier>doi:10.1101/070177</dc:identifier>
<dc:title><![CDATA[Phenome-wide Heritability Analysis of the UK Biobank]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2016-08-18</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/208496v1?rss=1">
<title>
<![CDATA[
Heritability of regional brain volumes in large-scale neuroimaging and genetic studies 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/208496v1?rss=1"
</link>
<description><![CDATA[
Brain genetics is an active research area. The degree to which genetic variants impact variations in brain structure and function remains largely unknown. We examined the heritability of regional brain volumes (p ~ 100) captured by single-nucleotide polymorphisms (SNPs) in UK Biobank (n ~ 9000). We found that regional brain volumes are highly heritable in this study population. We observed omni-genic impact across the genome as well as enrichment of SNPs in active chromatin regions. Principal components derived from regional volume data are also highly heritable, but the amount of variance in brain volume explained by the component did not seem to be related to its heritability. Heritability estimates vary substantially across large-scale functional networks and brain regions. The variation in heritability across regions was not related to measurement reliability. Heritability estimates exhibit a symmetric pattern across left and right hemispheres and are consistent in females and males. Our main findings in UK Biobank are consistent with those in Alzheimers Disease Neuroimaging Initiative (n ~ 1100), Philadelphia Neurodevelopmental Cohort (n ~ 600), and Pediatric Imaging, Neurocognition, and Genetics (n ~ 500) datasets, with more stable estimates in UK Biobank.
]]></description>
<dc:creator>Zhao, B.</dc:creator>
<dc:creator>Ibrahim, J. G.</dc:creator>
<dc:creator>Li, Y.</dc:creator>
<dc:creator>Li, T.</dc:creator>
<dc:creator>Wang, Y.</dc:creator>
<dc:creator>Shan, Y.</dc:creator>
<dc:creator>Zhu, Z.</dc:creator>
<dc:creator>Zhou, F.</dc:creator>
<dc:creator>Zhang, J.</dc:creator>
<dc:creator>Huang, C.</dc:creator>
<dc:creator>Liao, H.</dc:creator>
<dc:creator>Yang, L.</dc:creator>
<dc:creator>Thompson, P. M.</dc:creator>
<dc:creator>Zhu, H.</dc:creator>
<dc:date>2017-10-25</dc:date>
<dc:identifier>doi:10.1101/208496</dc:identifier>
<dc:title><![CDATA[Heritability of regional brain volumes in large-scale neuroimaging and genetic studies]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-10-25</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/034207v1?rss=1">
<title>
<![CDATA[
Maternal genome-wide association study identifies a fasting glucose variant associated with offspring birth weight 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/034207v1?rss=1"
</link>
<description><![CDATA[
Genome-wide association studies (GWAS) of birth weight have focused on fetal genetics, while relatively little is known about how maternal genetic variation influences fetal growth. We aimed to identify maternal genetic variants associated with birth weight that could highlight potentially relevant maternal determinants of fetal growth.nnWe meta-analysed GWAS data on up to 8.7 million SNPs in up to 86,577 women of European descent from the Early Growth Genetics (EGG) Consortium and the UK Biobank. We used structural equation modelling (SEM) and analyses of mother-child pairs to quantify the separate maternal and fetal genetic effects.nnMaternal SNPs at 10 loci (MTNR1B, HMGA2, SH2B3, KCNAB1, L3MBTL3, GCK, EBF1, TCF7L2, ACTL9 and CYP3A7) showed evidence of association with offspring birth weight at P<5x10-8. The SEM analyses showed at least 7 of the 10 associations were consistent with effects of the maternal genotype acting via the intrauterine environment, rather than via effects of shared alleles with the fetus. Variants, or correlated proxies, at many of the loci had been previously associated with adult traits, including fasting glucose (MTNR1B, GCK and TCF7L2) and sex hormone levels (CYP3A7), and one (EBF1) with gestational duration.nnThe identified associations indicate effects of maternal glucose, cytochrome P450 activity and gestational duration, and potential effects of maternal blood pressure and immune function on fetal growth. Further characterization of these associations, for example in mechanistic and causal analyses, will enhance understanding of the potentially modifiable maternal determinants of fetal growth, with the goal of reducing the morbidity and mortality associated with low and high birth weights.
]]></description>
<dc:creator>Bjarke Feenstra</dc:creator>
<dc:creator>Alana Cavadino</dc:creator>
<dc:creator>Jessica Tyrrell</dc:creator>
<dc:creator>George McMahon</dc:creator>
<dc:creator>Michael Nodzenski</dc:creator>
<dc:creator>Momoko Horikoshi</dc:creator>
<dc:creator>Frank Geller</dc:creator>
<dc:creator>Ronny Myhre</dc:creator>
<dc:creator>Rebecca Richmond</dc:creator>
<dc:creator>Lavinia Paternoster</dc:creator>
<dc:creator>Jonathan Bradfield</dc:creator>
<dc:creator>Eskil Kreiner-Moller</dc:creator>
<dc:creator>Ville Huikari</dc:creator>
<dc:creator>Sarah Metrustry</dc:creator>
<dc:creator>Kathryn Lunetta</dc:creator>
<dc:creator>Jodie Painter</dc:creator>
<dc:creator>Jouke-Jan Hottenga</dc:creator>
<dc:creator>Catherine Allard</dc:creator>
<dc:creator>Sheila Barton</dc:creator>
<dc:creator>Ana Espinosa</dc:creator>
<dc:creator>Julie Marsh</dc:creator>
<dc:creator>Catherine Potter</dc:creator>
<dc:creator>Wei Ang</dc:creator>
<dc:creator>Diane Berry</dc:creator>
<dc:creator>Luigi Bouchard</dc:creator>
<dc:creator>Shikta Das</dc:creator>
<dc:creator>Hakon Hakonarson</dc:creator>
<dc:creator>Jani Heikkinen</dc:creator>
<dc:creator>Berthold Hocher</dc:creator>
<dc:creator>Albert Hofman</dc:creator>
<dc:creator>Hazel Inskip</dc:creator>
<dc:creator>Manolis Kogevinas</dc:creator>
<dc:creator>Penelope Lind</dc:creator>
<dc:creator>Letizia Marullo</dc:creator>
<dc:creator>Sarah Medland</dc:creator>
<dc:creator>Jeffrey Murray</dc:creator>
<dc:creator>Ellen Nohr</dc:creator>
<dc:creator>Christoph Reichetzeder</dc:creator>
<dc:creator>Susan Ring</dc:creator>
<dc:creator>Loreto</dc:creator>
<dc:date>2015-12-11</dc:date>
<dc:identifier>doi:10.1101/034207</dc:identifier>
<dc:title><![CDATA[Maternal genome-wide association study identifies a fasting glucose variant associated with offspring birth weight]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2015-12-11</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/072306v1?rss=1">
<title>
<![CDATA[
Genetic Prediction of Male Pattern Baldness 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/072306v1?rss=1"
</link>
<description><![CDATA[
Male pattern baldness can have substantial psychosocial effects, and it has been phenotypically linked to adverse health outcomes such as prostate cancer and cardiovascular disease. We explored the genetic architecture of the trait using data from over 52,000 male participants of UK Biobank, aged 40-69 years. We identified over 250 independent novel genetic loci associated with severe hair loss. By developing a prediction algorithm based entirely on common genetic variants, and applying it to an independent sample, we could discriminate accurately (AUC = 0.82) between those with no hair loss from those with severe hair loss. The results of this study might help identify those at the greatest risk of hair loss and also potential genetic targets for intervention.
]]></description>
<dc:creator>Saskia P Hagenaars</dc:creator>
<dc:creator>William David Hill</dc:creator>
<dc:creator>Sarah E Harris</dc:creator>
<dc:creator>Stuart J Ritchie</dc:creator>
<dc:creator>Gail Davies</dc:creator>
<dc:creator>David Liewald</dc:creator>
<dc:creator>Catharine Gale</dc:creator>
<dc:creator>David John Porteous</dc:creator>
<dc:creator>Ian Deary</dc:creator>
<dc:creator>Riccardo Marioni</dc:creator>
<dc:creator></dc:creator>
<dc:date>2016-08-30</dc:date>
<dc:identifier>doi:10.1101/072306</dc:identifier>
<dc:title><![CDATA[Genetic Prediction of Male Pattern Baldness]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2016-08-30</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/233692v1?rss=1">
<title>
<![CDATA[
A frailty index for UK Biobank participants 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/233692v1?rss=1"
</link>
<description><![CDATA[
BackgroundFrailty indices (FIs) measure variation in health between aging individuals. Researching FIs in resources with large-scale genetic and phenotypic data will provide insights into the causes and consequences of frailty. Thus, we aimed to develop an FI using UK Biobank data, a cohort study of 500,000 middle-aged and older adults.nnMethodsAn FI was calculated using 49 self-reported questionnaire items on traits covering health, presence of diseases and disabilities, and mental wellbeing, according to standard protocol. We used multiple imputation to derive FI values for the entire eligible sample in the presence of missing item data (N =500,336). To validate the measure, we assessed associations of the FI with age, sex, and risk of all-cause mortality (follow-up [&le;] 9.7 years) using linear and Cox proportional hazards regression models.nnResultsMean FI in the cohort was 0.125 (standard deviation = 0.075), and there was a curvilinear trend towards higher values in older participants. FI values were also marginally higher on average in women than men. In survival models, 10% higher baseline frailty (i.e. a 0.1 FI increment) was associated with higher risk of death (hazard ratio (HR) = 1.65; 95% confidence interval: 1.62, 1.68). Associations were stronger in younger participants than in old, and in men compared to women (HRs: 1.72 vs. 1.56, respectively).nnConclusionsThe FI is a valid measure of frailty in UK Biobank. The cohorts data are open-access for researchers to use, and we provide script for deriving this tool to facilitate future studies on frailty.
]]></description>
<dc:creator>Williams, D. M.</dc:creator>
<dc:creator>Jylhava, J.</dc:creator>
<dc:creator>Pedersen, N. L.</dc:creator>
<dc:creator>Hagg, S.</dc:creator>
<dc:date>2017-12-13</dc:date>
<dc:identifier>doi:10.1101/233692</dc:identifier>
<dc:title><![CDATA[A frailty index for UK Biobank participants]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-12-13</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/178806v1?rss=1">
<title>
<![CDATA[
The genetic basis of human brain structure and function: 1,262 genome-wide associations found from 3,144 GWAS of multimodal brain imaging phenotypes from 9,707 UK Biobank participants 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/178806v1?rss=1"
</link>
<description><![CDATA[
The genetic basis of brain structure and function is largely unknown. We carried out genome-wide association studies of 3,144 distinct functional and structural brain imaging derived phenotypes in UK Biobank (discovery dataset 8,428 subjects). We show that many of these phenotypes are heritable. We identify 148 clusters of SNP-imaging associations with lead SNPs that replicate at p<0.05, when we would expect 21 to replicate by chance. Notable significant and interpretable associations include: iron transport and storage genes, related to changes in T2* in subcortical regions; extracellular matrix and the epidermal growth factor genes, associated with white matter micro-structure and lesion volume; genes regulating mid-line axon guidance development associated with pontine crossing tract organisation; and overall 17 genes involved in development, pathway signalling and plasticity. Our results provide new insight into the genetic architecture of the brain with relevance to complex neurological and psychiatric disorders, as well as brain development and aging. The full set of results is available on the interactive Oxford Brain Imaging Genetics (BIG) web browser.
]]></description>
<dc:creator>Elliott, L.</dc:creator>
<dc:creator>Sharp, K.</dc:creator>
<dc:creator>Alfaro-Almagro, F.</dc:creator>
<dc:creator>Douaud, G.</dc:creator>
<dc:creator>Miller, K.</dc:creator>
<dc:creator>Marchini, J.</dc:creator>
<dc:creator>Smith, S.</dc:creator>
<dc:date>2017-08-21</dc:date>
<dc:identifier>doi:10.1101/178806</dc:identifier>
<dc:title><![CDATA[The genetic basis of human brain structure and function: 1,262 genome-wide associations found from 3,144 GWAS of multimodal brain imaging phenotypes from 9,707 UK Biobank participants]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-08-21</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/074781v1?rss=1">
<title>
<![CDATA[
Quantifying the extent to which index event biases influence large genetic association studies 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/074781v1?rss=1"
</link>
<description><![CDATA[
As genetic association studies increase in size to 100,000s of individuals, subtle biases may influence conclusions. One possible bias is "index event bias" (IEB), also called "collider bias", caused by the stratification by, or enrichment for, disease status when testing associations between gene variants and a disease-associated trait. We first provided a statistical framework for quantifying IEB then identified real examples of IEB in a range of study and analytical designs. We observed evidence of biased associations for some disease alleles and genetic risk scores, even in population-based studies. For example, a genetic risk score consisting of type 2 diabetes variants was associated with lower BMI in 113,203 type 2 diabetes controls from the population based UK Biobank study (-0.010 SDs BMI per allele, P=5E-4), entirely driven by IEB. Three of 11 individual type 2 diabetes risk alleles, and 10 of 25 hypertension alleles were associated with lower BMI at p<0.05 in UK Biobank when analyzing disease free individuals only, of which six hypertension alleles remained associated at p<0.05 after correction for IEB. Our analysis suggested that the associations between CCND2 and TCF7L2 diabetes risk alleles and BMI could (at least partially) be explained by IEB. Variants remaining associated after correction may be pleiotropic and include those in CYP17A1 (allele associated with hypertension risk and lower BMI). In conclusion, IEB may result in false positive or negative associations in very large studies stratified or strongly enriched for/against disease cases.
]]></description>
<dc:creator>Hanieh Yaghootkar</dc:creator>
<dc:creator>Michael Bancks</dc:creator>
<dc:creator>Sam Jones</dc:creator>
<dc:creator>Aaron McDaid</dc:creator>
<dc:creator>Robin Beaumont</dc:creator>
<dc:creator>Louise Donnelly</dc:creator>
<dc:creator>Andrew Wood</dc:creator>
<dc:creator>Archie Campbell</dc:creator>
<dc:creator>Jessica Tyrrell</dc:creator>
<dc:creator>Lynne Hocking</dc:creator>
<dc:creator>Marcus Tuke</dc:creator>
<dc:creator>Katherine Ruth</dc:creator>
<dc:creator>Ewan Pearson</dc:creator>
<dc:creator>Anna Murray</dc:creator>
<dc:creator>Rachel Freathy</dc:creator>
<dc:creator>Patricia Munroe</dc:creator>
<dc:creator>Caroline Hayward</dc:creator>
<dc:creator>Colin Palmer</dc:creator>
<dc:creator>Michael Weedon</dc:creator>
<dc:creator>James Pankow</dc:creator>
<dc:creator>Timothy Frayling</dc:creator>
<dc:creator>Zoltan Kutalik</dc:creator>
<dc:creator></dc:creator>
<dc:date>2016-09-12</dc:date>
<dc:identifier>doi:10.1101/074781</dc:identifier>
<dc:title><![CDATA[Quantifying the extent to which index event biases influence large genetic association studies]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2016-09-12</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/179317v1?rss=1">
<title>
<![CDATA[
Genome-wide association study of habitual physical activity in over 277,000 UK Biobank participants identifies novel variants and genetic correlations with chronotype and obesity-related traits. 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/179317v1?rss=1"
</link>
<description><![CDATA[
Background/ObjectivesPhysical activity (PA) protects against a wide range of diseases. Engagement in habitual PA has been shown to be heritable, motivating the search for specific genetic variants that may ultimately inform efforts to promote PA and target the best type of PA for each individual.nnSubjects/MethodsWe used data from the UK Biobank to perform the largest genome-wide association study of PA to date, using three measures based on self-report (n=277,656) and two measures based on wrist-worn accelerometry data (n=67,808). We examined genetic correlations of PA with other traits and diseases, as well as tissue-specific gene expression patterns. With data from the Atherosclerosis Risk in Communities (ARIC; n=8,556) study, we performed a meta-analysis of our top hits for moderate-to-vigorous PA (MVPA).nnResultsWe identified 26 genome-wide loci across the five PA measures examined. Upon meta-analysis of the top hits for MVPA with results from the ARIC study, 8 of 10 remained significant at p<5x10-8. Interestingly, among these, the rs429358 variant in the APOE gene was the most strongly associated with MVPA. Variants in CADM2, a gene recently implicated in risk-taking behavior and other personality and cognitive traits, were found to be associated with regular engagement in strenuous sports or other exercises. We also identified thirteen loci consistently associated (p<0.005) with each of the five PA measures. We find genetic correlations of PA with educational attainment traits, chronotype, psychiatric traits, and obesity-related traits. Tissue enrichment analyses implicate the brain and pituitary gland as locations where PA-associated loci may exert their actions.nnConclusionsThese results provide new insight into the genetic basis of habitual PA, and the genetic links connecting PA with other traits and diseases.
]]></description>
<dc:creator>Klimentidis, Y. C.</dc:creator>
<dc:creator>Raichlen, D. A.</dc:creator>
<dc:creator>Bea, J.</dc:creator>
<dc:creator>Garcia, D. O.</dc:creator>
<dc:creator>Mandarino, L. J.</dc:creator>
<dc:creator>Alexander, G. A.</dc:creator>
<dc:creator>Chen, Z.</dc:creator>
<dc:creator>Going, S. B.</dc:creator>
<dc:date>2017-08-22</dc:date>
<dc:identifier>doi:10.1101/179317</dc:identifier>
<dc:title><![CDATA[Genome-wide association study of habitual physical activity in over 277,000 UK Biobank participants identifies novel variants and genetic correlations with chronotype and obesity-related traits.]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-08-22</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/184945v1?rss=1">
<title>
<![CDATA[
A transcriptome-wide association study identifies PALMD as a susceptibility gene for calcific aortic valve stenosis 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/184945v1?rss=1"
</link>
<description><![CDATA[
Calcific aortic valve stenosis (CAVS) is a common and life-threatening heart disease with no drug that can stop or delay its progression. Elucidating the genetic factors underpinning CAVS is an urgent priority to find new therapeutic targets1. Major landmarks in genetics of CAVS include the discoveries of NOTCH12 and LPA3. However, genetic variants in these genes accounted for a small number of cases and low population-attributable risk. Here we mapped a new susceptibility locus for CAVS on chromosome 1p21.2 and identified PALMD (palmdelphin) as the causal gene. PALMD was revealed using a transcriptome-wide association study (TWAS)4, which combines a genome-wide association study (GWAS) of 1,009 cases and 1,017 ethnically-matched controls with the first large-scale expression quantitative trait loci (eQTL) mapping study on human aortic valve tissues (n=233). The CAVS risk alleles and increasing disease severity were both associated with lowered mRNA expression levels of PALMD in valve tissues. The top variant explained up to 12.5% of the population-attributable risk and showed similar effect and strong association with CAVS (P=1.53 x 10-10) in UK Biobank comparing 1,391 cases and 352,195 controls. The identification of PALMD as a susceptibility gene for CAVS provides new insights about the genetic nature of this disease and opens new avenues to investigate its etiology and develop much-needed therapeutic options.
]]></description>
<dc:creator>Theriault, S.</dc:creator>
<dc:creator>Gaudreault, N.</dc:creator>
<dc:creator>Lamontagne, M.</dc:creator>
<dc:creator>Messika-Zeitoun, D.</dc:creator>
<dc:creator>Clavel, M.-A.</dc:creator>
<dc:creator>Capoulade, R.</dc:creator>
<dc:creator>Dagenais, F.</dc:creator>
<dc:creator>Pibarot, P.</dc:creator>
<dc:creator>Mathieu, P.</dc:creator>
<dc:creator>Bosse, Y.</dc:creator>
<dc:date>2017-09-05</dc:date>
<dc:identifier>doi:10.1101/184945</dc:identifier>
<dc:title><![CDATA[A transcriptome-wide association study identifies PALMD as a susceptibility gene for calcific aortic valve stenosis]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-09-05</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/188086v1?rss=1">
<title>
<![CDATA[
Quantification of frequency-dependent genetic architectures and action of negative selection in 25 UK Biobank traits 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/188086v1?rss=1"
</link>
<description><![CDATA[
Understanding the role of rare variants is important in elucidating the genetic basis of human diseases and complex traits. It is widely believed that negative selection can cause rare variants to have larger per-allele effect sizes than common variants. Here, we develop a method to estimate the minor allele frequency (MAF) dependence of SNP effect sizes. We use a model in which per-allele effect sizes have variance proportional to [p(1-p)], where p is the MAF and negative values of  imply larger effect sizes for rare variants. We estimate  by maximizing its profile likelihood in a linear mixed model framework using imputed genotypes, including rare variants (MAF >0.07%). We applied this method to 25 UK Biobank diseases and complex traits (N = 113,851). All traits produced negative  estimates with 20 significantly negative, implying larger rare variant effect sizes. The inferred best-fit distribution of true  values across traits had mean -0.38 (s.e. 0.02) and standard deviation 0.08 (s.e. 0.03), with statistically significant heterogeneity across traits (P = 0.0014). Despite larger rare variant effect sizes, we show that for most traits analyzed, rare variants (MAF <1%) explain less than 10% of total SNP-heritability. Using evolutionary modeling and forward simulations, we validated the  model of MAF-dependent trait effects and estimated the level of coupling between fitness effects and trait effects. Based on this analysis an average genome-wide negative selection coefficient on the order of 10-4 or stronger is necessary to explain the  values that we inferred.
]]></description>
<dc:creator>Schoech, A.</dc:creator>
<dc:creator>Jordan, D.</dc:creator>
<dc:creator>Loh, P.-R.</dc:creator>
<dc:creator>Gazal, S.</dc:creator>
<dc:creator>O'Connor, L.</dc:creator>
<dc:creator>Balick, D. J.</dc:creator>
<dc:creator>Palamara, P. F.</dc:creator>
<dc:creator>Finucane, H.</dc:creator>
<dc:creator>Sunyaev, S. R.</dc:creator>
<dc:creator>Price, A. L.</dc:creator>
<dc:date>2017-09-13</dc:date>
<dc:identifier>doi:10.1101/188086</dc:identifier>
<dc:title><![CDATA[Quantification of frequency-dependent genetic architectures and action of negative selection in 25 UK Biobank traits]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-09-13</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/201020v1?rss=1">
<title>
<![CDATA[
Biological Insights Into Muscular Strength: Genetic Findings in the UK Biobank 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/201020v1?rss=1"
</link>
<description><![CDATA[
BackgroundHand grip strength, a simple indicator of muscular strength, has been associated with a range of health conditions, including fractures, disability, cardiovascular disease and premature death risk. Twin studies have suggested a high (50-60%) heritability, but genetic determinants are largely unknown.nnAimsIn this study, our aim was to study genetic variation associated with muscular strength in a large sample of 334,925 individuals of European descent from the UK Biobank, and to evaluate shared genetic aetiology with and causal effects of grip strength on physical and cognitive health.nnMethods and ResultsIn our discovery analysis of 223,315 individuals, we identified 101 loci associated with grip strength at genome-wide significance (P<5x10-8). Of these, 64 were associated (P<0.01 and consistent direction) also in the replication dataset (N=111,610). Many of the lead SNPs were located in or near genes known to have a function in developmental disorders (FTO, SLC39A8, TFAP2B, TGFA, CELF1, TCF4, BDNF, FOXP1, KIF1B, ANTXR2), and one of the most significant genes based on a gene-based analysis (ATP2A1) encodes SERCA1, the critical enzyme in calcium uptake to the sarcoplasmic reticulum, which plays a major role in muscle contraction and relaxation. Further, we demonstrated a significant enrichment of gene expression in brain-related transcripts among grip strength associations. Finally, we observed inverse genetic correlations of grip strength with cardiometabolic traits, and positive correlation with parents age of death and education; and showed that grip strength was causally related to fitness, physical activity and other indicators of frailty, including cognitive performance scores.nnConclusionsIn our study of over 330,000 individuals from the general population, the genetic findings for hand grip strength suggest an important role of the central nervous system in strength performance. Further, our results indicate that maintaining good muscular strength is important for physical and cognitive performance and healthy aging.
]]></description>
<dc:creator>Tikkanen, E.</dc:creator>
<dc:creator>Gustafsson, S.</dc:creator>
<dc:creator>Amar, D.</dc:creator>
<dc:creator>Shcherbina, A.</dc:creator>
<dc:creator>Waggott, D.</dc:creator>
<dc:creator>Ashley, E.</dc:creator>
<dc:creator>Ingelsson, E.</dc:creator>
<dc:date>2017-10-10</dc:date>
<dc:identifier>doi:10.1101/201020</dc:identifier>
<dc:title><![CDATA[Biological Insights Into Muscular Strength: Genetic Findings in the UK Biobank]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-10-10</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/072603v1?rss=1">
<title>
<![CDATA[
Pleiotropy-robust Mendelian Randomization 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/072603v1?rss=1"
</link>
<description><![CDATA[
BackgroundThe potential of Mendelian Randomization studies is rapidly expanding due to (i) the growing power of GWAS meta-analyses to detect genetic variants associated with several exposures, and (ii) the increasing availability of these genetic variants in large-scale surveys. However, without a proper biological understanding of the pleiotropic working of genetic variants, a fundamental assumption of Mendelian Randomization (the exclusion restriction) can always be contested.nnMethodsWe build upon and synthesize recent advances in the econometric literature on instrumental variables (IV) estimation that test and relax the exclusion restriction. Our Pleiotropy-robust Mendelian Randomization (PRMR) method first estimates the degree of pleiotropy, and in turn corrects for it. If a sample exists for which the genetic variants do not affect the exposure, and pleiotropic effects are homogenous, PRMR obtains unbiased estimates of causal effects in case of pleiotropy.nnResultsSimulations show that existing MR methods produce biased estimators for realistic forms of pleiotropy. Under the aforementioned assumptions, PRMR produces unbiased estimators. We illustrate the practical use of PRMR by estimating the causal effect of (i) cigarettes smoked per day on Body Mass Index (BMI); (ii) prostate cancer on self-reported health, and (iii) educational attainment on BMI in the UK Biobank data.nnConclusionsPRMR allows for instrumental variables that violate the exclusion restriction due to pleiotropy, and corrects for pleiotropy in the estimation of the causal effect. If the degree of pleiotropy is unknown, PRMR can still be used as a sensitivity analysis.nnKey messagesO_LIIf genetic variants have pleiotropic effects, causal estimates of Mendelian Randomization studies will be biased.nC_LIO_LIPleiotropy-robust Mendelian Randomization (PRMR) produces unbiased causal estimates in case (i) a subsample can be identified for which the genetic variants do not affect the exposure, and (ii) pleiotropic effects are homogenous.nC_LIO_LIIf such a subsample does not exist, PRMR can still routinely be reported as a sensitivity analysis in any MR analysis.nC_LIO_LIIf pleiotropic effects are not homogenous, PRMR can be used as an informal test to gauge the exclusion restriction.nC_LI
]]></description>
<dc:creator>Hans van Kippersluis</dc:creator>
<dc:creator>Cornelius A Rietveld</dc:creator>
<dc:creator></dc:creator>
<dc:date>2016-08-31</dc:date>
<dc:identifier>doi:10.1101/072603</dc:identifier>
<dc:title><![CDATA[Pleiotropy-robust Mendelian Randomization]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2016-08-31</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/194167v1?rss=1">
<title>
<![CDATA[
Genetic study links components of the autonomous nervous system to heart-rate profile during exercise 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/194167v1?rss=1"
</link>
<description><![CDATA[
Heart rate (HR) response to exercise, as defined by HR-increase upon exercise and HR-recovery after exercise, is an important predictor of mortality and believed to be modulated by the autonomic nervous system. However, the mechanistic basis underlying inter-individual differences remains to be elucidated. To investigate this, we performed a large-scale genome wide analysis of HR-increase and HR-recovery in 58,818 individuals. A total of 25 significant independent SNPs in 23 loci (P<8.3x10-9) were associated with HR-increase or HR-recovery, and 36 candidate causal genes were prioritized that were enriched for pathways related to neuron biology. There was no evidence of a causal relationship with mortality or cardiovascular diseases, however, a nominal association with parental lifespan was observed (5.5x10-4) that requires further study. In conclusion, our findings provide new biological and clinical insight into the mechanistic under-pinning of HR response to exercise, underscoring the role of the autonomous nervous system in HR-recovery.nnABBREVIATIONS
]]></description>
<dc:creator>Verweij, N.</dc:creator>
<dc:creator>van de Vegte, Y. J.</dc:creator>
<dc:creator>van der Harst, P.</dc:creator>
<dc:date>2017-10-04</dc:date>
<dc:identifier>doi:10.1101/194167</dc:identifier>
<dc:title><![CDATA[Genetic study links components of the autonomous nervous system to heart-rate profile during exercise]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-10-04</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/218875v1?rss=1">
<title>
<![CDATA[
Phenome-wide association studies (PheWAS) across large "real-world data" population cohorts support drug target validation 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/218875v1?rss=1"
</link>
<description><![CDATA[
Phenome-wide association studies (PheWAS), which assess whether a genetic variant is associated with multiple phenotypes across a phenotypic spectrum, have been proposed as a possible aid to drug development through elucidating mechanisms of action, identifying alternative indications, or predicting adverse drug events (ADEs). Here, we evaluate whether PheWAS can inform target validation during drug development. We selected 25 single nucleotide polymorphisms (SNPs) linked through genome-wide association studies (GWAS) to 19 candidate drug targets for common disease therapeutic indications. We independently interrogated these SNPs through PheWAS in four large "real-world data" cohorts (23andMe, UK Biobank, FINRISK, CHOP) for association with a total of 1,892 binary endpoints. We then conducted meta-analyses for 145 harmonized disease endpoints in up to 697,815 individuals and joined results with summary statistics from 57 published GWAS. Our analyses replicate 70% of known GWAS associations and identify 10 novel associations with study-wide significance after multiple test correction (P<1.8x10-6; out of 72 novel associations with FDR<0.1). By leveraging directionality and point estimate of the effect sizes, we describe new associations that may predict ADEs, e.g., acne, high cholesterol, gout and gallstones for rs738409 (p.I148M) in PNPLA3; or asthma for rs1990760 (p.T946A) in IFIH1. We further propose how quantitative estimates of genetic safety/efficacy profiles can be used to help prioritize candidate targets for a specific indication. Our results demonstrate PheWAS as a powerful addition to the toolkit for drug discovery.nnOne Sentence SummaryMatching genetics with phenotypes in 800,000 individuals predicts efficacy and on-target safety of future drugs.
]]></description>
<dc:creator>Diogo, D.</dc:creator>
<dc:creator>Tian, C.</dc:creator>
<dc:creator>Franklin, C.</dc:creator>
<dc:creator>Alanne-Kinnunen, M.</dc:creator>
<dc:creator>March, M.</dc:creator>
<dc:creator>Spencer, C.</dc:creator>
<dc:creator>Vangjeli, C.</dc:creator>
<dc:creator>Weale, M.</dc:creator>
<dc:creator>Mattsson, H.</dc:creator>
<dc:creator>Kilpelainen, E.</dc:creator>
<dc:creator>Sleiman, P.</dc:creator>
<dc:creator>Reilly, D.</dc:creator>
<dc:creator>McElwee, J.</dc:creator>
<dc:creator>Maranville, J.</dc:creator>
<dc:creator>Chatterjee, A.</dc:creator>
<dc:creator>Bhandari, A.</dc:creator>
<dc:creator>the 23andMe Research Team,</dc:creator>
<dc:creator>Reeve, M.-P.</dc:creator>
<dc:creator>Hutz, J.</dc:creator>
<dc:creator>Bing, N.</dc:creator>
<dc:creator>John, S.</dc:creator>
<dc:creator>MacArthur, D.</dc:creator>
<dc:creator>Salomaa, V.</dc:creator>
<dc:creator>Ripatti, S.</dc:creator>
<dc:creator>Hakonarson, H.</dc:creator>
<dc:creator>Daly, M.</dc:creator>
<dc:creator>Palotie, A.</dc:creator>
<dc:creator>Hinds, D.</dc:creator>
<dc:creator>Donnelly, P.</dc:creator>
<dc:creator>Fox, C.</dc:creator>
<dc:creator>Day-Williams, A.</dc:creator>
<dc:creator>Plenge, R.</dc:creator>
<dc:creator>Runz, H.</dc:creator>
<dc:date>2017-11-13</dc:date>
<dc:identifier>doi:10.1101/218875</dc:identifier>
<dc:title><![CDATA[Phenome-wide association studies (PheWAS) across large "real-world data" population cohorts support drug target validation]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-11-13</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/116707v1?rss=1">
<title>
<![CDATA[
Genome-wide association study of alcohol consumption and genetic overlap with other health-related traits in UK Biobank (N=112,117). 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/116707v1?rss=1"
</link>
<description><![CDATA[
Alcohol consumption has been linked to over 200 diseases and is responsible for over 5% of the global disease burden. Well known genetic variants in alcohol metabolizing genes, e.g. ALDH2, ADH1B, are strongly associated with alcohol consumption but have limited impact in European populations where they are found at low frequency. We performed a genome-wide association study (GWAS) of self-reported alcohol consumption in 112,117 individuals in the UK Biobank (UKB) sample of white British individuals. We report significant genome-wide associations at 8 independent loci. These include SNPs in alcohol metabolizing genes (ADH1B/ADH1C/ADH5) and 2 loci in KLB, a gene recently associated with alcohol consumption. We also identify SNPs at novel loci including GCKR, PXDN, CADM2 and TNFRSF11A. Gene-based analyses found significant associations with genes implicated in the neurobiology of substance use (CRHR1, DRD2), and genes previously associated with alcohol consumption (AUTS2). GCTA-GREML analyses found a significant SNP-based heritability of self-reported alcohol consumption of 13% (S.E.=0.01). Sex-specific analyses found largely overlapping GWAS loci and the genetic correlation between male and female alcohol consumption was 0.73 (S.E.=0.09, p-value = 1.37 x 10-16). Using LD score regression, genetic overlap was found between alcohol consumption and schizophrenia (rG=0.13, S.E=0.04), HDL cholesterol (rG=0.21, S.E=0.05), smoking (rG=0.49, S.E=0.06) and various anthropometric traits (e.g. Overweight, rG=-0.19, S.E.=0.05). This study replicates the association between alcohol consumption and alcohol metabolizing genes and KLB, and identifies 4 novel gene associations that should be the focus of future studies investigating the neurobiology of alcohol consumption.
]]></description>
<dc:creator>Clarke, T.-K.</dc:creator>
<dc:creator>Adams, M. J.</dc:creator>
<dc:creator>Davies, G.</dc:creator>
<dc:creator>Howard, D. M.</dc:creator>
<dc:creator>Hall, L. S.</dc:creator>
<dc:creator>Padmanabhan, S.</dc:creator>
<dc:creator>Murray, A. D.</dc:creator>
<dc:creator>Smith, B. H.</dc:creator>
<dc:creator>Campbell, A.</dc:creator>
<dc:creator>Hayward, C.</dc:creator>
<dc:creator>Porteous, D. J.</dc:creator>
<dc:creator>Deary, I. J.</dc:creator>
<dc:creator>McIntosh, A. M.</dc:creator>
<dc:date>2017-03-14</dc:date>
<dc:identifier>doi:10.1101/116707</dc:identifier>
<dc:title><![CDATA[Genome-wide association study of alcohol consumption and genetic overlap with other health-related traits in UK Biobank (N=112,117).]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-03-14</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/215301v1?rss=1">
<title>
<![CDATA[
Loss of function, missense, and intronic variants in NOTCH1 confer different risks for left ventricular outflow tract obstructive heart defects in two European cohorts 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/215301v1?rss=1"
</link>
<description><![CDATA[
Loss of function variants in NOTCH1 cause left ventricular outflow tract obstructive defects (LVOTO) in a small percentage of families. Clinical surveys report an increased prevalence of missense variants in NOTCH1 in family members of individuals with LVOTO and other types of congenital heart disease (CHD). However, the risk conferred by rare variants in NOTCH1 for LVOTO remains largely uncharacterized. In a cohort of 49 families affected by hypoplastic left heart syndrome, a severe form of LVOTO, we discovered predicted loss of function NOTCH1 variants in 6% of individuals. Rare missense variants were found in an additional 16% of families. To make a quantitative estimate of the genetic risk posed by variants in NOTCH1 for LVOTO, we studied associations of 400 coding and non-coding variants in NOTCH1 in 271 adult cases and 333,571 controls from the UK Biobank. Two rare intronic variants in strong linkage disequilibrium displayed significant association with risk for LVOTO (g.chr9:139427582C>T, Odds Ratio 16.9, p=3.12e-6; g.chr9:139435649C>T, Odds Ratio 19.6, p = 2.44e-6) amongst European-ancestry British individuals. This result was replicated in an independent analysis of 51 cases and 68,901 controls of non-European and mixed ancestry. We conclude that carrying rare predicted loss of function variants or either of two intronic variants in NOTCH1 confer significant risk for LVOTO. Our approach demonstrates the utility of population-based datasets in quantifying the specific risk of individual variants for disease related phenotypes.nnAuthor summaryCongenital heart defects are the most common class of birth defect and are present in 1% of live births. Although CHD cases are often clustered in families, and thus the causal variant(s) are seemingly inherited, finding genetic variants causing these defects has been challenging. With the knowledge that variation in the NOTCH1 gene previously has been associated with CHDs affecting the left side of the heart, our aim was to further investigate the role of different types of NOTCH1 variants in left sided CHDs in two cohorts - a cohort of Finnish families with severe lesions affecting the left side of the heart, and the UK Biobank population including individuals with less severe left-sided lesions such as bicuspid aortic valve, congenital aortic stenosis, and coarctation of the aorta. We found a causal loss-of-function NOTCH1 variant in 6% of the families in the Finnish cohort and in the UK Biobank cohort, we identified two rare variants in the non-coding region of NOTCH1, associated with severe left-sided CHDs. These findings support screening of NOTCH1 loss-of-function variants in patients with severe left sided congenital heart defects and suggests that non-coding region variants in NOTCH1 play a role in CHDs.
]]></description>
<dc:creator>Helle, E.</dc:creator>
<dc:creator>Cordova-Palomera, A.</dc:creator>
<dc:creator>Ojala, T.</dc:creator>
<dc:creator>Saha, P.</dc:creator>
<dc:creator>Potiny, P.</dc:creator>
<dc:creator>Gustafsson, S.</dc:creator>
<dc:creator>Ingelsson, E.</dc:creator>
<dc:creator>Bamshad, M.</dc:creator>
<dc:creator>Nickerson, D.</dc:creator>
<dc:creator>Chong, J. X.</dc:creator>
<dc:creator>University of Washington Center for Mendelian Geno,</dc:creator>
<dc:creator>Ashley, E.</dc:creator>
<dc:creator>Priest, J. R.</dc:creator>
<dc:date>2017-11-07</dc:date>
<dc:identifier>doi:10.1101/215301</dc:identifier>
<dc:title><![CDATA[Loss of function, missense, and intronic variants in NOTCH1 confer different risks for left ventricular outflow tract obstructive heart defects in two European cohorts]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-11-07</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/182717v1?rss=1">
<title>
<![CDATA[
Elucidating the genetic architecture of reproductive ageing in the Japanese population 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/182717v1?rss=1"
</link>
<description><![CDATA[
Population studies over the past decade have successfully elucidated the genetic architecture of reproductive ageing. However, those studies were largely limited to European ancestries, restricting the generalizability of the findings and overlooking possible key genes poorly captured by common European genetic variation. Here, in up to 67,029 women of Japanese ancestry, we report 26 loci (all P<5x10{macron}8) for puberty timing or age at menopause, representing the first loci for reproductive ageing in any non-European population. Highlighted genes for menopause include GNRH1, which supports a primary, rather than passive, role for hypothalamic-pituitary GnRH signalling in the timing of menopause. For puberty timing, we demonstrate an aetiological role for receptor-like protein tyrosine phosphatases by combining evidence across population genetics and pre- and peri-pubertal changes in hypothalamic gene expression in rodent and primate models. Furthermore, our findings demonstrate widespread differences in allele frequencies and effect estimates between Japanese and European populations, highlighting the benefits and challenges of large-scale trans-ethnic approaches.
]]></description>
<dc:creator>Horikoshi, M.</dc:creator>
<dc:creator>Day, F.</dc:creator>
<dc:creator>Kamatani, Y.</dc:creator>
<dc:creator>Hirata, M.</dc:creator>
<dc:creator>Akiyama, M.</dc:creator>
<dc:creator>Matsuda, K.</dc:creator>
<dc:creator>Wright, H.</dc:creator>
<dc:creator>Toro, C.</dc:creator>
<dc:creator>Ojeda, S.</dc:creator>
<dc:creator>Lomniczi, A.</dc:creator>
<dc:creator>Kubo, M.</dc:creator>
<dc:creator>Ong, K.</dc:creator>
<dc:creator>Perry, J.</dc:creator>
<dc:date>2017-08-30</dc:date>
<dc:identifier>doi:10.1101/182717</dc:identifier>
<dc:title><![CDATA[Elucidating the genetic architecture of reproductive ageing in the Japanese population]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-08-30</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/038620v1?rss=1">
<title>
<![CDATA[
Genome-wide association analysis identifies novel loci for chronotype in 100,420 individuals from the UKBiobank 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/038620v1?rss=1"
</link>
<description><![CDATA[
Our sleep timing preference, or chronotype, is a manifestation of our internal biological clock. Variation in chronotype has been linked to sleep disorders, cognitive and physical performance, and chronic disease. Here, we perform a genome-wide association study of self-reported chronotype within the UKBiobank cohort (n=100,420). We identify 12 new genetic loci that implicate known components of the circadian clock machinery and point to previously unstudied genetic variants and candidate genes that might modulate core circadian rhythms or light-sensing pathways. Pathway analyses highlight central nervous and ocular systems and fear-response related processes. Genetic correlation analysis suggests chronotype shares underlying genetic pathways with schizophrenia, educational attainment and possibly BMI. Further, Mendelian randomization suggests that evening chronotype relates to higher educational attainment. These results not only expand our knowledge of the circadian system in humans, but also expose the influence of circadian characteristics over human health and life-history variables such as educational attainment.
]]></description>
<dc:creator>Jacqueline M Lane</dc:creator>
<dc:creator>Irma Vlasac</dc:creator>
<dc:creator>Simon G Anderson</dc:creator>
<dc:creator>Simon Kyle</dc:creator>
<dc:creator>William G Dixon</dc:creator>
<dc:creator>David A Bechtold</dc:creator>
<dc:creator>Shubhroz Gill</dc:creator>
<dc:creator>Max A Little</dc:creator>
<dc:creator>Annemarie Luik</dc:creator>
<dc:creator>Andrew Loudon</dc:creator>
<dc:creator>Richard Emsley</dc:creator>
<dc:creator>Frank AJL Scheer</dc:creator>
<dc:creator>Deborah A Lawlor</dc:creator>
<dc:creator>Susan Redline</dc:creator>
<dc:creator>David W Ray</dc:creator>
<dc:creator>Martin K Rutter</dc:creator>
<dc:creator>Richa Saxena</dc:creator>
<dc:creator></dc:creator>
<dc:date>2016-02-02</dc:date>
<dc:identifier>doi:10.1101/038620</dc:identifier>
<dc:title><![CDATA[Genome-wide association analysis identifies novel loci for chronotype in 100,420 individuals from the UKBiobank]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2016-02-02</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/198234v1?rss=1">
<title>
<![CDATA[
Genetic analysis of over one million people identifies 535 novel loci for blood pressure. 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/198234v1?rss=1"
</link>
<description><![CDATA[
High blood pressure is the foremost heritable global risk factor for cardiovascular disease. We report the largest genetic association study of blood pressure traits to date (systolic, diastolic, pulse pressure) in over one million people of European ancestry. We identify 535 novel blood pressure loci that not only offer new biological insights into blood pressure regulation but also reveal shared loci influencing lifestyle exposures. Our findings offer the potential for a precision medicine strategy for future cardiovascular disease prevention.
]]></description>
<dc:creator>Evangelou, E.</dc:creator>
<dc:creator>Warren, H. R.</dc:creator>
<dc:creator>Mosen-Ansorena, D.</dc:creator>
<dc:creator>Mifsud, B.</dc:creator>
<dc:creator>Pazoki, R.</dc:creator>
<dc:creator>Gao, H.</dc:creator>
<dc:creator>Ntritsos, G.</dc:creator>
<dc:creator>Dimou, N.</dc:creator>
<dc:creator>Cabrera, C. P.</dc:creator>
<dc:creator>Karaman, I.</dc:creator>
<dc:creator>Ng, F. L.</dc:creator>
<dc:creator>Evangelou, M.</dc:creator>
<dc:creator>Witkowska, K.</dc:creator>
<dc:creator>Tzanis, E.</dc:creator>
<dc:creator>Hellwege, J. N.</dc:creator>
<dc:creator>Giri, A.</dc:creator>
<dc:creator>Velez Edwards, D. R.</dc:creator>
<dc:creator>Sun, Y. V.</dc:creator>
<dc:creator>Cho, K.</dc:creator>
<dc:creator>Gaziano, J. M.</dc:creator>
<dc:creator>Wilson, P. W. F.</dc:creator>
<dc:creator>Tsao, P. S.</dc:creator>
<dc:creator>Kovesdy, C. P.</dc:creator>
<dc:creator>Esko, T.</dc:creator>
<dc:creator>Magi, R.</dc:creator>
<dc:creator>Milani, L.</dc:creator>
<dc:creator>Almgren, P.</dc:creator>
<dc:creator>Boutin, T.</dc:creator>
<dc:creator>Debette, S.</dc:creator>
<dc:creator>Ding, J.</dc:creator>
<dc:creator>Giulianini, F.</dc:creator>
<dc:creator>Holliday, E. G.</dc:creator>
<dc:creator>Jackson, A. U.</dc:creator>
<dc:creator>Li-Gao, R.</dc:creator>
<dc:creator>Lin, W.-Y.</dc:creator>
<dc:creator>Luan, J.</dc:creator>
<dc:creator>Mangino, M.</dc:creator>
<dc:creator>Oldmeadow, C.</dc:creator>
<dc:creator>Prins, B.</dc:creator>
<dc:creator>Qian, Y.</dc:creator>
<dc:creator>Sargurupremraj, M.</dc:creator>
<dc:creator>Shah, N.</dc:creator>
<dc:creator>Surendran, P.</dc:creator>
<dc:creator>Theriault, S.</dc:creator>
<dc:creator>Verwe</dc:creator>
<dc:date>2017-10-11</dc:date>
<dc:identifier>doi:10.1101/198234</dc:identifier>
<dc:title><![CDATA[Genetic analysis of over one million people identifies 535 novel loci for blood pressure.]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-10-11</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/123729v1?rss=1">
<title>
<![CDATA[
Sex Differences In The Adult Human Brain: Evidence From 5,216 UK Biobank Participants 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/123729v1?rss=1"
</link>
<description><![CDATA[
Sex differences in the human brain are of interest, for example because of sex differences in the observed prevalence of psychiatric disorders and in some psychological traits. We report the largest single-sample study of structural and functional sex differences in the human brain (2,750 female, 2,466 male participants; 44-77 years). Males had higher volumes, surface areas, and white matter fractional anisotropy; females had thicker cortices and higher white matter tract complexity. There was considerable distributional overlap between the sexes. Subregional differences were not fully attributable to differences in total volume or height. There was generally greater male variance across structural measures. Functional connectome organization showed stronger connectivity for males in unimodal sensorimotor cortices, and stronger connectivity for females in the default mode network. This large-scale study provides a foundation for attempts to understand the causes and consequences of sex differences in adult brain structure and function.
]]></description>
<dc:creator>Ritchie, S. J.</dc:creator>
<dc:creator>Cox, S. R.</dc:creator>
<dc:creator>Shen, X.</dc:creator>
<dc:creator>Lombardo, M. V.</dc:creator>
<dc:creator>Reus, L. M.</dc:creator>
<dc:creator>Alloza, C.</dc:creator>
<dc:creator>Harris, M. A.</dc:creator>
<dc:creator>Alderson, H.</dc:creator>
<dc:creator>Hunter, S.</dc:creator>
<dc:creator>Neilson, E.</dc:creator>
<dc:creator>Liewald, D. C.</dc:creator>
<dc:creator>Auyeung, B.</dc:creator>
<dc:creator>Whalley, H. C.</dc:creator>
<dc:creator>Lawrie, S. M.</dc:creator>
<dc:creator>Gale, C. R.</dc:creator>
<dc:creator>Bastin, M. E.</dc:creator>
<dc:creator>McIntosh, A. M.</dc:creator>
<dc:creator>Deary, I. J.</dc:creator>
<dc:date>2017-04-04</dc:date>
<dc:identifier>doi:10.1101/123729</dc:identifier>
<dc:title><![CDATA[Sex Differences In The Adult Human Brain: Evidence From 5,216 UK Biobank Participants]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-04-04</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/189076v1?rss=1">
<title>
<![CDATA[
Systematic Mendelian randomization framework elucidates hundreds of genetic loci which may influence disease through changes in DNA methylation levels 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/189076v1?rss=1"
</link>
<description><![CDATA[
We have undertaken an extensive Mendelian randomization (MR) study using methylation quantitative trait loci (mQTL) as genetic instruments to assess the potential causal relationship between genetic variation, DNA methylation and 139 complex traits. Using two-sample MR, we observed 1,191 effects across 62 traits where genetic variants were associated with both proximal DNA methylation (i.e. cis-mQTL) and complex trait variation (P<1.39x10-08). Joint likelihood mapping provided evidence that the causal mQTL for 364 of these effects across 58 traits was also likely the causal variant for trait variation. These effects showed a high rate of replication in the UK Biobank dataset for 14 selected traits, as 121 of the attempted 129 effects replicated. Integrating expression quantitative trait loci (eQTL) data suggested that genetic variants responsible for 319 of the 364 mQTL effects also influence gene expression, which indicates a coordinated system of effects that are consistent with causality. CpG sites were enriched for histone mark peaks in tissue types relevant to their associated trait and implicated genes were enriched across relevant biological pathways. Though we are unable to distinguish mediation from horizontal pleiotropy in these analyses, our findings should prove valuable in identifying candidate loci for further evaluation and help develop mechanistic insight into the aetiology of complex disease.
]]></description>
<dc:creator>Richardson, T. G.</dc:creator>
<dc:creator>Haycock, P. C.</dc:creator>
<dc:creator>Zheng, J.</dc:creator>
<dc:creator>Timpson, N. J.</dc:creator>
<dc:creator>Gaunt, T. R.</dc:creator>
<dc:creator>Davey Smith, G.</dc:creator>
<dc:creator>Relton, C. L.</dc:creator>
<dc:creator>Hemani, G.</dc:creator>
<dc:date>2017-09-14</dc:date>
<dc:identifier>doi:10.1101/189076</dc:identifier>
<dc:title><![CDATA[Systematic Mendelian randomization framework elucidates hundreds of genetic loci which may influence disease through changes in DNA methylation levels]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-09-14</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/154013v1?rss=1">
<title>
<![CDATA[
The Genetic Epidemiology of Developmental Dysplasia of the Hip: A Genome-Wide Association Study Harnessing National Clinical Audit Data 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/154013v1?rss=1"
</link>
<description><![CDATA[
BackgroundDevelopmental dysplasia of the hip (DDH) is a common, heritable condition characterised by abnormal formation of the hip joint, but has a poorly understood genetic architecture due to small sample sizes. We apply a novel case-ascertainment approach using national clinical audit (NCA) data to conduct the largest DDH genome-wide association study (GWAS) to date, and replicate our findings in independent cohorts.nnMethodsWe used the English National Joint Registry (NJR) dataset to collect DNA and conducted a GWAS in 770 DDH cases and 3364 controls. We tested the variant most strongly associated with DDH in independent replication cohorts comprising 1129 patients and 4652 controls.nnResultsThe heritable component of DDH attributable to common variants was 55% and distributed similarly across autosomal and the X-chromosomes. Variation within the GDF5 gene promoter was strongly and reproducibly associated with DDH (rs143384, OR 1.44 [95% CI 1.34-1.56], p=3.55x10-22). Two further replicating loci showed suggestive association with DDH near NFIB (rs4740554, OR 1.30 [95% CI 1.16-1.45], p=4.44x10-6) and LOXL4 (rs4919218, 1.19 [1.10-1.28] p=4.38x10-6). Through gene-based enrichment we identify GDF5, UQCC1, MMP24, RETSAT and PDRG1 association with DDH (p<1.2x10-7). Using the UK Biobank and arcOGEN cohorts to generate polygenic risk scores we find that risk alleles for hip osteoarthritis explain <0.5% of the variance in DDH susceptibility.nnConclusionUsing the NJR as a proof-of-principle, we describe the genetic architecture of DDH and identify several candidate intervention loci and demonstrate a scalable recruitment strategy for genetic studies that is transferrable to other complex diseases.nnKey MessagesO_LIWe report the first genome-wide scan for DDH in a European population, and the first to use national clinical audit data for case-ascertainment in complex disease.nC_LIO_LIThe heritable component of DDH attributable to common variants is 55% and is distributed similarly across autosomal and the X-chromosomes.nC_LIO_LIVariation within the GDF5 gene promoter is strongly and reproducibly associated with DDH, with fine-mapping indicating rs143384 as the likely casual variant.nC_LIO_LIEnrichment analyses implicate GDF5, UQCC1, MMP24, RETSAT and PDRG1 as candidate targets for intervention in DDH.nC_LIO_LIDDH shares little common genetic aetiology with idiopathic osteoarthritis of the hip, despite sharing variation within the GDF5 promoter as a common risk factor.nC_LI
]]></description>
<dc:creator>Hatzikotoulas, K.</dc:creator>
<dc:creator>Roposch, A.</dc:creator>
<dc:creator>Shah, K.</dc:creator>
<dc:creator>Clark, M.</dc:creator>
<dc:creator>Bratherton, S.</dc:creator>
<dc:creator>Limbani, V.</dc:creator>
<dc:creator>Steinberg, J.</dc:creator>
<dc:creator>Zengini, E.</dc:creator>
<dc:creator>Warsame, K.</dc:creator>
<dc:creator>Ratnayake, M.</dc:creator>
<dc:creator>Tselepi, M.</dc:creator>
<dc:creator>Schwartzentruber, J.</dc:creator>
<dc:creator>Loughlin, J.</dc:creator>
<dc:creator>Eastwood, D.</dc:creator>
<dc:creator>Zeggini, E.</dc:creator>
<dc:creator>Wilkinson, J. M.</dc:creator>
<dc:date>2017-06-24</dc:date>
<dc:identifier>doi:10.1101/154013</dc:identifier>
<dc:title><![CDATA[The Genetic Epidemiology of Developmental Dysplasia of the Hip: A Genome-Wide Association Study Harnessing National Clinical Audit Data]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-06-24</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/110460v1?rss=1">
<title>
<![CDATA[
Improving data availability for brain image biobanking in healthy subjects: practice-based suggestions from an international multidisciplinary working group 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/110460v1?rss=1"
</link>
<description><![CDATA[
Brain imaging is now ubiquitous in clinical practice and research. The case for bringing together large amounts of image data from well-characterised healthy subjects and those with a range of common brain diseases across the life course is now compelling. This report follows a meeting of international experts from multiple disciplines, all interested in brain image biobanking. The meeting included neuroimaging experts (clinical and non-clinical), computer scientists, epidemiologists, clinicians, ethicists, and lawyers involved in creating brain image banks. The meeting followed a structured format to discuss current and emerging brain image banks; applications such as atlases; conceptual and statistical problems (e.g. defining  normality); legal, ethical and technological issues (e.g. consents, potential for data linkage, data security, harmonisation, data storage and enabling of research data sharing). We summarise the lessons learned from the experiences of a wide range of individual image banks, and provide practical recommendations to enhance creation, use and reuse of neuroimaging data. Our aim is to maximise the benefit of the image data, provided voluntarily by research participants and funded by many organisations, for human health. Our ultimate vision is of a federated network of brain image biobanks accessible for large studies of brain structure and function.
]]></description>
<dc:creator>Shenkin, S. D.</dc:creator>
<dc:creator>Pernet, C.</dc:creator>
<dc:creator>Nichols, T. E.</dc:creator>
<dc:creator>Poline, J.-B.</dc:creator>
<dc:creator>Matthews, P. M.</dc:creator>
<dc:creator>van der Lugt, A.</dc:creator>
<dc:creator>Mackay, C.</dc:creator>
<dc:creator>Linda, L.</dc:creator>
<dc:creator>Mazoyer, B.</dc:creator>
<dc:creator>Boardman, J. P.</dc:creator>
<dc:creator>Thompson, P. M.</dc:creator>
<dc:creator>Fox, N.</dc:creator>
<dc:creator>Marcus, D. S.</dc:creator>
<dc:creator>Sheikh, A.</dc:creator>
<dc:creator>Cox, S. R.</dc:creator>
<dc:creator>Anblagan, D.</dc:creator>
<dc:creator>Job, D. E.</dc:creator>
<dc:creator>Dickie, D. A.</dc:creator>
<dc:creator>Rodriguez, D.</dc:creator>
<dc:creator>Wardlaw, J. M.</dc:creator>
<dc:date>2017-02-27</dc:date>
<dc:identifier>doi:10.1101/110460</dc:identifier>
<dc:title><![CDATA[Improving data availability for brain image biobanking in healthy subjects: practice-based suggestions from an international multidisciplinary working group]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-02-27</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/191080v1?rss=1">
<title>
<![CDATA[
Testing the moderation of quantitative gene by environment interactions in unrelated individuals 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/191080v1?rss=1"
</link>
<description><![CDATA[
The environment can moderate the effect of genes - a phenomenon called gene-environment (GxE) interaction. There are two broad types of GxE modeled in human behavior - qualitative GxE, where the effects of individual genetic variants differ depending on some environmental moderator, and quantitative GxE, where the additive genetic variance changes as a function of an environmental moderator. Tests of both qualitative and quantitative GxE have traditionally relied on comparing the covariances between twins and close relatives, but recently there has been interest in testing such models on unrelated individuals measured on genomewide data. However, to date, there has been no ability to test quantitative GxE effects in unrelated individuals using genomewide data because standard software cannot solve nonlinear constraints. Here, we introduce a maximum likelihood approach with parallel constrained optimization to fit such models. We use simulation to estimate the accuracy, power, and type I error rates of our method and to gauge its computational performance, and then apply this method to IQ data measured on 40,172 individuals with whole-genome SNP data from the UK Biobank. We found that the additive genetic variation of IQ tagged by SNPs increases as socioeconomic status (SES) decreases, opposite the direction found by several twin studies conducted in the U.S. on adolescents, but consistent with several studies from Europe and Australia on adults.
]]></description>
<dc:creator>Tahmasbi, R.</dc:creator>
<dc:creator>Evans, L. M.</dc:creator>
<dc:creator>Turkheimer, E.</dc:creator>
<dc:creator>Keller, M. C.</dc:creator>
<dc:date>2017-09-19</dc:date>
<dc:identifier>doi:10.1101/191080</dc:identifier>
<dc:title><![CDATA[Testing the moderation of quantitative gene by environment interactions in unrelated individuals]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-09-19</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/213595v1?rss=1">
<title>
<![CDATA[
Genome-wide analysis yields new loci associating with aortic valve stenosis 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/213595v1?rss=1"
</link>
<description><![CDATA[
Aortic valve stenosis (AS) is the most common valvular heart disease, characterized by a thickened and calcified valve causing left ventricular outflow obstruction. Severe AS is a significant cause of morbidity and mortality, affecting approximately 5% of those over 70 years of age1,2,3. Little is known about the genetics of AS, although recently a variant at the LPA locus4 and a rare MYH6 missense variant were found to associate with AS5. We report a large genome-wide association study (GWAS) with a follow-up in up to 7,307 AS cases and 801,073 controls. We identified two new AS loci, on chromosome 1p21 near PALMD (rs7543130; OR=1.20, P=1.2x10-22) and on chromosome 2q22 in TEX41 (rs1830321; OR=1.15, P=1.8x10-13). Rs7543130 also associates with bicuspid aortic valve (BAV) (OR=1.28, P=6.6x10-10) and aortic root diameter (P=1.30x10-8) and rs1830321 associates with BAV (OR=1.12, P=5.3x10-3 and coronary artery disease (CAD) (OR=1.05, P=9.3x10-5). These results indicate that AS is partly rooted in the same processes as cardiac development and atherosclerosis.
]]></description>
<dc:creator>Helgadottir, A.</dc:creator>
<dc:creator>Thorleifsson, G.</dc:creator>
<dc:creator>Gretarsdottir, S.</dc:creator>
<dc:creator>Stefansson, O. A.</dc:creator>
<dc:creator>Tragante, V.</dc:creator>
<dc:creator>Thorolfsdottir, R. B.</dc:creator>
<dc:creator>Jonsdottir, I.</dc:creator>
<dc:creator>Bjornsson, T.</dc:creator>
<dc:creator>Steinthorsdottir, V.</dc:creator>
<dc:creator>Verweij, N.</dc:creator>
<dc:creator>Nielsen, J. B.</dc:creator>
<dc:creator>Zhou, W.</dc:creator>
<dc:creator>Folkersen, L.</dc:creator>
<dc:creator>Martinsson, A.</dc:creator>
<dc:creator>Heydarpour, M.</dc:creator>
<dc:creator>Prakash, S.</dc:creator>
<dc:creator>Oskarsson, G.</dc:creator>
<dc:creator>Gudbjartsson, T.</dc:creator>
<dc:creator>Geirsson, A.</dc:creator>
<dc:creator>Olafsson, I.</dc:creator>
<dc:creator>Sigurdsson, E. L.</dc:creator>
<dc:creator>Almgren, P.</dc:creator>
<dc:creator>Melander, O.</dc:creator>
<dc:creator>Franco-Cereceda, A.</dc:creator>
<dc:creator>Hamsten, A.</dc:creator>
<dc:creator>Fritsche, L.</dc:creator>
<dc:creator>Lin, M.</dc:creator>
<dc:creator>Yang, B.</dc:creator>
<dc:creator>Hornsby, W.</dc:creator>
<dc:creator>Guo, D.</dc:creator>
<dc:creator>Brummett, C. M.</dc:creator>
<dc:creator>Abecasis, G.</dc:creator>
<dc:creator>Mathis, M.</dc:creator>
<dc:creator>Milewicz, D.</dc:creator>
<dc:creator>Body, S. C.</dc:creator>
<dc:creator>Eriksson, P.</dc:creator>
<dc:creator>Willer, C. J.</dc:creator>
<dc:creator>Hveem, K.</dc:creator>
<dc:creator>Newton-Cheh, C.</dc:creator>
<dc:creator>Smith, J. G.</dc:creator>
<dc:creator>D</dc:creator>
<dc:date>2017-11-03</dc:date>
<dc:identifier>doi:10.1101/213595</dc:identifier>
<dc:title><![CDATA[Genome-wide analysis yields new loci associating with aortic valve stenosis]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-11-03</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/136192v1?rss=1">
<title>
<![CDATA[
Fitness, Physical Activity, And Cardiovascular Disease: Longitudinal And Genetic Analyses In The UK Biobank Study 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/136192v1?rss=1"
</link>
<description><![CDATA[
BackgroundExercise is inversely related with cardiovascular disease (CVD), but large-scale studies of incident CVD events are lacking. Moreover, little is known about genetic determinants of fitness and physical activity, and modifiable effects of exercise in individuals with elevated genetic risk of CVD. Finally, causal analyses of exercise traits are limited.nnMethodsWe estimated associations of grip strength, physical activity, and cardiorespiratory fitness with CVD and all-cause death in up to 502,635 individuals from the UK Biobank. We also examined these associations in individuals with different genetic burden on coronary heart disease (CHD) and atrial fibrillation (AF). Finally, we performed genome-wide association study (GWAS) of grip strength and physical activity, as well as Mendelian randomization analysis to assess the causal role of grip strength in CHD.nnFindingsGrip strength, physical activity, and cardiorespiratory fitness showed strong inverse associations with incident cardiovascular events and all-cause death (for composite CVD; HR, 0.78, 95% CI, 0.77-0.80; HR, 0.94, 95% CI, 0.93-0.95, and HR, 0.67, 95% CI, 0.63-0.71, per SD change, respectively). We observed stronger associations of grip strength with CHD and AF for individuals in the lowest tertile of genetic risk (Pinteraction = 0.006, Pinteraction = 0.03, respectively), but the inverse associations were present in each category of genetic risk. We report 27 novel genetic loci associated with grip strength and 2 loci with physical activity, with the strongest associations in FTO (rs56094641, P=3.8x10-24) and SMIM2 (rs9316077, P=1.4x10-8), respectively. By use of Mendelian randomization, we provide evidence that grip strength is causally related to CHD.nnInterpretationMaintaining physical strength is likely to prevent future cardiovascular events, also in individuals with elevated genetic risk for CVD.nnFundingNational Institutes of Health (1 R01 HL135313-01), Knut and Alice Wallenberg Foundation (2013.0126), and the Finnish Cultural Foundation.
]]></description>
<dc:creator>Tikkanen, E.</dc:creator>
<dc:creator>Gustafsson, S.</dc:creator>
<dc:creator>Ingelsson, E.</dc:creator>
<dc:date>2017-05-12</dc:date>
<dc:identifier>doi:10.1101/136192</dc:identifier>
<dc:title><![CDATA[Fitness, Physical Activity, And Cardiovascular Disease: Longitudinal And Genetic Analyses In The UK Biobank Study]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-05-12</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/115527v1?rss=1">
<title>
<![CDATA[
Comparison of methods that use whole genome data to estimate the heritability and genetic architecture of complex traits. 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/115527v1?rss=1"
</link>
<description><![CDATA[
Heritability, h2, is a foundational concept in genetics, critical to understanding the genetic basis of complex traits. Recently-developed methods that estimate heritability from genotyped SNPs, h2 SNP, explain substantially more genetic variance than genome-wide significant loci, but less than classical estimates from twins and families. However, h2SNP estimates have yet to be comprehensively compared under a range of genetic architectures, making it difficult to draw conclusions from sometimes conflicting published estimates. Here, we used thousands of real whole genome sequences to simulate realistic phenotypes under a variety of genetic architectures, including those from very rare causal variants. We compared the performance of ten methods across different types of genotypic data (commercial SNP array positions, whole genome sequence variants, and imputed variants) and under differing causal variant frequencies, levels of stratification, and relatedness thresholds. These results provide guidance in interpreting past results and choosing optimal approaches for future studies. We then chose two methods (GREML-MS and GREML-LDMS) that best estimated overall h2SNP and the causal variant frequency spectra to six phenotypes in the UK Biobank using imputed genome-wide variants. Our results suggest that as imputation reference panels become larger and more diverse, estimates of the frequency distribution of causal variants will become increasingly unbiased and the vast majority of trait narrow-sense heritability will be accounted for.
]]></description>
<dc:creator>Evans, L.</dc:creator>
<dc:creator>Tahmasbi, R.</dc:creator>
<dc:creator>Vrieze, S.</dc:creator>
<dc:creator>Abecasis, G.</dc:creator>
<dc:creator>Das, S.</dc:creator>
<dc:creator>Bjelland, D.</dc:creator>
<dc:creator>deCandia, T.</dc:creator>
<dc:creator>Haplotype Reference Consortium,</dc:creator>
<dc:creator>Goddard, M.</dc:creator>
<dc:creator>Nealie, B.</dc:creator>
<dc:creator>Yang, J.</dc:creator>
<dc:creator>Visscher, P.</dc:creator>
<dc:creator>Keller, M.</dc:creator>
<dc:date>2017-03-09</dc:date>
<dc:identifier>doi:10.1101/115527</dc:identifier>
<dc:title><![CDATA[Comparison of methods that use whole genome data to estimate the heritability and genetic architecture of complex traits.]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-03-09</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/107409v1?rss=1">
<title>
<![CDATA[
A machine-learning heuristic to improve gene score prediction of polygenic traitsShort title: Machine-learning boosted gene scores 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/107409v1?rss=1"
</link>
<description><![CDATA[
Machine-learning techniques have helped solve a broad range of prediction problems, yet are not widely used to build polygenic risk scores for the prediction of complex traits. We propose a novel heuristic based on machine-learning techniques (GraBLD) to boost the predictive performance of polygenic risk scores. Gradient boosted regression trees were first used to optimize the weights of SNPs included in the score, followed by a novel regional adjustment for linkage disequilibrium. A calibration set with sample size of ~200 individuals was sufficient for optimal performance. GraBLD yielded prediction R2 of 0.239 and 0.082 using GIANT summary association statistics for height and BMI in the UK Biobank study (N=130K; 1.98M SNPs), explaining 46.9% and 32.7% of the overall polygenic variance, respectively. For diabetes status, the area under the receiver operating characteristic curve was 0.602 in the UK Biobank study using summary-level association statistics from the DIAGRAM consortium. GraBLD outperformed other polygenic score heuristics for the prediction of height (p<2.2x10-16) and BMI (p<1.57x10-4), and was equivalent to LDpred for diabetes. Results were independently validated in the Health and Retirement Study (N=8,292; 688,398 SNPs). Our report demonstrates the use of machine-learning techniques, coupled with summary-level data from large genome-wide meta-analyses to improve the prediction of polygenic traits.
]]></description>
<dc:creator>Pare, G.</dc:creator>
<dc:creator>Mao, S.</dc:creator>
<dc:creator>Deng, W. Q.</dc:creator>
<dc:date>2017-02-09</dc:date>
<dc:identifier>doi:10.1101/107409</dc:identifier>
<dc:title><![CDATA[A machine-learning heuristic to improve gene score prediction of polygenic traitsShort title: Machine-learning boosted gene scores]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-02-09</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/190124v1?rss=1">
<title>
<![CDATA[
Accurate Genomic Prediction Of Human Height 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/190124v1?rss=1"
</link>
<description><![CDATA[
We construct genomic predictors for heritable and extremely complex human quantitative traits (height, heel bone density, and educational attainment) using modern methods in high dimensional statistics (i.e., machine learning). Replication tests show that these predictors capture, respectively, ~40, 20, and 9 percent of total variance for the three traits. For example, predicted heights correlate ~0.65 with actual height; actual heights of most individuals in validation samples are within a few cm of the prediction. The variance captured for height is comparable to the estimated SNP heritability from GCTA (GREML) analysis, and seems to be close to its asymptotic value (i.e., as sample size goes to infinity), suggesting that we have captured most of the heritability for the SNPs used. Thus, our results resolve the common SNP portion of the "missing heritability" problem - i.e., the gap between prediction R-squared and SNP heritability. The ~20k activated SNPs in our height predictor reveal the genetic architecture of human height, at least for common SNPs. Our primary dataset is the UK Biobank cohort, comprised of almost 500k individual genotypes with multiple phenotypes. We also use other datasets and SNPs found in earlier GWAS for out-of-sample validation of our results.
]]></description>
<dc:creator>Lello, L.</dc:creator>
<dc:creator>Avery, S. G.</dc:creator>
<dc:creator>Tellier, L.</dc:creator>
<dc:creator>Vazquez, A.</dc:creator>
<dc:creator>de los Campos, G.</dc:creator>
<dc:creator>Hsu, S. D. H.</dc:creator>
<dc:date>2017-09-18</dc:date>
<dc:identifier>doi:10.1101/190124</dc:identifier>
<dc:title><![CDATA[Accurate Genomic Prediction Of Human Height]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-09-18</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/174755v1?rss=1">
<title>
<![CDATA[
The genetic architecture of osteoarthritis: insights from UK Biobank 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/174755v1?rss=1"
</link>
<description><![CDATA[
Osteoarthritis is a common complex disease with huge public health burden. Here we perform a genome-wide association study for osteoarthritis using data across 16.5 million variants from the UK Biobank resource. Following replication and meta-analysis in up to 30,727 cases and 297,191 controls, we report 9 new osteoarthritis loci, in all of which the most likely causal variant is non-coding. For three loci, we detect association with biologically-relevant radiographic endophenotypes, and in five signals we identify genes that are differentially expressed in degraded compared to intact articular cartilage from osteoarthritis patients. We establish causal effects for higher body mass index, but not for triglyceride levels or type 2 diabetes liability, on osteoarthritis.
]]></description>
<dc:creator>Zengini, E.</dc:creator>
<dc:creator>Hatzikotoulas, K.</dc:creator>
<dc:creator>Tachmazidou, I.</dc:creator>
<dc:creator>Steinberg, J.</dc:creator>
<dc:creator>Hartwig, F. P.</dc:creator>
<dc:creator>Southam, L.</dc:creator>
<dc:creator>Hackinger, S.</dc:creator>
<dc:creator>Boer, C. G.</dc:creator>
<dc:creator>Styrkarsdottir, U.</dc:creator>
<dc:creator>Suveges, D.</dc:creator>
<dc:creator>Kilian, B.</dc:creator>
<dc:creator>Gilly, A.</dc:creator>
<dc:creator>Ingvarsson, T.</dc:creator>
<dc:creator>Jonsson, H.</dc:creator>
<dc:creator>Babis, G. C.</dc:creator>
<dc:creator>McCaskie, A.</dc:creator>
<dc:creator>Uitterlinden, A. G.</dc:creator>
<dc:creator>van Meurs, J. B. J.</dc:creator>
<dc:creator>Thorsteinsdottir, U.</dc:creator>
<dc:creator>Stefansson, K.</dc:creator>
<dc:creator>Davey Smith, G.</dc:creator>
<dc:creator>Wilkinson, J. M.</dc:creator>
<dc:creator>Zeggini, E.</dc:creator>
<dc:date>2017-08-11</dc:date>
<dc:identifier>doi:10.1101/174755</dc:identifier>
<dc:title><![CDATA[The genetic architecture of osteoarthritis: insights from UK Biobank]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-08-11</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/092015v1?rss=1">
<title>
<![CDATA[
Cannabis use and risk of schizophrenia:a Mendelian randomization study 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/092015v1?rss=1"
</link>
<description><![CDATA[
Cannabis use is observationally associated with an increased risk of schizophrenia, however whether the relationship is causal is not known. To determine the nature of the association between cannabis use on risk of schizophrenia using Mendelian randomization (MR) analysis, we used ten genetic variants previously identified to associate with cannabis use in 32,330 individuals. Genetic variants were used in a MR analyses of the association of genetically determined cannabis on risk of schizophrenia in 34,241 cases and 45,604 controls from predominantly European descent. Estimates from MR were compared to a metaanalysis of observational studies reporting effect estimates for ever use of cannabis and risk of schizophrenia or related disorders. Genetically determined use of cannabis was associated with increased risk of schizophrenia (OR of schizophrenia for users vs. non-users of cannabis: 1.37; 95%CI, 1.09 to 1.67; P-value=0.007). The corresponding estimate from observational analysis was 1.50 (95% CI, 1.10 to 2.00; P-value for heterogeneity = 0.88). The genetic instrument did not show evidence of pleiotropy on MR-Egger (Egger test, P-value=0.292) nor on multivariable MR accounting for tobacco exposure (OR of schizophrenia for users vs. nonusers of cannabis, adjusted for ever vs. never smoker: 1.41; 95% CI, 1.09-1.83). Furthermore, the causal estimate remained robust to sensitivity analyses. These findings strongly support a causal association between genetically determined use of cannabis and risk of schizophrenia. Such robust evidence may inform public health message about the risks of cannabis use, especially regarding its potential mental health consequences.
]]></description>
<dc:creator>Vaucher, J.</dc:creator>
<dc:creator>Keating, B. J.</dc:creator>
<dc:creator>Lasserre, A. M.</dc:creator>
<dc:creator>Gan, W.</dc:creator>
<dc:creator>Lyall, D.</dc:creator>
<dc:creator>Ward, J.</dc:creator>
<dc:creator>Smith, D. J.</dc:creator>
<dc:creator>Pell, J.</dc:creator>
<dc:creator>Sattar, N.</dc:creator>
<dc:creator>Pare, G.</dc:creator>
<dc:creator>Holmes, M.</dc:creator>
<dc:date>2016-12-07</dc:date>
<dc:identifier>doi:10.1101/092015</dc:identifier>
<dc:title><![CDATA[Cannabis use and risk of schizophrenia:a Mendelian randomization study]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2016-12-07</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/076133v1?rss=1">
<title>
<![CDATA[
Correcting subtle stratification in summary association statistics 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/076133v1?rss=1"
</link>
<description><![CDATA[
Population stratification is a well-documented confounder in GWASes, and is often addressed by including principal component (PC) covariates computed from common SNPs (SNP-PCs). In our analyses of summary statistics from 36 GWASes (mean n=88k), including 20 GWASes using 23andMe data that included SNP-PC covariates, we observed a significantly inflated LD score regression (LDSC) intercept for several traits--suggesting that residual stratification remains a concern, even when SNPPC covariates are included.nnHere we propose a new method, PC loading regression, to correct for stratification in summary statistics by leveraging SNP loadings for PCs computed in a large reference panel. In addition to SNP-PCs, the method can be applied to haploSNP-PCs, i.e. PCs computed from a larger number of rare haplotype variants that better capture subtle structure. Using simulations based on real genotypes from 54,000 individuals of diverse European ancestry from the Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort, we show that PC loading regression effectively corrects for stratification along top PCs.nnWe applied PC loading regression to several traits with inflated LDSC intercepts. Correcting for the top four SNP-PCs in GERA data, we observe a significant reduction in LDSC intercept height summary statistics from the Genetic Investigation of ANthropometric Traits (GIANT) consortium, but not for 23andMe summary statistics, which already included SNP-PC covariates. However, when correcting for additional haploSNP-PCs in 23andMe GWASes, inflation in the LDSC intercept was eliminated for eye color, hair color, and skin color and substantially reduced for height (1.41 to 1.16; n=430k). Correcting for haploSNP-PCs in GIANT height summary statistics eliminated inflation in the LDSC intercept (from 1.35 to 1.00; n=250k), eliminating 27 significant association signals including one at the LCT locus, which is highly differentiated among European populations and widely known to produce spurious signals. Overall, our results suggest that uncorrected population stratification is a concern in GWASes of large sample size and that PC loading regression can correct for this stratification.
]]></description>
<dc:creator>Gaurav Bhatia</dc:creator>
<dc:creator>Nicholas A Furlotte</dc:creator>
<dc:creator>Po-Ru Loh</dc:creator>
<dc:creator>Xuanyao Liu</dc:creator>
<dc:creator>Hilary Kiyo Finucane</dc:creator>
<dc:creator>Alexander Gusev</dc:creator>
<dc:creator>Alkes Price</dc:creator>
<dc:creator></dc:creator>
<dc:date>2016-09-19</dc:date>
<dc:identifier>doi:10.1101/076133</dc:identifier>
<dc:title><![CDATA[Correcting subtle stratification in summary association statistics]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2016-09-19</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/164426v1?rss=1">
<title>
<![CDATA[
Meta-analysis of exome array data identifies six novel genetic loci for lung function 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/164426v1?rss=1"
</link>
<description><![CDATA[
Over 90 regions of the genome have been associated with lung function to date, many of which have also been implicated in chronic obstructive pulmonary disease (COPD). We carried out meta-analyses of exome array data and three lung function measures: forced expiratory volume in one second (FEV1), forced vital capacity (FVC) and the ratio of FEV1 to FVC (FEV1/FVC). These analyses by the SpiroMeta and CHARGE consortia included 60,749 individuals of European ancestry from 23 studies, and 7,721 individuals of African Ancestry from 5 studies in the discovery stage, with follow-up in up to 111,556 independent individuals. We identified significant (P<2{middle dot}8x10-7) associations with six SNPs: a nonsynonymous variant in RPAP1, which is predicted to be damaging, three intronic SNPs (SEC24C, CASC17 and UQCC1) and two intergenic SNPs near to LY86 and FGF10. eQTL analyses found evidence for regulation of gene expression at three signals and implicated several genes including TYRO3 and PLAU. Further interrogation of these loci could provide greater understanding of the determinants of lung function and pulmonary disease.
]]></description>
<dc:creator>Jackson, V. E.</dc:creator>
<dc:creator>Latourelle, J. C.</dc:creator>
<dc:creator>Wain, L. V.</dc:creator>
<dc:creator>Smith, A. V.</dc:creator>
<dc:creator>Grove, M. L.</dc:creator>
<dc:creator>Bartz, T. M.</dc:creator>
<dc:creator>Obeidat, M.</dc:creator>
<dc:creator>Province, M. A.</dc:creator>
<dc:creator>Gao, W.</dc:creator>
<dc:creator>Qaiser, B.</dc:creator>
<dc:creator>Porteous, D. J.</dc:creator>
<dc:creator>Cassano, P. A.</dc:creator>
<dc:creator>Ahluwalia, T. S.</dc:creator>
<dc:creator>Grarup, N.</dc:creator>
<dc:creator>Li, J.</dc:creator>
<dc:creator>Altmaier, E.</dc:creator>
<dc:creator>Marten, J.</dc:creator>
<dc:creator>Harris, S. E.</dc:creator>
<dc:creator>Manichaikul, A.</dc:creator>
<dc:creator>Pottinger, T. D.</dc:creator>
<dc:creator>Li-Gao, R.</dc:creator>
<dc:creator>Lind-Thomsen, A.</dc:creator>
<dc:creator>Mahajan, A.</dc:creator>
<dc:creator>Lahousse, L.</dc:creator>
<dc:creator>Imboden, M.</dc:creator>
<dc:creator>Teumer, A.</dc:creator>
<dc:creator>Prins, B.</dc:creator>
<dc:creator>Lyytikäinen, L.-P.</dc:creator>
<dc:creator>Eiriksdottir, G.</dc:creator>
<dc:creator>Franceschini, N.</dc:creator>
<dc:creator>Sitlani, C. M.</dc:creator>
<dc:creator>Brody, J. A.</dc:creator>
<dc:creator>Bosse, Y.</dc:creator>
<dc:creator>Timens, W.</dc:creator>
<dc:creator>Kraja, A.</dc:creator>
<dc:creator>Loukola, A.</dc:creator>
<dc:creator>Tang, W.</dc:creator>
<dc:creator>Liu, Y.</dc:creator>
<dc:creator>Bork-Jensen, J.</dc:creator>
<dc:creator>Justesen, J. M.</dc:creator>
<dc:creator>Linneberg, A.</dc:creator>
<dc:creator>Lange, L.</dc:creator>
<dc:date>2017-07-17</dc:date>
<dc:identifier>doi:10.1101/164426</dc:identifier>
<dc:title><![CDATA[Meta-analysis of exome array data identifies six novel genetic loci for lung function]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-07-17</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/203380v1?rss=1">
<title>
<![CDATA[
Leveraging molecular QTL to understand the genetic architecture of diseases and complex traits 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/203380v1?rss=1"
</link>
<description><![CDATA[
There is increasing evidence that many GWAS risk loci are molecular QTL for gene ex-pression (eQTL), histone modification (hQTL), splicing (sQTL), and/or DNA methylation (meQTL). Here, we introduce a new set of functional annotations based on causal posterior prob-abilities (CPP) of fine-mapped molecular cis-QTL, using data from the GTEx and BLUEPRINT consortia. We show that these annotations are very strongly enriched for disease heritability across 41 independent diseases and complex traits (average N = 320K): 5.84x for GTEx eQTL, and 5.44x for eQTL, 4.27-4.28x for hQTL (H3K27ac and H3K4me1), 3.61x for sQTL and 2.81x for meQTL in BLUEPRINT (all P [&le;] 1.39e-10), far higher than enrichments obtained using stan-dard functional annotations that include all significant molecular cis-QTL (1.17-1.80x). eQTL annotations that were obtained by meta-analyzing all 44 GTEx tissues generally performed best, but tissue-specific blood eQTL annotations produced stronger enrichments for autoimmune dis-eases and blood cell traits and tissue-specific brain eQTL annotations produced stronger enrich-ments for brain-related diseases and traits, despite high cis-genetic correlations of eQTL effect sizes across tissues. Notably, eQTL annotations restricted to loss-of-function intolerant genes from ExAC were even more strongly enriched for disease heritability (17.09x; vs. 5.84x for all genes; P = 4.90e-17 for difference). All molecular QTL except sQTL remained significantly enriched for disease heritability in a joint analysis conditioned on each other and on a broad set of functional annotations from previous studies, implying that each of these annotations is uniquely informative for disease and complex trait architectures.
]]></description>
<dc:creator>Hormozdiari, F.</dc:creator>
<dc:creator>Gazal, S.</dc:creator>
<dc:creator>Geijn, B. v. d.</dc:creator>
<dc:creator>Finucane, H.</dc:creator>
<dc:creator>Ju, C. J.- T.</dc:creator>
<dc:creator>Loh, P.-R.</dc:creator>
<dc:creator>Schoech, A.</dc:creator>
<dc:creator>Reshef, Y.</dc:creator>
<dc:creator>Liu, X.</dc:creator>
<dc:creator>O'Connor, L.</dc:creator>
<dc:creator>Gusev, A.</dc:creator>
<dc:creator>Eskin, E.</dc:creator>
<dc:creator>Price, A.</dc:creator>
<dc:date>2017-10-15</dc:date>
<dc:identifier>doi:10.1101/203380</dc:identifier>
<dc:title><![CDATA[Leveraging molecular QTL to understand the genetic architecture of diseases and complex traits]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-10-15</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/207498v1?rss=1">
<title>
<![CDATA[
Genome-wide association study of body fat distribution identifies novel loci and sex-specific effects 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/207498v1?rss=1"
</link>
<description><![CDATA[
Body mass and body fat composition are of clinical interest due to their links to cardiovascular- and metabolic diseases. Fat stored in the trunk has been suggested as more pathogenic compared to fat stored in other compartments of the body. In this study, we performed genome-wide association studies (GWAS) for the proportion of body fat distributed to the arms, legs and trunk estimated from segmental bio-electrical impedance analysis (sBIA) for 362,499 individuals from the UK Biobank. A total of 97 loci, were identified to be associated with body fat distribution, 40 of which have not previously been associated with an anthropometric trait. A high degree of sex-heterogeneity was observed and associations were primarily observed in females, particularly for distribution of fat to the legs or trunk. Our findings also implicate that body fat distribution in females involves mesenchyme derived tissues and cell types, female endocrine tissues a well as several enzymatically active members of the ADAMTS family of metalloproteinases, which are involved in extracellular matrix maintenance and remodeling.
]]></description>
<dc:creator>Rask-Andersen, M.</dc:creator>
<dc:creator>Karlsson, T.</dc:creator>
<dc:creator>Ek, W. E.</dc:creator>
<dc:creator>Johansson, A.</dc:creator>
<dc:date>2017-10-23</dc:date>
<dc:identifier>doi:10.1101/207498</dc:identifier>
<dc:title><![CDATA[Genome-wide association study of body fat distribution identifies novel loci and sex-specific effects]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-10-23</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/199828v1?rss=1">
<title>
<![CDATA[
Investigating genetic correlations and causal effects between caffeine consumption and sleep behaviours 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/199828v1?rss=1"
</link>
<description><![CDATA[
Study Objectives: Higher caffeine consumption has been linked to poorer sleep and insomnia complaints. We investigated whether these observational associations are the result of genetic risk factors influencing both caffeine consumption and poorer sleep, and/or whether they reflect (possibly bidirectional) causal effects. Methods: Summary-level data were available from genome-wide association studies (GWAS) on caffeine consumption (n=91,462), sleep duration, and chronotype (i.e., being a  morning versus an  evening person) (both n=128,266), and insomnia complaints (n=113,006). Linkage disequilibrium (LD) score regression was used to calculate genetic correlations, reflecting the extent to which genetic variants influencing caffeine consumption and sleep behaviours overlap. Causal effects were tested with bidirectional, two-sample Mendelian randomization (MR), an instrumental variable approach that utilizes genetic variants robustly associated with an exposure variable as an instrument to test causal effects. Estimates from individual genetic variants were combined using inverse-variance weighted meta-analysis, weighted median regression and MR Egger regression methods. Results: There was no clear evidence for genetic correlation between caffeine consumption and sleep duration (rg=0.000, p=0.998), chronotype (rg=0.086, p=0.192) or insomnia (rg=-0.034, p=0.700). Two-sample Mendelian randomization analyses did not support causal effects from caffeine consumption to sleep behaviours, or the other way around. Conclusions: We found no evidence in support of genetic correlation or causal effects between caffeine consumption and sleep. While caffeine may have acute effects on sleep when taken shortly before habitual bedtime, our findings suggest that a more sustained pattern of high caffeine consumption is likely associated with poorer sleep through shared environmental factors.
]]></description>
<dc:creator>Treur, J. L.</dc:creator>
<dc:creator>Gibson, M.</dc:creator>
<dc:creator>Taylor, A. E.</dc:creator>
<dc:creator>Rogers, P.</dc:creator>
<dc:creator>Munafo, M. R.</dc:creator>
<dc:date>2017-10-07</dc:date>
<dc:identifier>doi:10.1101/199828</dc:identifier>
<dc:title><![CDATA[Investigating genetic correlations and causal effects between caffeine consumption and sleep behaviours]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-10-07</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/144352v1?rss=1">
<title>
<![CDATA[
Breastfeeding And Risk Of Asthma, Hay Fever And Eczema 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/144352v1?rss=1"
</link>
<description><![CDATA[
BackgroundBreastfeeding is commonly proposed to protect against atopic diseases. However, studies aiming to quantify these protective effects have shown conflicting results.nnMethodsTo entrench the effects of breastfeeding on risk of asthma, hay fever and eczema, our study included a large study cohort, UK Biobank (N=502,682). Information was collected on whether participants had been breastfeed and on the prevalence of disease. Disease was tested for association with breastfeeding, adjusting or matching for influential covariates.nnFindingsA total of 443,068 participants were included in our analyses of which 71{middle dot}2% had been breastfed. The prevalence of asthma was 11{middle dot}4 % and 12{middle dot}7% in the breastfed and non-breastfed groups, and hay fever or eczema (23{middle dot}9% and 24{middle dot}8 % in the two groups respectively. When correcting or matching for potential confounders, we could not see any association between being breastfed and asthma. However, there were increased odds of hay fever and eczema among participants that had been breastfed (P=7{middle dot}78x10-6).nnInterpretationThis study reports that breastfeeding is associated with increased odds of hay fever and eczema but it show no evidence for breastfeeding being associated with asthma diagnosis.nnFundingThe Swedish Society for Medical Research (SSMF), the Kjell and Marta Beijers Foundation, Goran Gustafssons Foundation, the Swedish Medical Research Council (Project Number 2015-03327), the Marcus Borgstrom Foundation, the [A]ke Wiberg Foundation and the Vleugels Foundation.nnEvidence before this studyAtopic diseases affect quality of life for a large part of the human population and pose a very high socio-economic burden. Genetic, environmental, and a number of lifestyle factors influence our risk of developing atopic disorders and high familial prevalence is one of the strongest known risk factors for disease. Several environmental and lifestyle risk factors have already been well established in the scientific community, such as smoking on the risk of developing asthma. Breastfeeding is commonly argued to be protective against atopic diseases. However, studies aiming to quantify these protective effects have shown conflicting results.nnAdded value of this studyOur study is, to our knowledge, the largest investigation on how breastfeeding is associated with being diagnosed with asthma, hay fever and eczema at adult age. The study found breastfeeding to be associated with increased odds of being diagnosed with hay fever and eczema during life, while we found no association between breastfeeding and asthma. Our results for hay fever and eczema is in line with the western world hygiene hypothesis, but contradict the general picture of breastfeeding being protective.nnImplications of all the available evidenceTo be able to give parents correct advice on lifestyles choices that will protect their kids against atopic diseases, we need to clarify the currently conflicting results on the effect of breastfeeding on risk of atopic diseases. However, these results should not be used to recommend breastfeeding or to discourage it since the present study only investigates the association between breastfeeding history and being diagnosed with asthma, hay fever and eczema during lifetime.nnAbbreviations
]]></description>
<dc:creator>Ek, W.</dc:creator>
<dc:creator>Karlsson, T.</dc:creator>
<dc:creator>Azuaje Hernandez, C.</dc:creator>
<dc:creator>Rask-Andersen, M.</dc:creator>
<dc:creator>Johansson, A.</dc:creator>
<dc:date>2017-05-31</dc:date>
<dc:identifier>doi:10.1101/144352</dc:identifier>
<dc:title><![CDATA[Breastfeeding And Risk Of Asthma, Hay Fever And Eczema]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-05-31</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/076794v1?rss=1">
<title>
<![CDATA[
Genomic analyses for age at menarche identify 389 independent signals and indicate BMI-independent effects of puberty timing on cancer susceptibility 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/076794v1?rss=1"
</link>
<description><![CDATA[
The timing of puberty is a highly polygenic childhood trait that is epidemiologically associated with various adult diseases. Here, we analyse 1000-Genome reference panel imputed genotype data on up to ~370,000 women and identify 389 independent signals (all P<5x10-8) for age at menarche, a notable milestone in female pubertal development. In Icelandic data from deCODE, these signals explain ~7.4% of the population variance in age at menarche, corresponding to one quarter of the estimated heritability. We implicate over 250 genes via coding variation or associated gene expression, and demonstrate enrichment across genes active in neural tissues. We identify multiple rare variants near the imprinted genes MKRN3 and DLK1 that exhibit large effects on menarche only when paternally inherited. Disproportionate effects of variants on early or late puberty timing are observed: single variant and heritability estimates are larger for early than late puberty timing in females. The opposite pattern is seen in males, with larger estimates for late than early puberty timing. Mendelian randomization analyses indicate causal inverse associations, independent of BMI, between puberty timing and risks for breast and endometrial cancers in women, and prostate cancer in men. In aggregate, our findings reveal new complexity in the genetic regulation of puberty timing and support new causal links with adult cancer risks.
]]></description>
<dc:creator>Felix Day</dc:creator>
<dc:creator>Deborah Thompson</dc:creator>
<dc:creator>Hannes Helgason</dc:creator>
<dc:creator>Daniel Chasman</dc:creator>
<dc:creator>Hilary Finucane</dc:creator>
<dc:creator>Patrick Sulem</dc:creator>
<dc:creator>Katherine Ruth</dc:creator>
<dc:creator>Sean Whalen</dc:creator>
<dc:creator>Abhishek Sarkar</dc:creator>
<dc:creator>Eva Albrecht</dc:creator>
<dc:creator>Elisabeth Altmaier</dc:creator>
<dc:creator>Marzyeh Amini</dc:creator>
<dc:creator>Caterina Barbieri</dc:creator>
<dc:creator>Thibaud Boutin</dc:creator>
<dc:creator>Archie Campbell</dc:creator>
<dc:creator>Ellen Demerath</dc:creator>
<dc:creator>Ayush Giri</dc:creator>
<dc:creator>Chunyan He</dc:creator>
<dc:creator>Jouke Hottenga</dc:creator>
<dc:creator>Robert Karlsson</dc:creator>
<dc:creator>Ivana Kolcic</dc:creator>
<dc:creator>Po-Ru Loh</dc:creator>
<dc:creator>Kathryn Lunetta</dc:creator>
<dc:creator>Massimo Mangino</dc:creator>
<dc:creator>Brumat Marco</dc:creator>
<dc:creator>Gerorge McMahon</dc:creator>
<dc:creator>Sarah Medland</dc:creator>
<dc:creator>Ilja Nolte</dc:creator>
<dc:creator>Raymond Noordam</dc:creator>
<dc:creator>Teresa Nutile</dc:creator>
<dc:creator>Lavinia Paternoster</dc:creator>
<dc:creator>Natalia Perjakova</dc:creator>
<dc:creator>Eleonora Porcu</dc:creator>
<dc:creator>Lynda Rose</dc:creator>
<dc:creator>Katharina Schraut</dc:creator>
<dc:creator>Ayellet Segre</dc:creator>
<dc:creator>Albert Smith</dc:creator>
<dc:creator>Lisette Stolk</dc:creator>
<dc:creator>Alexander Teumer</dc:creator>
<dc:creator>Irene Andrulis</dc:creator>
<dc:creator>Stefania Ban</dc:creator>
<dc:date>2016-09-23</dc:date>
<dc:identifier>doi:10.1101/076794</dc:identifier>
<dc:title><![CDATA[Genomic analyses for age at menarche identify 389 independent signals and indicate BMI-independent effects of puberty timing on cancer susceptibility]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2016-09-23</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/196071v1?rss=1">
<title>
<![CDATA[
Genome-wide association study of social relationship satisfaction: significant loci and correlations with psychiatric conditions 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/196071v1?rss=1"
</link>
<description><![CDATA[
Dissatisfaction in social relationships is reported widely across many psychiatric conditions. We investigated the genetic architecture of family relationship satisfaction and friendship satisfaction in the UK Biobank. We leveraged the high genetic correlation between the two phenotypes (rg = 0.87{+/-}0.03; P < 2.2x10-16) to conduct multi-trait analysis of Genome Wide Association Study (GWAS) (Neffective family = 164,112; Neffective friendship = 158,116). We identified two genome-wide significant associations for both the phenotypes: rs1483617 on chromosome 3 and rs2189373 on chromosome 6, a region previously implicated in schizophrenia. eQTL and chromosome conformation capture in neural tissues prioritizes several genes including NLGN1. Gene-based association studies identified several significant genes, with highest expression in brain tissues. Genetic correlation analysis identified significant negative correlations for multiple psychiatric conditions including highly significant negative correlation with cross-psychiatric disorder GWAS, underscoring the central role of social relationship dissatisfaction in psychiatric diagnosis. The two phenotypes were enriched for genes that are loss of function intolerant. Both phenotypes had modest, significant additive SNP heritability of approximately 6%. Our results underscore the central role of social relationship satisfaction in mental health and identify genes and tissues associated with it.
]]></description>
<dc:creator>Warrier, V.</dc:creator>
<dc:creator>Bourgeron, T.</dc:creator>
<dc:creator>Baron-Cohen, S.</dc:creator>
<dc:date>2017-09-29</dc:date>
<dc:identifier>doi:10.1101/196071</dc:identifier>
<dc:title><![CDATA[Genome-wide association study of social relationship satisfaction: significant loci and correlations with psychiatric conditions]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-09-29</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/144410v1?rss=1">
<title>
<![CDATA[
Refining The Accuracy Of Validated Target Identification Through Coding Variant Fine-Mapping In Type 2 Diabetes 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/144410v1?rss=1"
</link>
<description><![CDATA[
Identification of coding variant associations for complex diseases offers a direct route to biological insight, but is dependent on appropriate inference concerning the causal impact of those variants on disease risk. We aggregated exome-array and exome sequencing data for 81,412 type 2 diabetes (T2D) cases and 370,832 controls of diverse ancestry, identifying 40 distinct coding variant association signals (at 38 loci) reaching significance (p<2.2x10-7). Of these, 16 represent novel associations mapping outside known genome-wide association study (GWAS) signals. We make two important observations. First, despite a threefold increase in sample size over previous efforts, only five of the 40 signals are driven by variants with minor allele frequency <5%, and we find no evidence for low-frequency variants with allelic odds ratio >1.36. Second, we used GWAS data from 50,160 T2D cases and 465,272 controls to fine-map associated coding variants in their regional context, with and without additional weighting, to account for the global enrichment of complex trait association signals in coding exons. We demonstrate convincing support (posterior probability >80% under the "annotation-weighted" model) that coding variants are causal for the association at 16 of the 40 signals (including novel signals involving POC5 p.His36Arg, ANKH p.Arg187Gln, WSCD2 p.Thr113Ile, PLCB3 p.Ser778Leu, and PNPLA3 p.Ile148Met). However, one third of coding variant association signals represent "false leads" at which naive analysis would have led to an erroneous inference regarding the effector transcript mediating the signal. Accurate identification of validated targets is dependent on correct specification of the contribution of coding and non-coding mediated mechanisms at associated loci.
]]></description>
<dc:creator>Mahajan, A.</dc:creator>
<dc:creator>Morris, A. P.</dc:creator>
<dc:creator>Rotter, J. I.</dc:creator>
<dc:creator>McCarthy, M. I.</dc:creator>
<dc:date>2017-05-31</dc:date>
<dc:identifier>doi:10.1101/144410</dc:identifier>
<dc:title><![CDATA[Refining The Accuracy Of Validated Target Identification Through Coding Variant Fine-Mapping In Type 2 Diabetes]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-05-31</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/192237v1?rss=1">
<title>
<![CDATA[
Selection bias in instrumental variable analyses 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/192237v1?rss=1"
</link>
<description><![CDATA[
Participants in epidemiological and genetic studies are rarely true random samples of the populations they are intended to represent, and both known and unknown factors can influence participation in a study (known as selection into a study). The circumstances in which selection causes bias in an instrumental variable (IV) analysis are not widely understood by practitioners of IV analyses. We use directed acyclic graphs (DAGs) to depict assumptions about the selection mechanism (factors affecting selection) and show how DAGs can be used to determine when a two-stage least squares (2SLS) IV analysis is biased by different selection mechanisms. Via simulations, we show that selection can result in a biased IV estimate with substantial confidence interval undercoverage, and the level of bias can differ between instrument strengths, a linear and nonlinear exposure-instrument association, and a causal and noncausal exposure effect. We present an application from the UK Biobank study, which is known to be a selected sample of the general population. Of interest was the causal effect of education on the decision to smoke. The 2SLS exposure estimates were very different between the IV analysis ignoring selection and the IV analysis which adjusted for selection (e.g., 1.8 [95% confidence interval -1.5, 5.0] and -4.5 [-6.6, -2.4], respectively). We conclude that selection bias can have a major effect on an IV analysis and that statistical methods for estimating causal effects using data from nonrandom samples are needed.
]]></description>
<dc:creator>Hughes, R. A.</dc:creator>
<dc:creator>Davies, N. M.</dc:creator>
<dc:creator>Davey Smith, G.</dc:creator>
<dc:creator>Tilling, K.</dc:creator>
<dc:date>2017-09-22</dc:date>
<dc:identifier>doi:10.1101/192237</dc:identifier>
<dc:title><![CDATA[Selection bias in instrumental variable analyses]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-09-22</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/118018v1?rss=1">
<title>
<![CDATA[
Estimating the proportion of disease heritability mediated by gene expression levels 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/118018v1?rss=1"
</link>
<description><![CDATA[
Disease risk variants identified by GWAS are predominantly noncoding, suggesting that gene regulation plays an important role. eQTL studies in unaffected individuals are often used to link disease-associated variants with the genes they regulate, relying on the hypothesis that noncoding regulatory effects are mediated by steady-state expression levels. To test this hypothesis, we developed a method to estimate the proportion of disease heritability mediated by the cis-genetic component of assayed gene expression levels. The method, gene expression co-score regression (GECS regression), relies on the idea that, for a gene whose expression level affects a phenotype, SNPs with similar effects on the expression of that gene will have similar phenotypic effects. In order to distinguish directional effects mediated by gene expression from non-directional pleiotropic or tagging effects, GECS regression operates on pairs of cis SNPs in linkage equilibrium, regressing pairwise products of disease effect sizes on products of cis-eQTL effect sizes. We verified that GECS regression produces robust estimates of mediated effects in simulations. We applied the method to eQTL data in 44 tissues from the GTEx consortium (average NeQTL = 158 samples) in conjunction with GWAS summary statistics for 30 diseases and complex traits (average NGWAS = 88K) with low pairwise genetic correlation, estimating the proportion of SNP-heritability mediated by the cis-genetic component of assayed gene expression in the union of the 44 tissues. The mean estimate was 0.21 (s.e. = 0.01) across 30 traits, with a significantly positive estimate (p < 0.001) for every trait. Thus, assayed gene expression in bulk tissues mediates a statistically significant but modest proportion of disease heritability, motivating the development of additional assays to capture regulatory effects and the use of our method to estimate how much disease heritability they mediate.
]]></description>
<dc:creator>O'Connor, L. J.</dc:creator>
<dc:creator>Gusev, A.</dc:creator>
<dc:creator>Liu, X.</dc:creator>
<dc:creator>Loh, P.-R.</dc:creator>
<dc:creator>Finucane, H. K.</dc:creator>
<dc:creator>Price, A. L.</dc:creator>
<dc:date>2017-03-18</dc:date>
<dc:identifier>doi:10.1101/118018</dc:identifier>
<dc:title><![CDATA[Estimating the proportion of disease heritability mediated by gene expression levels]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-03-18</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/145755v1?rss=1">
<title>
<![CDATA[
Widespread signatures of negative selection in the genetic architecture of human complex traits 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/145755v1?rss=1"
</link>
<description><![CDATA[
Estimation of the joint distribution of effect size and minor allele frequency (MAF) for genetic variants is important for understanding the genetic basis of complex trait variation and can be used to detect signature of natural selection. We develop a Bayesian mixed linear model that simultaneously estimates SNP-based heritability, polygenicity (i.e. the proportion of SNPs with nonzero effects) and the relationship between effect size and MAF for complex traits in conventionally unrelated individuals using genome-wide SNP data. We apply the method to 28 complex traits in the UK Biobank data (N = 126,752), and show that on average across 28 traits, 6% of SNPs have nonzero effects, which in total explain 22% of phenotypic variance. We detect significant (p < 0.05/28 =1.8x10-3) signatures of natural selection for 23 out of 28 traits including reproductive, cardiovascular, and anthropometric traits, as well as educational attainment. We further apply the method to 27,869 gene expression traits (N = 1,748), and identify 30 genes that show significant (p < 2.3x10-6) evidence of natural selection. All the significant estimates of the relationship between effect size and MAF in either complex traits or gene expression traits are consistent with a model of negative selection, as confirmed by forward simulation. We conclude that natural selection acts pervasively on human complex traits shaping genetic variation in the form of negative selection.
]]></description>
<dc:creator>Zeng, J.</dc:creator>
<dc:creator>de Vlaming, R.</dc:creator>
<dc:creator>Wu, Y.</dc:creator>
<dc:creator>Robinson, M.</dc:creator>
<dc:creator>Lloyd-Jones, L.</dc:creator>
<dc:creator>Yengo, L.</dc:creator>
<dc:creator>Yap, C.</dc:creator>
<dc:creator>Xue, A.</dc:creator>
<dc:creator>Sidorenko, J.</dc:creator>
<dc:creator>McRae, A.</dc:creator>
<dc:creator>Powell, J.</dc:creator>
<dc:creator>Montgomery, G.</dc:creator>
<dc:creator>Metspalu, A.</dc:creator>
<dc:creator>Esko, T.</dc:creator>
<dc:creator>Gibson, G.</dc:creator>
<dc:creator>Wray, N.</dc:creator>
<dc:creator>Visscher, P.</dc:creator>
<dc:creator>Yang, J.</dc:creator>
<dc:date>2017-06-03</dc:date>
<dc:identifier>doi:10.1101/145755</dc:identifier>
<dc:title><![CDATA[Widespread signatures of negative selection in the genetic architecture of human complex traits]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-06-03</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/074815v1?rss=1">
<title>
<![CDATA[
The Causal Effects of Education on Health, Mortality, Cognition, Well-being, and Income in the UK Biobank 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/074815v1?rss=1"
</link>
<description><![CDATA[
Educated people are generally healthier, have fewer comorbidities and live longer than people with less education. Previous evidence about the effects of education come from observational studies many of which are affected by residual confounding. Legal changes to the minimum school leave age is a potential natural experiment which provides a potentially more robust source of evidence about the effects of schooling. Previous studies have exploited this natural experiment using population-level administrative data to investigate mortality, and relatively small surveys to investigate the effect on mortality. Here, we add to the evidence using data from a large sample from the UK Biobank. We exploit the raising of the school-leaving age in the UK in September 1972 as a natural experiment and regression discontinuity and instrumental variable estimators to identify the causal effects of staying on in school. Remaining in school was positively associated with 23 of 25 outcomes. After accounting for multiple hypothesis testing, we found evidence of causal effects on twelve outcomes, however, the associations of schooling and intelligence, smoking, and alcohol consumption may be due to genomic and socioeconomic confounding factors. Education affects some, but not all health and socioeconomic outcomes. Differences between educated and less educated people may be partially due to residual genetic and socioeconomic confounding.nnSignificance StatementOn average people who choose to stay in education for longer are healthier, wealthier, and live longer. We investigated the causal effects of education on health, income, and well-being later in life. This is the largest study of its kind to date and it has objective clinic measures of morbidity and aging. We found evidence that people who were forced to remain in school had higher wages and lower mortality. However, there was little evidence of an effect on intelligence later in life. Furthermore, estimates of the effects of education using conventionally adjusted regression analysis are likely to suffer from genomic confounding. In conclusion, education affects some, but not all health outcomes later in life.nnFundingThe Medical Research Council (MRC) and the University of Bristol fund the MRC Integrative Epidemiology Unit [MC_UU_12013/1, MC_UU_12013/9]. NMD is supported by the Economics and Social Research Council (ESRC) via a Future Research Leaders Fellowship [ES/N000757/1]. The research described in this paper was specifically funded by a grant from the Economics and Social Research Council for Transformative Social Science. No funding body has influenced data collection, analysis or its interpretations. This publication is the work of the authors, who serve as the guarantors for the contents of this paper. This work was carried out using the computational facilities of the Advanced Computing Research Centre -http://www.bris.ac.uk/acrc/ and the Research Data Storage Facility of the University of Bristol -- http://www.bris.ac.uk/acrc/storage/. This research was conducted using the UK Biobank Resource.nnData accessThe statistical code used to produce these results can be accessed here: (https://github.com/nmdavies/UKbiobankROSLA). The final analysis dataset used in this study is archived with UK Biobank, which can be accessed by contacting UK Biobank access@biobank.ac.uk.
]]></description>
<dc:creator>Neil M Davies</dc:creator>
<dc:creator>Matt Dickson</dc:creator>
<dc:creator>George Davey Smith</dc:creator>
<dc:creator>Gerard van den Berg</dc:creator>
<dc:creator>Frank Windmeijer</dc:creator>
<dc:creator></dc:creator>
<dc:date>2016-09-13</dc:date>
<dc:identifier>doi:10.1101/074815</dc:identifier>
<dc:title><![CDATA[The Causal Effects of Education on Health, Mortality, Cognition, Well-being, and Income in the UK Biobank]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2016-09-13</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/070409v1?rss=1">
<title>
<![CDATA[
Association of AKAP6 and MIR2113 with cognitive performance in a population based sample of older adults 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/070409v1?rss=1"
</link>
<description><![CDATA[
Genetic factors make a substantial contribution to inter-individual variability in cognitive function. A recent meta-analysis of genome-wide association studies identified two loci, AKAP6 and MIR2113 that are associated with general cognitive function. Here, we extend this previous research by investigating the association of MIR2113 and AKAP6 with baseline and longitudinal nonlinear change across a broad spectrum of cognitive domains in community-based cohort of 1,570 older adults without dementia. Two SNPs, MIR211-rs10457441 and AKAP6-rs17522122 were genotyped in 1,570 non-demented older Australians of European ancestry, who were examined up to 4 times over 12 years. Linear mixed effects models were used to examine the association between AKAP6 and MIR2113 with cognitive performance in episodic memory, working memory, vocabulary, perceptual speed and reaction time at baseline and with linear and quadratic rates of change. AKAP6-rs17522122*T was associated with worse baseline performance in episodic memory, working memory, vocabulary and perceptual speed, but it was not associated with cognitive change in any domain. MIR2113-rs10457441*T was associated with accelerated decline in episodic memory. No other associations with baseline cognitive performance or with linear or quadratic rate or cognitive changes was observed for this SNP. These results confirm the previous finding that, AKAP6 is associated with performance across multiple cognitive domains at baseline but not with cognitive decline, while MIR2113 primarily affects the rate at which memory declines over time.
]]></description>
<dc:creator>Shea Andrews</dc:creator>
<dc:creator>Debjani Das</dc:creator>
<dc:creator>Kaarin J Anstey</dc:creator>
<dc:creator>Simon Easteal</dc:creator>
<dc:creator></dc:creator>
<dc:date>2016-08-19</dc:date>
<dc:identifier>doi:10.1101/070409</dc:identifier>
<dc:title><![CDATA[Association of AKAP6 and MIR2113 with cognitive performance in a population based sample of older adults]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2016-08-19</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/164848v1?rss=1">
<title>
<![CDATA[
Narrow-sense heritability estimation of complex traits using identity-by-descent information. 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/164848v1?rss=1"
</link>
<description><![CDATA[
Heritability is a fundamental parameter in genetics. Traditional estimates based on family or twin studies can be biased due to shared environmental or non-additive genetic variance. Alternatively, those based on genotyped or imputed variants typically underestimate narrow-sense heritability contributed by rare or otherwise poorly-tagged causal variants. Identical-by-descent (IBD) segments of the genome share all variants between pairs of chromosomes except new mutations that have arisen since the last common ancestor. Therefore, relating phenotypic similarity to degree of IBD sharing among classically unrelated individuals is an appealing approach to estimating the near full additive genetic variance while avoiding biases that can occur when modeling close relatives. We applied an IBD-based approach (GREML-IBD) to estimate heritability in unrelated individuals using phenotypic simulation with thousands of whole genome sequences across a range of stratification, polygenicity levels, and the minor allele frequencies of causal variants (CVs). IBD-based heritability estimates were unbiased when using unrelated individuals, even for traits with extremely rare CVs, but stratification led to strong biases in IBD-based heritability estimates with poor precision. We used data on two traits in ~120,000 people from the UK Biobank to demonstrate that, depending on the trait and possible confounding environmental effects, GREML-IBD can be applied successfully to very large genetic datasets to infer the contribution of very rare variants lost using other methods. However, we observed apparent biases in this real data that were not predicted from our simulation, suggesting that more work may be required to understand factors that influence IBD-based estimates.
]]></description>
<dc:creator>Evans, L.</dc:creator>
<dc:creator>Tahmasbi, R.</dc:creator>
<dc:creator>Jones, M.</dc:creator>
<dc:creator>Vrieze, S.</dc:creator>
<dc:creator>Abecasis, G.</dc:creator>
<dc:creator>Das, S.</dc:creator>
<dc:creator>Bjelland, D.</dc:creator>
<dc:creator>deCandia, T.</dc:creator>
<dc:creator>- Haplotype Reference Consortium,</dc:creator>
<dc:creator>Yang, J.</dc:creator>
<dc:creator>Goddard, M.</dc:creator>
<dc:creator>Visscher, P.</dc:creator>
<dc:creator>Keller, M.</dc:creator>
<dc:date>2017-07-17</dc:date>
<dc:identifier>doi:10.1101/164848</dc:identifier>
<dc:title><![CDATA[Narrow-sense heritability estimation of complex traits using identity-by-descent information.]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-07-17</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/184218v1?rss=1">
<title>
<![CDATA[
Investigating causality in associations between education and smoking: A two-sample Mendelian randomization study 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/184218v1?rss=1"
</link>
<description><![CDATA[
BackgroundLower educational attainment is associated with increased rates of smoking, but ascertaining causality is challenging. We used two-sample Mendelian randomization (MR) analyses of summary statistics to examine whether educational attainment is causally related to smoking.nnMethods and FindingsWe used summary statistics from genome-wide association studies of educational attainment and a range of smoking phenotypes (smoking initiation, cigarettes per day, cotinine levels and smoking cessation). Various complementary MR techniques (inverse-variance weighted regression, MR Egger, weighted-median regression) were used to test the robustness of our results. We found broadly consistent evidence across these techniques that higher educational attainment leads to reduced likelihood of smoking initiation, reduced heaviness of smoking among smokers (as measured via self-report and cotinine levels), and greater likelihood of smoking cessation among smokers.nnConclusionsOur findings indicate a causal association between low educational attainment and increased risk of smoking, and may explain the observational associations between educational attainment and adverse health outcomes such as risk of coronary heart disease.
]]></description>
<dc:creator>Gage, S. H.</dc:creator>
<dc:creator>Bowden, J.</dc:creator>
<dc:creator>Davey Smith, G.</dc:creator>
<dc:creator>Munafo, M. R.</dc:creator>
<dc:date>2017-09-07</dc:date>
<dc:identifier>doi:10.1101/184218</dc:identifier>
<dc:title><![CDATA[Investigating causality in associations between education and smoking: A two-sample Mendelian randomization study]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-09-07</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/223248v1?rss=1">
<title>
<![CDATA[
Circulating selenium and prostate cancer risk: a Mendelian randomization analysis 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/223248v1?rss=1"
</link>
<description><![CDATA[
In the Selenium and Vitamin E Cancer Prevention Trial (SELECT), selenium supplementation (causing a median 114 g/L increase in circulating selenium) did not lower overall prostate cancer risk, but increased risk of high-grade prostate cancer and type 2 diabetes. Mendelian randomization analysis uses genetic variants to proxy modifiable risk factors and can strengthen causal inference in observational studies. We constructed a genetic risk score comprising eleven single-nucleotide polymorphisms robustly (P<5x10-8) associated with circulating selenium in genome-wide association studies. In a Mendelian randomization analysis of 72,729 men in the PRACTICAL Consortium (44,825 cases, 27,904 controls), 114 g/L higher genetically-elevated circulating selenium was not associated with prostate cancer (OR: 1.01; 95% CI: 0.89-1.13). Concordant with findings from SELECT, selenium was weakly associated with advanced (including high-grade) prostate cancer (OR: 1.21; 95% CI: 0.98-1.49) and type 2 diabetes (OR: 1.18; 95% CI: 0.97-1.43; in a type 2 diabetes GWAS meta-analysis with up to 49,266 cases, 249,906 controls). Mendelian randomization mirrored the outcome of selenium supplementation in SELECT and may offer an approach for the prioritization of interventions for follow-up in large-scale randomized controlled trials.
]]></description>
<dc:creator>Yarmolinsky, J.</dc:creator>
<dc:creator>Bonilla, C.</dc:creator>
<dc:creator>Haycock, P. C.</dc:creator>
<dc:creator>Langdon, R. J.</dc:creator>
<dc:creator>Lotta, L. A.</dc:creator>
<dc:creator>Langenberg, C.</dc:creator>
<dc:creator>Relton, C. L.</dc:creator>
<dc:creator>Lewis, S. J.</dc:creator>
<dc:creator>Evans, D. M.</dc:creator>
<dc:creator>the PRACTICAL consortium,</dc:creator>
<dc:creator>Smith, G. D.</dc:creator>
<dc:creator>Martin, R. M.</dc:creator>
<dc:date>2017-11-21</dc:date>
<dc:identifier>doi:10.1101/223248</dc:identifier>
<dc:title><![CDATA[Circulating selenium and prostate cancer risk: a Mendelian randomization analysis]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-11-21</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/205435v1?rss=1">
<title>
<![CDATA[
Distinguishing genetic correlation from causation across 52 diseases and complex traits 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/205435v1?rss=1"
</link>
<description><![CDATA[
Mendelian randomization (MR) is widely used to identify causal relationships among heritable traits, but it can be confounded by genetic correlations reflecting shared etiology. We propose a model in which a latent causal variable mediates the genetic correlation between two traits. Under the latent causal variable (LCV) model, trait 1 is fully genetically causal for trait 2 if it is perfectly genetically correlated with the latent causal variable, implying that the entire genetic component of trait 1 is causal for trait 2; it is partially genetically causal for trait 2 if it has a high genetic correlation with the latent variable, implying that part of the genetic component of trait 1 is causal for trait 2. To quantify the degree of partial genetic causality, we define the genetic causality proportion (gcp). We fit this model using mixed fourth moments E([Formula]12) and E([Formula]12) of marginal effect sizes for each trait, exploiting the fact that if trait 1 is causal for trait 2 then SNPs affecting trait 1 (large [Formula]) will have correlated effects on trait 2 (large 12), but not vice versa. We performed simulations under a wide range of genetic architectures and determined that LCV, unlike state-of-the-art MR methods, produced well-calibrated false positive rates and reliable gcp estimates in the presence of genetic correlations and asymmetric genetic architectures; we also determined that LCV is well-powered to detect a causal effect. We applied LCV to GWAS summary statistics for 52 traits (average N=331k), identifying partially or fully genetically causal effects (1% FDR) for 59 pairs of traits, including 30 pairs of traits with high gcp estimates (g[c]p > 0.6). Results consistent with the published literature included genetically causal effects on myocardial infarction (MI) for LDL, triglycerides and BMI. Novel findings included a genetically causal effect of LDL on bone mineral density, consistent with clinical trials of statins in osteoporosis. These results demonstrate that it is possible to distinguish between genetic correlation and causation using genetic data.
]]></description>
<dc:creator>O'Connor, L.</dc:creator>
<dc:creator>Price, A. L.</dc:creator>
<dc:date>2017-10-18</dc:date>
<dc:identifier>doi:10.1101/205435</dc:identifier>
<dc:title><![CDATA[Distinguishing genetic correlation from causation across 52 diseases and complex traits]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-10-18</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/187849v1?rss=1">
<title>
<![CDATA[
Detecting and correcting for bias in Mendelian randomization analyses using gene-by-environment interactions 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/187849v1?rss=1"
</link>
<description><![CDATA[
BackgroundMendelian randomization has developed into an established method for strengthening causal inference and estimating causal effects, largely due to the proliferation of genome-wide association studies. However, genetic instruments remain controversial as pleiotropic effects can introduce bias into causal estimates. Recent work has highlighted the potential of gene-environment interactions in detecting and correcting for pleiotropic bias in Mendelian randomization analyses.nnMethodsWe introduce MR using Gene-by-Environment interactions (MRGxE) as a framework capable of identifying and correcting for pleiotropic bias, drawing upon developments in econometrics and epidemiology. If an instrument-covariate interaction induces variation in the association between a genetic instrument and exposure, it is possible to identify and correct for pleiotropic effects. The interpretation of MRGxE is similar to conventional summary Mendelian randomization approaches, with a particular advantage of MRGxE being the ability to assess the validity of an individual instrument.nnResultsWe investigate the effect of BMI upon systolic blood pressure (SBP) using data from the UK Biobank and the GIANT consortium using a single instrument (a weighted allelic score). We find MRGxE produces findings in agreement with MR Egger regression in a two-sample summary MR setting, however, association estimates obtained across all methods differ considerably when excluding related participants or individuals of non-European ancestry. This could be a consequence of selection bias, though there is also potential for introducing bias by using a mixed ancestry population. Further, we assess the performance of MRGxE with respect to identifying and correcting for horizontal pleiotropy in a simulation setting, highlighting the utility of the approach even when the MRGxE assumptions are violated.nnConclusionsBy utilising instrument-covariate interactions within a linear regression framework, it is possible to identify and correct for pleiotropic bias, provided the average magnitude of pleiotropy is constant across interaction covariate subgroups.nnO_TEXTBOXKey MessagesO_LIInstrument-covariate interactions can be used to identify pleiotropic bias in Mendelian randomization analyses, provided they induce sufficient variation in the association between the genetic instrument and exposure.nC_LIO_LIBy regressing the gene-outcome association upon the gene-exposure association across interaction covariate subgroups, it is possible to obtain an estimate of the average pleiotropic effect and a causal effect estimate.nC_LIO_LIThe interpretation of MRGxE is analogous to that of MR-Egger regression.nC_LIO_LIThe approach serves as a valuable test for directional pleiotropy and can be used to inform instrument selection.nC_LInnC_TEXTBOX
]]></description>
<dc:creator>Spiller, W.</dc:creator>
<dc:creator>Slichter, D.</dc:creator>
<dc:creator>Bowden, J.</dc:creator>
<dc:creator>Davey Smith, G.</dc:creator>
<dc:date>2017-09-15</dc:date>
<dc:identifier>doi:10.1101/187849</dc:identifier>
<dc:title><![CDATA[Detecting and correcting for bias in Mendelian randomization analyses using gene-by-environment interactions]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-09-15</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/194290v1?rss=1">
<title>
<![CDATA[
Genome-wide interaction study of stress-sensitivity and its prediction of major depressive disorder in UK Biobank and Generation Scotland 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/194290v1?rss=1"
</link>
<description><![CDATA[
Individual response to stress is correlated with neuroticism and is an important predictor of both neuroticism and the onset of major depressive disorder (MDD). Identification of the genetics underpinning individual differences in response to negative events (stress-sensitivity) may improve our understanding of the molecular pathways involved, and its association with stress-related illnesses. We sought to generate a proxy for stress-sensitivity through modelling the interaction between SNP allele and MDD status on neuroticism score in order to identify genetic variants that contribute to the higher neuroticism seen in individuals with a lifetime diagnosis of depression compared to unaffected individuals. Meta-analysis of genome-wide interaction studies (GWIS) in UK Biobank (N = 23,092) and Generation Scotland: Scottish Family Health Study (N = 7,155) identified no genome-wide significance SNP interactions. However, gene-based tests identified a genome-wide significant gene, ZNF366, a negative regulator of glucocorticoid receptor function implicated in alcohol dependence (p = 1.48x10-7; Bonferroni-corrected significance threshold p < 2.79x10-6). Using summary statistics from the stress-sensitivity term of the GWIS, SNP heritability for stress-sensitivity was estimated at 5.0%. In models fitting polygenic risk scores of both MDD and neuroticism derived from independent GWAS, we show that polygenic risk scores derived from the UK Biobank stress-sensitivity GWIS significantly improved the prediction of MDD in Generation Scotland. This study may improve interpretation of larger genome-wide association studies of MDD and other stress-related illnesses, and the understanding of the etiological mechanisms underpinning stress-sensitivity.
]]></description>
<dc:creator>Arnau Soler, A.</dc:creator>
<dc:creator>Thomson, P.</dc:creator>
<dc:creator>Adams, M.</dc:creator>
<dc:creator>Generation Scotland,</dc:creator>
<dc:creator>Hayward, C.</dc:creator>
<dc:date>2017-09-27</dc:date>
<dc:identifier>doi:10.1101/194290</dc:identifier>
<dc:title><![CDATA[Genome-wide interaction study of stress-sensitivity and its prediction of major depressive disorder in UK Biobank and Generation Scotland]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-09-27</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/134197v1?rss=1">
<title>
<![CDATA[
Genetic Instrumental Variable (GIV) Regression: Explaining Socioeconomic And Health Outcomes In Non-Experimental Data 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/134197v1?rss=1"
</link>
<description><![CDATA[
Identifying causal effects in non-experimental data is an enduring challenge. One proposed solution that recently gained popularity is the idea to use genes as instrumental variables (i.e. Mendelian Randomization - MR). However, this approach is problematic because many variables of interest are genetically correlated, which implies the possibility that many genes could affect both the exposure and the outcome directly or via unobserved confounding factors. Thus, pleiotropic effects of genes are themselves a source of bias in non-experimental data that would also undermine the ability of MR to correct for endogeneity bias from non-genetic sources. Here, we propose an alternative approach, GIV regression, that provides estimates for the effect of an exposure on an outcome in the presence of pleiotropy. As a valuable byproduct, GIV regression also provides accurate estimates of the chip heritability of the outcome variable. GIV regression uses polygenic scores (PGS) for the outcome of interest which can be constructed from genome-wide association study (GWAS) results. By splitting the GWAS sample for the outcome into non-overlapping subsamples, we obtain multiple indicators of the outcome PGS that can be used as instruments for each other, and, in combination with other methods such as sibling fixed effects, can address endogeneity bias from both pleiotropy and the environment. In two empirical applications, we demonstrate that our approach produces reasonable estimates of the chip heritability of educational attainment (EA) and show that standard regression and MR provide upwardly biased estimates of the effect of body height on EA.
]]></description>
<dc:creator>DiPrete, T. A.</dc:creator>
<dc:creator>Burik, C.</dc:creator>
<dc:creator>Koellinger, P.</dc:creator>
<dc:date>2017-05-05</dc:date>
<dc:identifier>doi:10.1101/134197</dc:identifier>
<dc:title><![CDATA[Genetic Instrumental Variable (GIV) Regression: Explaining Socioeconomic And Health Outcomes In Non-Experimental Data]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-05-05</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/127993v1?rss=1">
<title>
<![CDATA[
The causal effect of educational attainment on Alzheimer’s disease: A two-sample Mendelian randomization study 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/127993v1?rss=1"
</link>
<description><![CDATA[
BackgroundObservational evidence suggests that higher educational attainment is protective for Alzheimers disease (AD). It is unclear whether this association is causal or confounded by demographic and socioeconomic characteristics. We examined the causal effect of educational attainment on AD in a two-sample MR framework.nnMethodsWe extracted all available effect estimates of the 74 single nucleotide polymorphisms (SNPs) associated with years of schooling from the largest genome-wide association study (GWAS) of educational attainment (N=293,723) and the GWAS of AD conducted by the International Genomics of Alzheimers Project (n=17,008 AD cases and 37,154 controls). SNP-exposure and SNP-outcome coefficients were combined using an inverse variance weighted approach, providing an estimate of the causal effect of each SD increase in years of schooling on AD. We also performed appropriate sensitivity analyses examining the robustness of causal effect estimates to the various assumptions and conducted simulation analyses to examine potential survival bias of MR analyses.nnFindingsWith each SD increase in years of schooling (3.51 years), the odds of AD were, on average, reduced by approximately one third (odds ratio= 0.63, 95% confidence interval [CI]: 0.48 to 0.83, p<0.001). Causal effect estimates were consistent when using causal methods with varying MR assumptions or different sets of SNPs for educational attainment, lending confidence to the magnitude and direction of effect in our main findings. There was also no evidence of survival bias in our study.nnInterpretationOur findings support a causal role of educational attainment on AD, whereby an additional [~]3.5 years of schooling reduces the odds of AD by approximately one third.
]]></description>
<dc:creator>Anderson, E.</dc:creator>
<dc:creator>Wade, K. H.</dc:creator>
<dc:creator>Hemani, G.</dc:creator>
<dc:creator>Bowden, J.</dc:creator>
<dc:creator>Korologou-Linden, R.</dc:creator>
<dc:creator>Davey Smith, G.</dc:creator>
<dc:creator>Ben-Shlomo, Y.</dc:creator>
<dc:creator>Howe, L. D.</dc:creator>
<dc:creator>Stergiakouli, E.</dc:creator>
<dc:date>2017-04-17</dc:date>
<dc:identifier>doi:10.1101/127993</dc:identifier>
<dc:title><![CDATA[The causal effect of educational attainment on Alzheimer’s disease: A two-sample Mendelian randomization study]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-04-17</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/167577v1?rss=1">
<title>
<![CDATA[
Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/167577v1?rss=1"
</link>
<description><![CDATA[
Major depressive disorder (MDD) is a notably complex illness with a lifetime prevalence of 14%.1 It is often chronic or recurrent and is thus accompanied by considerable morbidity, excess mortality, substantial costs, and heightened risk of suicide.2-7 MDD is a major cause of disability worldwide.8 We conducted a genome-wide association (GWA) meta-analysis in 130,664 MDD cases and 330,470 controls, and identified 44 independent loci that met criteria for statistical significance. We present extensive analyses of these results which provide new insights into the nature of MDD. The genetic findings were associated with clinical features of MDD, and implicated prefrontal and anterior cingulate cortex in the pathophysiology of MDD (regions exhibiting anatomical differences between MDD cases and controls). Genes that are targets of antidepressant medications were strongly enriched for MDD association signals (P=8.5x10-10), suggesting the relevance of these findings for improved pharmacotherapy of MDD. Sets of genes involved in gene splicing and in creating isoforms were also enriched for smaller MDD GWA P-values, and these gene sets have also been implicated in schizophrenia and autism. Genetic risk for MDD was correlated with that for many adult and childhood onset psychiatric disorders. Our analyses suggested important relations of genetic risk for MDD with educational attainment, body mass, and schizophrenia: the genetic basis of lower educational attainment and higher body mass were putatively causal for MDD whereas MDD and schizophrenia reflected a partly shared biological etiology. All humans carry lesser or greater numbers of genetic risk factors for MDD, and a continuous measure of risk underlies the observed clinical phenotype. MDD is not a distinct entity that neatly demarcates normalcy from pathology but rather a useful clinical construct associated with a range of adverse outcomes and the end result of a complex process of intertwined genetic and environmental effects. These findings help refine and define the fundamental basis of MDD.
]]></description>
<dc:creator>- Major Depressive Disorder Working Group of the PGC,</dc:creator>
<dc:creator>Wray, N. R.</dc:creator>
<dc:creator>Sullivan, P. F.</dc:creator>
<dc:date>2017-07-24</dc:date>
<dc:identifier>doi:10.1101/167577</dc:identifier>
<dc:title><![CDATA[Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-07-24</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/208629v1?rss=1">
<title>
<![CDATA[
Birthweight, Type 2 Diabetes and Cardiovascular Disease: Addressing the Barker Hypothesis with Mendelian randomization 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/208629v1?rss=1"
</link>
<description><![CDATA[
BackgroundLow birthweight (BW) has been associated with a higher risk of hypertension, type 2 diabetes (T2D) and cardiovascular disease (CVD) in epidemiological studies. The Barker hypothesis posits that intrauterine growth restriction resulting in lower BW is causal for these diseases, but causality and mechanisms are difficult to infer from observational studies. Mendelian randomization (MR) is a new tool to address this important question.nnMethodsWe performed regression analyses to assess associations of self-reported BW with CVD and T2D in 237,631 individuals from the UK Biobank, a large population-based cohort study aged 40-69 years recruited across UK in 2006-2010. Further, we assessed the causal relationship of such associations using the two- sample MR approach, estimating the causal effect by contrasting the SNP effects on the exposure with the SNP effects on the outcome using independent publicly available genome-wide association datasets.nnResultsIn the observational analyses, BW showed strong inverse associations with systolic and diastolic blood pressure ({beta}, -0.83 and -0.26; per raw unit in outcomes and SD change in BW; 95% CI, -0.90, -0.75 and -0.31, -0.22, respectively), T2D (odds ratio [OR], 0.83; 95% CI, 0.79, 0.87), lipid-lowering treatment (OR, 0.84; 95% CI, 0.81, 0.86) and CAD (hazard ratio [HR] 0.85; 95% CI, 0.78, 0.94); while the associations with adult body mass index (BMI) and body fat ({beta}, 0.04 and 0.02; per SD change in outcomes and BW; 95% CI, 0.03, 0.04 and 0.01, 0.02, respectively) were positive. The MR analyses indicated inverse causal associations of BW with low density lipoprotein cholesterol, 2-hour glucose, CAD and T2D, and positive causal association with BMI; but no associations with blood pressure. Sensitivity analyses and robust MR methods provided consistent results and indicated no horizontal pleiotropy.nnConclusionOur study indicates that lower BW is causally and directly related with increased susceptibility to CAD and T2D in adulthood. This causal relationship is not mediated by adult obesity or hypertension.
]]></description>
<dc:creator>Zanetti, D.</dc:creator>
<dc:creator>Tikkanen, E.</dc:creator>
<dc:creator>Gustafsson, S.</dc:creator>
<dc:creator>Priest, J. R.</dc:creator>
<dc:creator>Burgess, S.</dc:creator>
<dc:creator>Ingelsson, E.</dc:creator>
<dc:date>2017-10-25</dc:date>
<dc:identifier>doi:10.1101/208629</dc:identifier>
<dc:title><![CDATA[Birthweight, Type 2 Diabetes and Cardiovascular Disease: Addressing the Barker Hypothesis with Mendelian randomization]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-10-25</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/032789v1?rss=1">
<title>
<![CDATA[
Genetic Associations with Subjective Well-Being Also Implicate Depression and Neuroticism 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/032789v1?rss=1"
</link>
<description><![CDATA[
We conducted a genome-wide association study of subjective well-being (SWB) in 298,420 individuals. We also performed auxiliary analyses of depressive symptoms ("DS"; N = 161,460) and neuroticism (N = 170,910), both of which have a substantial genetic correlation with SWB [Formula]. We identify three SNPs associated with SWB at genome-wide significance. Two of them are significantly associated with DS in an independent sample. In our auxiliary analyses, we identify 13 additional genome-wide-significant associations: two with DS and eleven with neuroticism, including two inversion polymorphisms. Across our phenotypes, loci regulating expression in central nervous system and adrenal/pancreas tissues are enriched. The discovery of genetic loci associated with the three phenotypes we study has proven elusive; our findings illustrate the payoffs from studying them jointly.nnOne Sentence Summary: Using both genome-wide association studies and proxy-phenotype studies, we identify genetic variants associated with subjective well-being, depressive symptoms, and neuroticism.
]]></description>
<dc:creator>Aysu Okbay</dc:creator>
<dc:creator>Bart M. L. Baselmans</dc:creator>
<dc:creator>Jan-Emmanuel De Neve</dc:creator>
<dc:creator>Patrick Turley</dc:creator>
<dc:creator>Michel G. Nivard</dc:creator>
<dc:creator>Mark A. Fontana</dc:creator>
<dc:creator>Fleur S. W. Meddens</dc:creator>
<dc:creator>Richard Karlsson Linnér</dc:creator>
<dc:creator>Cornelius A. Rietveld</dc:creator>
<dc:creator>Jaime Derringer</dc:creator>
<dc:creator>Jacob Gratten</dc:creator>
<dc:creator>James J. Lee</dc:creator>
<dc:creator>Jimmy Z. Liu</dc:creator>
<dc:creator>Ronald de Vlaming</dc:creator>
<dc:creator>Dalton C. Conley</dc:creator>
<dc:creator>George Davey Smith</dc:creator>
<dc:creator>Albert Hofman</dc:creator>
<dc:creator>Magnus Johannesson</dc:creator>
<dc:creator>David I. Laibson</dc:creator>
<dc:creator>Sarah E. Medland</dc:creator>
<dc:creator>Michelle N. Meyer</dc:creator>
<dc:creator>Joseph K. Pickrell</dc:creator>
<dc:creator>Tonu Esko</dc:creator>
<dc:creator>Robert F. Krueger</dc:creator>
<dc:creator>Jonathan P. Beauchamp</dc:creator>
<dc:creator>Philipp D. Koellinger</dc:creator>
<dc:creator>Daniel J. Benjamin</dc:creator>
<dc:creator>Meike Bartels</dc:creator>
<dc:creator>David Cesarini</dc:creator>
<dc:creator>Social Science Genetic Association Consortium</dc:creator>
<dc:creator></dc:creator>
<dc:date>2015-11-24</dc:date>
<dc:identifier>doi:10.1101/032789</dc:identifier>
<dc:title><![CDATA[Genetic Associations with Subjective Well-Being Also Implicate Depression and Neuroticism]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2015-11-24</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/082024v1?rss=1">
<title>
<![CDATA[
Linkage disequilibrium dependent architecture of human complex traits reveals action of negative selection 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/082024v1?rss=1"
</link>
<description><![CDATA[
Recent work has hinted at the linkage disequilibrium (LD) dependent architecture of human complex traits, where SNPs with low levels of LD (LLD) have larger per-SNP heritability after conditioning on their minor allele frequency (MAF). However, this has not been formally assessed, quantified or biologically interpreted. Here, we analyzed summary statistics from 56 complex diseases and traits (average N = 101,401) by extending stratified LD score regression to continuous annotations. We determined that SNPs with low LLD have significantly larger per-SNP heritability. Roughly half of the LLD signal can be explained by functional annotations that are negatively correlated with LLD, such as DNase I hypersensitivity sites (DHS). The remaining signal is largely driven by our finding that common variants that are more recent tend to have lower LLD and to explain more heritability (P = 2.38 x 10-104); the youngest 20% of common SNPs explain 3.9x more heritability than the oldest 20%, consistent with the action of negative selection. We also inferred jointly significant effects of other LD-related annotations and confirmed via forward simulations that these annotations jointly predict deleterious effects. Our results are consistent with the action of negative selection on deleterious variants that affect complex traits, complementing efforts to learn about negative selection by analyzing much smaller rare variant data sets.
]]></description>
<dc:creator>Gazal, S.</dc:creator>
<dc:creator>Finucane, H.</dc:creator>
<dc:creator>Furlotte, N. A.</dc:creator>
<dc:creator>Loh, P.-R.</dc:creator>
<dc:creator>Palamara, P. F.</dc:creator>
<dc:creator>Liu, X.</dc:creator>
<dc:creator>Schoech, A.</dc:creator>
<dc:creator>Bulik-Sullivan, B.</dc:creator>
<dc:creator>Neale, B. M.</dc:creator>
<dc:creator>Gusev, A.</dc:creator>
<dc:creator>Price, A. L.</dc:creator>
<dc:date>2016-10-19</dc:date>
<dc:identifier>doi:10.1101/082024</dc:identifier>
<dc:title><![CDATA[Linkage disequilibrium dependent architecture of human complex traits reveals action of negative selection]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2016-10-19</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/190165v1?rss=1">
<title>
<![CDATA[
Association between alcohol consumption and Alzheimer’s disease: A Mendelian Randomization Study 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/190165v1?rss=1"
</link>
<description><![CDATA[
INTRODUCTIONObservational studies have suggested that light-moderate alcohol consumptions decreases the risk of Alzheimers disease, but it is unclear if this association is causal.nnMETHODSTwo-sample Mendelian randomization (MR) analysis was used to examine whether alcohol consumption, alcohol dependence or Alcohol Use Disorder Identification Test (AUDIT) scores were causally associated with the risk of Late Onset Alzheimers disease (LOAD) or Alzheimers disease age of onset survival (AAOS). Additionally, {gamma}-glutamyltransferase levels were included as a positive control.nnRESULTSThere was no evidence of a causal association between alcohol consumption, alcohol dependence or AUDIT and LOAD. Alcohol consumption was associated with an earlier AAOS and increased {gamma}-glutamyltransferase blood concentrations. Alcohol dependence was associated with a delayed AAOS.nnDISCUSSIONMR found robust evidence of a causal association between alcohol consumption and an earlier AAOS, but not alcohol intake and LOAD risk. The protective effect of alcohol dependence is potentially due to survivor bias.nnResearch in ContextO_ST_ABSSystematic ReviewC_ST_ABSThe authors reviewed the literature using online databases (e.g. PubMed). Previous research links light-moderate alcohol consumption to a decreased risk of Alzheimers disease (AD), however, prior studies based on observational study designs may be biased due to unmeasured confounders influencing both alcohol consumption and AD risk.nnInterpretationWe used a two-sample Mendelian randomization (MR) approach to evaluated the causal relationship between alcohol intake and AD. MR uses genetic variants as proxies for environmental exposures to provide an estimate of the causal association between an intermediate exposure and a disease outcome. MR found evidence of a causal association between alcohol consumption and an earlier AD age of onset, suggesting that light-moderate alcohol consumption does not reduce risk of Alzheimers disease.nnFuture DirectionsFuture studies should use alterative study designs and account for additional confounders when evaluating the causal relationship between alcohol consumption and AD.nnHighlightsO_LIWe evaluated causal relationships between alcohol intake and Alzheimers diseasenC_LIO_LIAlcohol consumption is causally associated with an earlier Alzheimers age of onsetnC_LIO_LINo evidence of causal assocations between alcohol intake and Alzheimers risknC_LI
]]></description>
<dc:creator>Andrews, S. J.</dc:creator>
<dc:creator>Anstey, K. J.</dc:creator>
<dc:date>2017-09-18</dc:date>
<dc:identifier>doi:10.1101/190165</dc:identifier>
<dc:title><![CDATA[Association between alcohol consumption and Alzheimer’s disease: A Mendelian Randomization Study]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-09-18</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/233825v1?rss=1">
<title>
<![CDATA[
The complement system supports normal postnatal development and gonadal function in both sexes 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/233825v1?rss=1"
</link>
<description><![CDATA[
Male and female infertility are clinically managed and classified as distinct diseases, and relatively little is known about mechanisms of gonadal function common to both sexes. We used genome-wide genetic analysis on 74,896 women and men to find rare genetic variants that modulate gonadal function in both sexes. This uncovered an association with variants disrupting CSMD1, a complement regulatory protein located on 8p23, in a genomic region with an exceptional evolution. We found that Csmd1 knockout mice display a diverse array of gonadal defects in both sexes, and in females, impaired mammary gland development that leads to increased offspring mortality. The complement pathway is significantly disrupted in Csmd1 mice, and further disruption of the complement pathway from joint inactivation of C3 leads to more extreme reproductive defects. Our results can explain a novel human genetic association with infertility and implicate the complement system in the normal development of postnatal tissues.
]]></description>
<dc:creator>Conrad, D.</dc:creator>
<dc:creator>Lee, A.</dc:creator>
<dc:creator>Rusch, J.</dc:creator>
<dc:creator>Usmani, A.</dc:creator>
<dc:creator>Lima, A.</dc:creator>
<dc:creator>Wong, W.</dc:creator>
<dc:creator>Huang, N.</dc:creator>
<dc:creator>Lepamets, M.</dc:creator>
<dc:creator>Vigh-Conrad, K.</dc:creator>
<dc:creator>Worthington, R.</dc:creator>
<dc:creator>Magi, R.</dc:creator>
<dc:creator>Niederhuber, J.</dc:creator>
<dc:creator>Wu, X.</dc:creator>
<dc:creator>Atkinson, J.</dc:creator>
<dc:creator>Hess, R.</dc:creator>
<dc:date>2017-12-13</dc:date>
<dc:identifier>doi:10.1101/233825</dc:identifier>
<dc:title><![CDATA[The complement system supports normal postnatal development and gonadal function in both sexes]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-12-13</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/184234v1?rss=1">
<title>
<![CDATA[
Effect modification of FADS2 polymorphisms on the association between breastfeeding and intelligence: results from a collaborative meta-analysis 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/184234v1?rss=1"
</link>
<description><![CDATA[
BackgroundAccumulating evidence suggests that breastfeeding benefits the childrens intelligence. Long-chain polyunsaturated fatty acids (LC-PUFAs) present in breast milk may explain part of this association. Under a nutritional adequacy hypothesis, an interaction between breastfeeding and genetic variants associated with endogenous LC-PUFAs synthesis might be expected. However, the literature on this topic is controversial.nnMethods and FindingsWe investigated this GenexEnvironment interaction in a de novo meta-analysis involving >12,000 individuals in the primary analysis, and >45,000 individuals in a secondary analysis using relaxed inclusion criteria. Our primary analysis used ever breastfeeding, FADS2 polymorphisms rs174575 and rs1535 coded assuming a recessive effect of the G allele, and intelligence quotient (IQ) in Z scores. Using random effects meta-analysis, ever breastfeeding was associated with 0.17 (95% CI: 0.03; 0.32) higher Z scores in IQ, or about 2.1 points. There was no strong evidence of interaction, with pooled covariate-adjusted interaction coefficients (i.e., difference between genetic groups of the difference in IQZ scores comparing ever with never breastfed individuals) of 0.12 (95% CI: -0.19; 0.43) and 0.06 (95% CI: -0.16; 0.27) for the rs174575 and rs1535 variants, respectively. Secondary analyses corroborated these results. In studies with >5.85 and <5.85 months of breastfeeding duration, pooled estimates for the rs174575 variant were 0.50 (95% CI: -0.06; 1.06) and 0.14 (95% CI: -0.10; 0.38), respectively, and 0.27 (95% CI: -0.28; 0.82) and -0.01 (95% CI: -0.19; 0.16) for the rs1535 variant. However, between-group comparisons were underpowered.nnConclusionsOur findings do not support an interaction between ever breastfeeding and FADS2 polymorphisms. However, our subgroup analysis raises the possibility that breastfeeding supplies LC-PUFAs requirements for cognitive development (if such threshold exists) if it lasts for some (currently unknown) time. Future studies in large individual-level datasets would allow properly powered subgroup analyses and would improve our understanding on the role of breastfeeding duration in the breastfeedingxFADS2 interaction.
]]></description>
<dc:creator>Hartwig, F. P.</dc:creator>
<dc:creator>Davies, N. M.</dc:creator>
<dc:creator>Horta, B. L.</dc:creator>
<dc:creator>Ahluwalia, T. S.</dc:creator>
<dc:creator>Bisgaard, H.</dc:creator>
<dc:creator>Bonnelykke, K.</dc:creator>
<dc:creator>Caspi, A.</dc:creator>
<dc:creator>Moffitt, T.</dc:creator>
<dc:creator>Poulton, R.</dc:creator>
<dc:creator>Sajjad, A.</dc:creator>
<dc:creator>Tiemeier, H. W.</dc:creator>
<dc:creator>Dalmau Bueno, A.</dc:creator>
<dc:creator>Guxens, M.</dc:creator>
<dc:creator>Bustamante Pineda, M.</dc:creator>
<dc:creator>Santa-Marina, L.</dc:creator>
<dc:creator>Parker, N.</dc:creator>
<dc:creator>Paus, T.</dc:creator>
<dc:creator>Pausova, Z.</dc:creator>
<dc:creator>Lauritzen, L.</dc:creator>
<dc:creator>Schnurr, T. M.</dc:creator>
<dc:creator>Michaelsen, K. F.</dc:creator>
<dc:creator>Hansen, T.</dc:creator>
<dc:creator>Oddy, W.</dc:creator>
<dc:creator>Pennell, C. E.</dc:creator>
<dc:creator>Warrington, N. M.</dc:creator>
<dc:creator>Davey Smith, G.</dc:creator>
<dc:creator>Victora, C. G.</dc:creator>
<dc:date>2017-09-07</dc:date>
<dc:identifier>doi:10.1101/184234</dc:identifier>
<dc:title><![CDATA[Effect modification of FADS2 polymorphisms on the association between breastfeeding and intelligence: results from a collaborative meta-analysis]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-09-07</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/195933v1?rss=1">
<title>
<![CDATA[
Genome-wide association analysis identifies 26 novel loci for asthma, hay fever and eczema 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/195933v1?rss=1"
</link>
<description><![CDATA[
The disease risk for asthma, hay fever and eczema include both environmental and genetic risk factors and the comorbidity between the diseases are large. Heritability estimates suggest that the risk of asthma, hay fever and eczema is largely due to genetic factors. In this GWAS, we include 346,545 Caucasian participants from the UK Biobank to increase power to identify novel loci for asthma, hay fever and eczema. We also investigate if associated lead SNPs have a significantly larger effect for one disease phenotype compared to the other phenotypes, to highlight possible disease specific effects.nnThis study identifies 141 loci, of which 41 are novel to this study, to be associated (P[&le;]3x10-8) with asthma, hay fever or eczema, analysed separately or combined as a single phenotype. At four of the novel loci, TNFRSF8, MYRF, TSPAN8, and BHMG1, the lead SNPs were in LD (> 0.8) with potentially casual missense variants. For seven of the novel GWAS loci, the lead SNP was in LD (> 0.8) with genetic variants associated with gene expression (eQTL) where, for example, increased levels of TMEM258 as well as decreased levels of HHEX and ADAM19 was associated with decreased odds for asthma.nnOur study shows that a large amount of the genetic contribution to asthma, hay fever and eczema is shared between the diseases. Nonetheless, a number of SNPs have a significantly larger effect on one of the phenotypes suggesting that part of the genetic contribution is more phenotype specific.
]]></description>
<dc:creator>Ek, W. E.</dc:creator>
<dc:creator>Rask-Andersen, M.</dc:creator>
<dc:creator>Karlsson, T.</dc:creator>
<dc:creator>Johansson, A.</dc:creator>
<dc:date>2017-09-29</dc:date>
<dc:identifier>doi:10.1101/195933</dc:identifier>
<dc:title><![CDATA[Genome-wide association analysis identifies 26 novel loci for asthma, hay fever and eczema]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-09-29</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/173831v1?rss=1">
<title>
<![CDATA[
Genetic Architecture of Subcortical Brain Structures in Over 40,000 Individuals Worldwide 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/173831v1?rss=1"
</link>
<description><![CDATA[
Subcortical brain structures are integral to motion, consciousness, emotions, and learning. We identified common genetic variation related to the volumes of nucleus accumbens, amygdala, brainstem, caudate nucleus, globus pallidus, putamen, and thalamus, using genome-wide association analyses in over 40,000 individuals from CHARGE, ENIGMA and the UK-Biobank. We show that variability in subcortical volumes is heritable, and identify 25 significantly associated loci (20 novel). Annotation of these loci utilizing gene expression, methylation, and neuropathological data identified 62 candidate genes implicated in neurodevelopment, synaptic signaling, axonal transport, apoptosis, and susceptibility to neurological disorders. This set of genes is significantly enriched for Drosophila orthologs associated with neurodevelopmental phenotypes, suggesting evolutionarily conserved mechanisms. Our findings uncover novel biology and potential drug targets underlying brain development and disease.
]]></description>
<dc:creator>Satizabal, C. L.</dc:creator>
<dc:creator>Adams, H. H. H.</dc:creator>
<dc:creator>Hibar, D. P.</dc:creator>
<dc:creator>White, C. C.</dc:creator>
<dc:creator>Stein, J. L.</dc:creator>
<dc:creator>Scholz, M.</dc:creator>
<dc:creator>Sargurupremraj, M.</dc:creator>
<dc:creator>Jahanshad, N.</dc:creator>
<dc:creator>Smith, A. V.</dc:creator>
<dc:creator>Bis, J. C.</dc:creator>
<dc:creator>Jian, X.</dc:creator>
<dc:creator>Luciano, M.</dc:creator>
<dc:creator>Hofer, E.</dc:creator>
<dc:creator>Teumer, A.</dc:creator>
<dc:creator>van der Lee, S. J.</dc:creator>
<dc:creator>Yang, J.</dc:creator>
<dc:creator>Yanek, L. R.</dc:creator>
<dc:creator>Lee, T. V.</dc:creator>
<dc:creator>Li, S.</dc:creator>
<dc:creator>Hu, Y.</dc:creator>
<dc:creator>Koh, J. Y.</dc:creator>
<dc:creator>Eicher, J. D.</dc:creator>
<dc:creator>Desrivieres, S.</dc:creator>
<dc:creator>Arias-Vasquez, A.</dc:creator>
<dc:creator>Chauhan, G.</dc:creator>
<dc:creator>Athanasiu, L.</dc:creator>
<dc:creator>Renteria, M. E.</dc:creator>
<dc:creator>Kim, S.</dc:creator>
<dc:creator>Hohn, D.</dc:creator>
<dc:creator>Armstrong, N. J.</dc:creator>
<dc:creator>Chen, Q.</dc:creator>
<dc:creator>Holmes, A. J.</dc:creator>
<dc:creator>den Braber, A.</dc:creator>
<dc:creator>Kloszewska, I.</dc:creator>
<dc:creator>Andersson, M.</dc:creator>
<dc:creator>Espeseth, T.</dc:creator>
<dc:creator>Grimm, O.</dc:creator>
<dc:creator>Abramovic, L.</dc:creator>
<dc:creator>Alhusaini, S.</dc:creator>
<dc:creator>Milaneschi, Y.</dc:creator>
<dc:creator>Papmeyer, M.</dc:creator>
<dc:creator>Axelsson, T.</dc:creator>
<dc:creator>Ehrlich, S.</dc:creator>
<dc:creator>Roi</dc:creator>
<dc:date>2017-08-28</dc:date>
<dc:identifier>doi:10.1101/173831</dc:identifier>
<dc:title><![CDATA[Genetic Architecture of Subcortical Brain Structures in Over 40,000 Individuals Worldwide]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-08-28</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/184853v1?rss=1">
<title>
<![CDATA[
GWAS meta-analysis (N=279,930) identifies new genes and functional links to intelligence 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/184853v1?rss=1"
</link>
<description><![CDATA[
Intelligence is highly heritable1 and a major determinant of human health and well-being2. Recent genome-wide meta-analyses have identified 24 genomic loci linked to intelligence3-7, but much about its genetic underpinnings remains to be discovered. Here, we present the largest genetic association study of intelligence to date (N=279,930), identifying 206 genomic loci (191 novel) and implicating 1,041 genes (963 novel) via positional mapping, expression quantitative trait locus (eQTL) mapping, chromatin interaction mapping, and gene-based association analysis. We find enrichment of genetic effects in conserved and coding regions and identify 89 nonsynonymous exonic variants. Associated genes are strongly expressed in the brain and specifically in striatal medium spiny neurons and cortical and hippocampal pyramidal neurons. Gene-set analyses implicate pathways related to neurogenesis, neuron differentiation and synaptic structure. We confirm previous strong genetic correlations with several neuropsychiatric disorders, and Mendelian Randomization results suggest protective effects of intelligence for Alzheimers dementia and ADHD, and bidirectional causation with strong pleiotropy for schizophrenia. These results are a major step forward in understanding the neurobiology of intelligence as well as genetically associated neuropsychiatric traits.
]]></description>
<dc:creator>Savage, J. E.</dc:creator>
<dc:creator>Jansen, P. R.</dc:creator>
<dc:creator>Stringer, S.</dc:creator>
<dc:creator>Watanabe, K.</dc:creator>
<dc:creator>Bryois, J.</dc:creator>
<dc:creator>de Leeuw, C. A.</dc:creator>
<dc:creator>Nagel, M.</dc:creator>
<dc:creator>Awasthi, S.</dc:creator>
<dc:creator>Barr, P. B.</dc:creator>
<dc:creator>Coleman, J. R. I.</dc:creator>
<dc:creator>Grasby, K. L.</dc:creator>
<dc:creator>Hammerschlag, A. R.</dc:creator>
<dc:creator>Kaminski, J.</dc:creator>
<dc:creator>Karlsson, R.</dc:creator>
<dc:creator>Krapohl, E.</dc:creator>
<dc:creator>Lam, M.</dc:creator>
<dc:creator>Nygaard, M.</dc:creator>
<dc:creator>Reynolds, C. A.</dc:creator>
<dc:creator>Trampush, J. W.</dc:creator>
<dc:creator>Young, H.</dc:creator>
<dc:creator>Zabaneh, D.</dc:creator>
<dc:creator>Hägg, S.</dc:creator>
<dc:creator>Hansell, N. K.</dc:creator>
<dc:creator>Karlsson, I. K.</dc:creator>
<dc:creator>Linnarsson, S.</dc:creator>
<dc:creator>Montgomery, G. W.</dc:creator>
<dc:creator>Munoz-Manchado, A. B.</dc:creator>
<dc:creator>Quinlan, E. B.</dc:creator>
<dc:creator>Schumann, G.</dc:creator>
<dc:creator>Skene, N.</dc:creator>
<dc:creator>Webb, B. T.</dc:creator>
<dc:creator>White, T.</dc:creator>
<dc:creator>Arking, D. E.</dc:creator>
<dc:creator>Attix, D. K.</dc:creator>
<dc:creator>Avramopoulos, D.</dc:creator>
<dc:creator>Bilder, R. M.</dc:creator>
<dc:creator>Bitsios, P.</dc:creator>
<dc:creator>Burdick, K. E.</dc:creator>
<dc:creator>Cannon, T. D.</dc:creator>
<dc:creator>Chiba-Falek, O.</dc:creator>
<dc:creator>Chr</dc:creator>
<dc:date>2017-09-06</dc:date>
<dc:identifier>doi:10.1101/184853</dc:identifier>
<dc:title><![CDATA[GWAS meta-analysis (N=279,930) identifies new genes and functional links to intelligence]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-09-06</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/174565v1?rss=1">
<title>
<![CDATA[
Integrative analyses of splicing in the aging brain: role in susceptibility to Alzheimer’s Disease 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/174565v1?rss=1"
</link>
<description><![CDATA[
We use deep sequencing to identify sources of variation in mRNA splicing in the dorsolateral prefrontal cortex (DLFPC) of 450 subjects from two prospective cohort studies of aging. Hundreds of aberrant pre-mRNA splicing events are reproducibly associated with Alzheimers Disease (AD). We also generate a catalog of splicing quantitative trait loci (sQTL) effects in the human cortex: splicing of 3,198 genes is influenced by genetic variation. sQTLs are enriched among those variants influencing DNA methylation and histone acetylation. In assessing known AD loci, we report that altered splicing is the mechanism for the effects of the PICALM, CLU, and PTK2B susceptibility alleles. Further, we leverage our sQTL catalog to identify genes whose aberrant splicing is associated with AD and mediated by genetics. This transcriptome-wide association study identified 21 genes with significant associations, many of which are found in AD GWAS loci, but 8 are in novel AD loci, including FUS, which is a known amyotrophic lateral sclerosis (ALS) gene. This highlights an intriguing shared genetic architecture that is further elaborated by the convergence of old and new AD genes in autophagy-lysosomal-related pathways already implicated in AD and other neurodegenerative diseases. Overall, this study of the aging brains transcriptome provides evidence that dysregulation of mRNA splicing is a feature of AD and is, in some genetically-driven cases, causal.
]]></description>
<dc:creator>Raj, T.</dc:creator>
<dc:creator>Li, Y.</dc:creator>
<dc:creator>Wong, G.</dc:creator>
<dc:creator>Ramdhani, S.</dc:creator>
<dc:creator>Wang, Y.-c.</dc:creator>
<dc:creator>Ng, B.</dc:creator>
<dc:creator>Wang, M.</dc:creator>
<dc:creator>Gupta, I.</dc:creator>
<dc:creator>Haroutunian, V.</dc:creator>
<dc:creator>Zhang, B.</dc:creator>
<dc:creator>Schadt, E. E.</dc:creator>
<dc:creator>Young-Pearse, T.</dc:creator>
<dc:creator>Mostafavi, S.</dc:creator>
<dc:creator>Sklar, P.</dc:creator>
<dc:creator>Bennett, D.</dc:creator>
<dc:creator>De Jager, P. L.</dc:creator>
<dc:date>2017-08-10</dc:date>
<dc:identifier>doi:10.1101/174565</dc:identifier>
<dc:title><![CDATA[Integrative analyses of splicing in the aging brain: role in susceptibility to Alzheimer’s Disease]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-08-10</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/168674v1?rss=1">
<title>
<![CDATA[
Causal associations between risk factors and common diseases inferred from GWAS summary data 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/168674v1?rss=1"
</link>
<description><![CDATA[
Health risk factors such as body mass index (BMI), serum cholesterol and blood pressure are associated with many common diseases. It often remains unclear whether the risk factors are cause or consequence of disease, or whether the associations are the result of confounding. Genetic methods are useful to infer causality because genetic variants are present from birth and therefore unlikely to be confounded with environmental factors. We develop and apply a method (GSMR) that performs a multi-SNP Mendelian Randomization analysis using summary-level data from large genome-wide association studies (sample sizes of up to 405,072) to test the causal associations of BMI, waist-to-hip ratio, serum cholesterols, blood pressures, height and years of schooling (EduYears) with a range of common diseases. We identify a number of causal associations including a protective effect of LDL-cholesterol against type-2 diabetes (T2D) that might explain the side effects of statins on T2D, a protective effect of EduYears against Alzheimers disease, and bidirectional associations with opposite effects (e.g. higher BMI increases the risk of T2D but the effect T2D of BMI is negative). HDL-cholesterol has a significant risk effect on age-related macular degeneration, and the effect size remains significant accounting for the other risk factors. Our study develops powerful tools to integrate summary data from large studies to infer causality, and provides important candidates to be prioritized for further studies in medical research and for drug discovery.
]]></description>
<dc:creator>Zhu, Z.</dc:creator>
<dc:creator>Zheng, Z.</dc:creator>
<dc:creator>Zhang, F.</dc:creator>
<dc:creator>Wu, Y.</dc:creator>
<dc:creator>Trzaskowski, M.</dc:creator>
<dc:creator>Maier, R.</dc:creator>
<dc:creator>Robinson, M.</dc:creator>
<dc:creator>McGrath, J.</dc:creator>
<dc:creator>Visscher, P.</dc:creator>
<dc:creator>Wray, N.</dc:creator>
<dc:creator>Yang, J.</dc:creator>
<dc:date>2017-07-26</dc:date>
<dc:identifier>doi:10.1101/168674</dc:identifier>
<dc:title><![CDATA[Causal associations between risk factors and common diseases inferred from GWAS summary data]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-07-26</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/118810v1?rss=1">
<title>
<![CDATA[
MTAG: Multi-Trait Analysis of GWAS 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/118810v1?rss=1"
</link>
<description><![CDATA[
We introduce Multi-Trait Analysis of GWAS (MTAG), a method for joint analysis of summary statistics from GWASs of different traits, possibly from overlapping samples. We apply MTAG to summary statistics for depressive symptoms (Neff = 354,862), neuroticism (N = 168,105), and subjective well-being (N = 388,538). Compared to 32, 9, and 13 genome-wide significant loci in the single-trait GWASs (most of which are themselves novel), MTAG increases the number of loci to 64, 37, and 49, respectively. Moreover, association statistics from MTAG yield more informative bioinformatics analyses and increase variance explained by polygenic scores by approximately 25%, matching theoretical expectations.
]]></description>
<dc:creator>Turley, P.</dc:creator>
<dc:creator>Walters, R. K.</dc:creator>
<dc:creator>Maghzian, O.</dc:creator>
<dc:creator>Okbay, A.</dc:creator>
<dc:creator>Lee, J. J.</dc:creator>
<dc:creator>Fontana, M. A.</dc:creator>
<dc:creator>Nguyen-Viet, T. A.</dc:creator>
<dc:creator>Furlotte, N. A.</dc:creator>
<dc:creator>23andMe Research Team,</dc:creator>
<dc:creator>Social Science Genetic Association Consortium,</dc:creator>
<dc:creator>Magnusson, P.</dc:creator>
<dc:creator>Oskarsson, S.</dc:creator>
<dc:creator>Johannesson, M.</dc:creator>
<dc:creator>Visscher, P. M.</dc:creator>
<dc:creator>Laibson, D.</dc:creator>
<dc:creator>Cesarini, D.</dc:creator>
<dc:creator>Neale, B.</dc:creator>
<dc:creator>Benjamin, D. J.</dc:creator>
<dc:date>2017-03-20</dc:date>
<dc:identifier>doi:10.1101/118810</dc:identifier>
<dc:title><![CDATA[MTAG: Multi-Trait Analysis of GWAS]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-03-20</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/148890v1?rss=1">
<title>
<![CDATA[
Dark Control: A Unified Account of Default Mode Function by Control Theory and Reinforcement Learning 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/148890v1?rss=1"
</link>
<description><![CDATA[
The default mode network (DMN) is believed to subserve the baseline mental activity in humans. Its highest energy consumption compared to other brain networks and its intimate coupling with conscious awareness are both pointing to an overarching function. Many research streams speak in favor of an evolutionarily adaptive role in envisioning experience to anticipate the future. In the present work, we propose a process model that tries to explain how the DMN may implement continuous evaluation and prediction of the environment to guide behavior. Specifically, we answer the question whether the neurobiological properties of the DMN collectively provide the computational building blocks necessary for a Markov Decision Process. We argue that our formal account of DMN function naturally accommodates as special cases previous interpretations based on (1) predictive coding, (2) semantic associations, and (3) a sentinel role. Moreover, this process model for the neural optimization of complex behavior in the DMN offers parsimonious explanations for recent experimental findings in animals and humans.
]]></description>
<dc:creator>Dohmatob, E.</dc:creator>
<dc:creator>Dumas, G.</dc:creator>
<dc:creator>Bzdok, D.</dc:creator>
<dc:date>2017-06-14</dc:date>
<dc:identifier>doi:10.1101/148890</dc:identifier>
<dc:title><![CDATA[Dark Control: A Unified Account of Default Mode Function by Control Theory and Reinforcement Learning]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-06-14</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/266411v1?rss=1">
<title>
<![CDATA[
Significant shared heritability underlies suicide attempt and clinically predicted probability of attempting suicide 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/266411v1?rss=1"
</link>
<description><![CDATA[
Suicide accounts for nearly 800,000 deaths per year worldwide with rates of both deaths and attempts rising. Family studies have estimated substantial heritability of suicidal behavior; however, collecting the sample sizes necessary for successful genetic studies has remained a challenge. We utilized two different approaches in independent datasets to characterize the contribution of common genetic variation to suicide attempt. The first is a patient reported suicide attempt phenotype from genotyped samples in the UK Biobank (337,199 participants, 2,433 cases). The second leveraged electronic health record (EHR) data from the Vanderbilt University Medical Center (VUMC, 2.8 million patients, 3,250 cases) and machine learning to derive probabilities of attempting suicide in 24,546 genotyped patients. We identified significant and comparable heritability estimates of suicide attempt from both the patient reported phenotype in the UK Biobank (h2SNP = 0.035, p = 7.12x10-4) and the clinically predicted phenotype from VUMC (h2SNP = 0.046, p = 1.51x10-2). A significant genetic overlap was demonstrated between the two measures of suicide attempt in these independent samples through polygenic risk score analysis (t = 4.02, p = 5.75x10-5) and genetic correlation (rg = 1.073, SE = 0.36, p = 0.003). Finally, we show significant but incomplete genetic correlation of suicide attempt with insomnia (rg = 0.34 - 0.81) as well as several psychiatric disorders (rg = 0.26 - 0.79). This work demonstrates the contribution of common genetic variation to suicide attempt. It points to a genetic underpinning to clinically predicted risk of attempting suicide that is similar to the genetic profile from a patient reported outcome. Lastly, it presents an approach for using EHR data and clinical prediction to generate quantitative measures from binary phenotypes that improved power for our genetic study.
]]></description>
<dc:creator>Ruderfer, D. M.</dc:creator>
<dc:creator>Walsh, C. G.</dc:creator>
<dc:creator>Aquirre, M. W.</dc:creator>
<dc:creator>Ribeiro, J. D.</dc:creator>
<dc:creator>Franklin, J. C.</dc:creator>
<dc:creator>Rivas, M. A.</dc:creator>
<dc:date>2018-02-15</dc:date>
<dc:identifier>doi:10.1101/266411</dc:identifier>
<dc:title><![CDATA[Significant shared heritability underlies suicide attempt and clinically predicted probability of attempting suicide]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-02-15</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/234294v1?rss=1">
<title>
<![CDATA[
Genome-wide association analysis of lifetime cannabis use (N=184,765) identifies new risk loci, genetic overlap with mental health, and a causal influence of schizophrenia on cannabis use 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/234294v1?rss=1"
</link>
<description><![CDATA[
Cannabis use is a heritable trait [1] that has been associated with adverse mental health outcomes. To identify risk variants and improve our knowledge of the genetic etiology of cannabis use, we performed the largest genome-wide association study (GWAS) meta-analysis for lifetime cannabis use (N=184,765) to date. We identified 4 independent loci containing genome-wide significant SNP associations. Gene-based tests revealed 29 genome-wide significant genes located in these 4 loci and 8 additional regions. All SNPs combined explained 10% of the variance in lifetime cannabis use. The most significantly associated gene, CADM2, has previously been associated with substance use and risk-taking phenotypes [2-4]. We used S-PrediXcan to explore gene expression levels and found 11 unique eGenes. LD-score regression uncovered genetic correlations with smoking, alcohol use and mental health outcomes, including schizophrenia and bipolar disorder. Mendelian randomisation analysis provided evidence for a causal positive influence of schizophrenia risk on lifetime cannabis use.
]]></description>
<dc:creator>Pasman, J. A.</dc:creator>
<dc:creator>Verweij, K. J. H.</dc:creator>
<dc:creator>Gerring, Z.</dc:creator>
<dc:creator>Stringer, S.</dc:creator>
<dc:creator>Sanchez-Roige, S.</dc:creator>
<dc:creator>Treur, J. L.</dc:creator>
<dc:creator>Abdellaoui, A.</dc:creator>
<dc:creator>Nivard, M. G.</dc:creator>
<dc:creator>Baselmans, B. M. L.</dc:creator>
<dc:creator>Ong, J.-S.</dc:creator>
<dc:creator>Ip, H. F.</dc:creator>
<dc:creator>van der Zee, M. D.</dc:creator>
<dc:creator>Bartels, M.</dc:creator>
<dc:creator>Day, F. R.</dc:creator>
<dc:creator>Fontanillas, P.</dc:creator>
<dc:creator>Elson, S. L.</dc:creator>
<dc:creator>the 23andMe Research Team,</dc:creator>
<dc:creator>de Wit, H.</dc:creator>
<dc:creator>Davis, L. K.</dc:creator>
<dc:creator>MacKillop, J.</dc:creator>
<dc:creator>International Cannabis Consortium,</dc:creator>
<dc:creator>Derringer, J. L.</dc:creator>
<dc:creator>Branje, S. J. T.</dc:creator>
<dc:creator>Hartman, C. A.</dc:creator>
<dc:creator>Heath, A. C.</dc:creator>
<dc:creator>van Lier, P. A. C.</dc:creator>
<dc:creator>Madden, P. A. F.</dc:creator>
<dc:creator>Magi, R.</dc:creator>
<dc:creator>Meeus, W.</dc:creator>
<dc:creator>Montgomery, G. W.</dc:creator>
<dc:creator>Oldehinkel, A. J.</dc:creator>
<dc:creator>Pausova, Z.</dc:creator>
<dc:creator>Ramos-Quiroga, J. A.</dc:creator>
<dc:creator>Paus, T.</dc:creator>
<dc:creator>Ribases, M.</dc:creator>
<dc:creator>Kaprio, J.</dc:creator>
<dc:creator>Boks, M. P. M</dc:creator>
<dc:date>2018-01-08</dc:date>
<dc:identifier>doi:10.1101/234294</dc:identifier>
<dc:title><![CDATA[Genome-wide association analysis of lifetime cannabis use (N=184,765) identifies new risk loci, genetic overlap with mental health, and a causal influence of schizophrenia on cannabis use]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-01-08</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/250712v1?rss=1">
<title>
<![CDATA[
Genomic risk prediction of coronary artery disease in nearly 500,000 adults: implications for early screening and primary prevention 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/250712v1?rss=1"
</link>
<description><![CDATA[
BackgroundCoronary artery disease (CAD) has substantial heritability and a polygenic architecture; however, genomic risk scores have not yet leveraged the totality of genetic information available nor been externally tested at population-scale to show potential utility in primary prevention.nnMethodsUsing a meta-analytic approach to combine large-scale genome-wide and targeted genetic association data, we developed a new genomic risk score for CAD (metaGRS), consisting of 1.7 million genetic variants. We externally tested metaGRS, individually and in combination with available conventional risk factors, in 22,242 CAD cases and 460,387 non-cases from UK Biobank.nnFindingsIn UK Biobank, a standard deviation increase in metaGRS had a hazard ratio (HR) of 1.71 (95% CI 1.68-1.73) for CAD, greater than any other externally tested genetic risk score. Individuals in the top 20% of the metaGRS distribution had a HR of 4.17 (95% CI 3.97-4.38) compared with those in the bottom 20%. The metaGRS had higher C-index (C=0.623, 95% CI 0.615-0.631) for incident CAD than any of four conventional factors (smoking, diabetes, hypertension, and body mass index), and addition of the metaGRS to a model of conventional risk factors increased C-index by 3.7%. In individuals on lipid-lowering or anti-hypertensive medications at recruitment, metaGRS hazard for incident CAD was significantly but only partially attenuated with HR of 2.83 (95% CI 2.61- 3.07) between the top and bottom 20% of the metaGRS distribution.nnInterpretationRecent genetic association studies have yielded enough information to meaningfully stratify individuals using the metaGRS for CAD risk in both early and later life, thus enabling targeted primary intervention in combination with conventional risk factors. The metaGRS effect was partially attenuated by lipid and blood pressure-lowering medication, however other prevention strategies will be required to fully benefit from earlier genomic risk stratification.nnFundingNational Health and Medical Research Council of Australia, British Heart Foundation, Australian Heart Foundation.
]]></description>
<dc:creator>Inouye, M.</dc:creator>
<dc:creator>Abraham, G.</dc:creator>
<dc:creator>Nelson, C. P.</dc:creator>
<dc:creator>Wood, A. M.</dc:creator>
<dc:creator>Sweeting, M. J.</dc:creator>
<dc:creator>Dudbridge, F.</dc:creator>
<dc:creator>Lai, F. Y.</dc:creator>
<dc:creator>Kaptoge, S.</dc:creator>
<dc:creator>Brozynska, M.</dc:creator>
<dc:creator>Wang, T.</dc:creator>
<dc:creator>Ye, S.</dc:creator>
<dc:creator>Webb, T. R.</dc:creator>
<dc:creator>Rutter, M. K.</dc:creator>
<dc:creator>Tzoulaki, I.</dc:creator>
<dc:creator>Patel, R. S.</dc:creator>
<dc:creator>Loos, R. J.</dc:creator>
<dc:creator>Keavney, B.</dc:creator>
<dc:creator>Hemingway, H.</dc:creator>
<dc:creator>Thompson, J.</dc:creator>
<dc:creator>Watkins, H.</dc:creator>
<dc:creator>Deloukas, P.</dc:creator>
<dc:creator>Di Angelantonio, E.</dc:creator>
<dc:creator>Butterworth, A. S.</dc:creator>
<dc:creator>Danesh, J.</dc:creator>
<dc:creator>Samani, N. J.</dc:creator>
<dc:date>2018-01-19</dc:date>
<dc:identifier>doi:10.1101/250712</dc:identifier>
<dc:title><![CDATA[Genomic risk prediction of coronary artery disease in nearly 500,000 adults: implications for early screening and primary prevention]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-01-19</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/243089v1?rss=1">
<title>
<![CDATA[
An empirical, 21st century evaluation of phrenology 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/243089v1?rss=1"
</link>
<description><![CDATA[
Phrenology was a nineteenth century endeavour to link personality traits with scalp morphology, which has been both influential and fiercely criticised, not least because of the assumption that scalp morphology can be informative of underlying brain function. Here we test the idea empirically rather than dismissing it out of hand. Whereas nineteenth century phrenologists had access to coarse measurement tools (digital technology referring then to fingers), we were able to re-examine phrenology using 21st century methods and thousands of subjects drawn from the largest neuroimaging study to date. High-quality structural MRI was used to quantify local scalp curvature. The resulting curvature statistics were compared against lifestyle measures acquired from the same cohort of subjects, being careful to match a subset of lifestyle measures to phrenological ideas of brain organisation, in an effort to evoke the character of Victorian times. The results represent the most rigorous evaluation of phrenological claims to date.
]]></description>
<dc:creator>Parker Jones, O.</dc:creator>
<dc:creator>Alfaro-Almagro, F.</dc:creator>
<dc:creator>Jbabdi, S.</dc:creator>
<dc:date>2018-01-05</dc:date>
<dc:identifier>doi:10.1101/243089</dc:identifier>
<dc:title><![CDATA[An empirical, 21st century evaluation of phrenology]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-01-05</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/214973v1?rss=1">
<title>
<![CDATA[
Genome-wide Analysis of Insomnia (N=1,331,010) Identifies Novel Loci and Functional Pathways 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/214973v1?rss=1"
</link>
<description><![CDATA[
Insomnia is the second-most prevalent mental disorder, with no sufficient treatment available. Despite a substantial role of genetic factors, only a handful of genes have been implicated and insight into the associated neurobiological pathways remains limited. Here, we use an unprecedented large genetic association sample (N=1,331,010) to allow detection of a substantial number of genetic variants and gain insight into biological functions, cell types and tissues involved in insomnia complaints. We identify 202 genome-wide significant loci implicating 956 genes through positional, eQTL and chromatin interaction mapping. We show involvement of the axonal part of neurons, of specific cortical and subcortical tissues, and of two specific cell-types in insomnia: striatal medium spiny neurons and hypothalamic neurons. These cell-types have been implicated previously in the regulation of reward processing, sleep and arousal in animal studies, but have never been genetically linked to insomnia in humans. We found weak genetic correlations with other sleep-related traits, but strong genetic correlations with psychiatric and metabolic traits. Mendelian randomization identified causal effects of insomnia on specific psychiatric and metabolic traits. Our findings reveal key brain areas and cells implicated in the neurobiology of insomnia and its related disorders, and provide novel targets for treatment.
]]></description>
<dc:creator>Jansen, P. R.</dc:creator>
<dc:creator>Watanabe, K.</dc:creator>
<dc:creator>Stringer, S.</dc:creator>
<dc:creator>Skene, N.</dc:creator>
<dc:creator>Bryois, J.</dc:creator>
<dc:creator>Hammerschlag, A. R.</dc:creator>
<dc:creator>de Leeuw, C. A.</dc:creator>
<dc:creator>Benjamins, J.</dc:creator>
<dc:creator>Munoz-Manchado, A. B.</dc:creator>
<dc:creator>Nagel, M.</dc:creator>
<dc:creator>Savage, J. E.</dc:creator>
<dc:creator>Tiemeier, H.</dc:creator>
<dc:creator>White, T.</dc:creator>
<dc:creator>Tung, J. Y.</dc:creator>
<dc:creator>Hinds, D. A.</dc:creator>
<dc:creator>Vacic, V.</dc:creator>
<dc:creator>Sullivan, P. F.</dc:creator>
<dc:creator>van der Sluis, S.</dc:creator>
<dc:creator>Polderman, T. J.</dc:creator>
<dc:creator>Smit, A. B.</dc:creator>
<dc:creator>Hjerling-Leffler, J.</dc:creator>
<dc:creator>van Someren, E. J.</dc:creator>
<dc:creator>Posthuma, D.</dc:creator>
<dc:date>2018-01-30</dc:date>
<dc:identifier>doi:10.1101/214973</dc:identifier>
<dc:title><![CDATA[Genome-wide Analysis of Insomnia (N=1,331,010) Identifies Novel Loci and Functional Pathways]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-01-30</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/350983v1?rss=1">
<title>
<![CDATA[
Genome-wide association study of circadian rhythmicity in 71 500 UK Biobank participants and polygenic association with mood instability 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/350983v1?rss=1"
</link>
<description><![CDATA[
BackgroundCircadian rhythms are fundamental to health and are particularly important for mental wellbeing. Disrupted rhythms of rest and activity are recognised as risk factors for major depressive disorder and bipolar disorder.nnMethodsWe conducted a genome-wide association study (GWAS) of low relative amplitude (RA), an objective measure of circadian rhythmicity derived from the accelerometer data of 71 500 UK Biobank participants. Polygenic risk scores (PRS) for low RA were used to investigate potential associations with psychiatric phenotypes.nnOutcomesTwo independent genetic loci were associated with low RA, within genomic regions for Neurofascin (NFASC) and Solute Carrier Family 25 Member 17 (SLC25A17). A secondary GWAS of RA as a continuous measure identified a locus within Meis Homeobox 1 (MEIS1). There were no significant genetic correlations between low RA and any of the psychiatric phenotypes assessed. However, PRS for low RA was significantly associated with mood instability across multiple PRS thresholds (at PRS threshold 0{middle dot}05: OR=1{middle dot}02, 95% CI=1{middle dot}01-1{middle dot}02, p=9{middle dot}6x10-5), and with major depressive disorder (at PRS threshold 0{middle dot}1: OR=1{middle dot}03, 95% CI=1{middle dot}01-1{middle dot}05, p=0{middle dot}025) and neuroticism (at PRS threshold 0{middle dot}5: Beta=0{middle dot}02, 95% CI=0{middle dot}007-0{middle dot}04, p=0{middle dot}021).nnInterpretationOverall, our findings contribute new knowledge on the complex genetic architecture of circadian rhythmicity and suggest a putative biological link between disrupted circadian function and mood disorder phenotypes, particularly mood instability, but also major depressive disorder and neuroticism.
]]></description>
<dc:creator>Ferguson, A.</dc:creator>
<dc:creator>Lyall, L. M.</dc:creator>
<dc:creator>Ward, J.</dc:creator>
<dc:creator>Strawbridge, R. J.</dc:creator>
<dc:creator>Cullen, B.</dc:creator>
<dc:creator>Graham, N.</dc:creator>
<dc:creator>Niedzwiedz, C. L.</dc:creator>
<dc:creator>Johnston, K. J.</dc:creator>
<dc:creator>MacKay, D.</dc:creator>
<dc:creator>Biello, S. M.</dc:creator>
<dc:creator>Pell, J. P.</dc:creator>
<dc:creator>Cavanagh, J.</dc:creator>
<dc:creator>McIntosh, A. M.</dc:creator>
<dc:creator>Doherty, A.</dc:creator>
<dc:creator>Bailey, M. E.</dc:creator>
<dc:creator>Lyall, D. M.</dc:creator>
<dc:creator>Wyse, C. A.</dc:creator>
<dc:creator>Smith, D. J.</dc:creator>
<dc:date>2018-06-20</dc:date>
<dc:identifier>doi:10.1101/350983</dc:identifier>
<dc:title><![CDATA[Genome-wide association study of circadian rhythmicity in 71 500 UK Biobank participants and polygenic association with mood instability]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-06-20</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/121012v1?rss=1">
<title>
<![CDATA[
Novel Locus Influencing Retinal Venular Tortuosity Is Also Associated With Risk Of Coronary Artery Disease 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/121012v1?rss=1"
</link>
<description><![CDATA[
Structural variation in retinal blood vessels is associated with global vascular health in humans and may provide a readily accessible indicator of several diseases of vascular origin. Increasing evidence suggests variation in retinal vasculature is highly heritable. This study aimed to identify genetic determinants of retinal vascular traits. We reported a meta-analysis of genome-wide association studies (GWAS) for quantitative retinal vascular traits derived using semi-automatic image analysis of digital retinal photographs from the Genetics of Diabetes Audit and Research in Tayside (GoDARTS) (n=1736) and the Orkney Complex Disease Study (ORCADES) (n=1358) cohorts. We identified a novel genome-wide significant locus at 19q13 (ACTN4/CAPN12) for retinal venular tortuosity (TortV), and one at 13q34 (COL4A2) for retinal arteriolar tortuosity (TortA); these two loci were subsequently confirmed in three independent cohorts (n=1413). In the combined analysis in ACTN4/CAPN12 the lead single nucleotide polymorphism (SNP) was rs1808382 (n=4507; Beta=-0.109; standard error (SE) =0.015; P=2.39x10-13) and in COL4A2 it was rs7991229 (n=4507; Beta=0.103; SE=0.015; P=4.66x10-12). Notably, the ACTN4/CAPN12 locus associated with retinal TortV is also associated with coronary artery disease and heart rate. Our findings demonstrate the contribution of genetics in retinal tortuosity traits, and provide new insights into cardiovascular diseases.nnAuthor SummaryRetinal vascular features are associated with wide range of diseases related to vascular health and provide an opportunity to understand early structural changes in vasculature which may help to predict disease risk. Emerging evidence indicates that retinal tortuosity traits are both associated with vascular health and highly heritable. However, the genetic architecture of retinal vascular tortuosity has not been investigated. We therefore performed a genome-wide association study on retinal arteriolar tortuosity (TortA) and retinal venular tortuosity trait (TortV) using data from two independent discovery cohorts of 3094 individuals of European-heritage. We found a novel associations at 19q13 (ACTN4/CAPN12) for TortV, and one at 13q34 (COL4A2) for TortA at discovery stage and validated in three independent cohorts. A significant association was subsequently found between lead SNPs at 19q13 and coronary artery disease, cardiovascular vascular risk factors and heart rate. We also performed genome-wide association studies for retinal vascular calibres and optic disc radius (ODradius) and replicated previously reported locus at 10q21.3 for ODradius. Our findings highlight genetic impacts on retinal venular tortuosity and it is association with cardiovascular disease. This may provide a molecular pathophysiological foundation for use of retinal vascular traits as biomarkers for cardiovascular diseases.
]]></description>
<dc:creator>Veluchamy, A.</dc:creator>
<dc:creator>Ballerini, L.</dc:creator>
<dc:creator>Vitart, V.</dc:creator>
<dc:creator>Hayward, C.</dc:creator>
<dc:creator>Schraut, K.</dc:creator>
<dc:creator>Joshi, P.</dc:creator>
<dc:creator>Campbell, H.</dc:creator>
<dc:creator>Kirin, M.</dc:creator>
<dc:creator>Relan, D.</dc:creator>
<dc:creator>Harris, S.</dc:creator>
<dc:creator>Brown, E.</dc:creator>
<dc:creator>Vaidya, S.</dc:creator>
<dc:creator>Dhillon, B.</dc:creator>
<dc:creator>Zhou, K.</dc:creator>
<dc:creator>Pearson, E.</dc:creator>
<dc:creator>Polasek, O.</dc:creator>
<dc:creator>Deary, I.</dc:creator>
<dc:creator>MacGillivray, T.</dc:creator>
<dc:creator>Wilson, J.</dc:creator>
<dc:creator>Trucco, E.</dc:creator>
<dc:creator>Palmer, C.</dc:creator>
<dc:creator>Doney, A.</dc:creator>
<dc:date>2017-04-14</dc:date>
<dc:identifier>doi:10.1101/121012</dc:identifier>
<dc:title><![CDATA[Novel Locus Influencing Retinal Venular Tortuosity Is Also Associated With Risk Of Coronary Artery Disease]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-04-14</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/350355v1?rss=1">
<title>
<![CDATA[
FunSPU: a versatile and adaptive multiple functional annotation-based association test of whole-genome sequencing data 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/350355v1?rss=1"
</link>
<description><![CDATA[
Despite ongoing large-scale population-based whole-genome sequencing (WGS) projects such as the NIH NHLBI TOPMed program and the NHGRI Genome Sequencing Program, WGS-based association analysis of complex traits remains a tremendous challenge due to the large number of rare variants, many of which are non-trait-associated neutral variants. External biological knowledge, such as functional annotations based on ENCODE, may be helpful in distinguishing causal rare variants from neutral ones; however, each functional annotation can only provide certain aspects of the biological functions. Our knowledge for selecting informative annotations a priori is limited, and incorporating non-informative annotations will introduce noise and lose power. We propose FunSPU, a versatile and adaptive test that incorporates multiple biological annotations and is adaptive at both the annotation and variant levels and thus maintains high power even in the presence of noninformative annotations. In addition to extensive simulations, we illustrate our proposed test using the TWINSUK cohort (n=1,752) of UK10K WGS data based on six functional annotations: CADD, RegulomeDB, FunSeq, Funseq2, GERP++, and GenoSkyline. We identified genome-wide significant genetic loci on chromosome 19 near gene TOMM40 and APOC4-APOC2 associated with low-density lipoprotein (LDL), which are replicated in the UK10K ALSPAC cohort (n=1,497). These replicated LDL-associated loci were missed by existing rare variant association tests that either ignore external biological information or rely on a single source of biological knowledge. We have implemented the proposed test in an R package "FunSPU".
]]></description>
<dc:creator>Ma, Y.</dc:creator>
<dc:creator>Wei, P.</dc:creator>
<dc:date>2018-06-19</dc:date>
<dc:identifier>doi:10.1101/350355</dc:identifier>
<dc:title><![CDATA[FunSPU: a versatile and adaptive multiple functional annotation-based association test of whole-genome sequencing data]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-06-19</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/349845v1?rss=1">
<title>
<![CDATA[
Low-frequency variation in TP53 has large effects on head circumference and intracranial volume 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/349845v1?rss=1"
</link>
<description><![CDATA[
Cranial growth and development affects the closely related traits of head circumference (HC) and intracranial volume (ICV). Here we model the developmental genetic architecture of HC, showing this is genetically stable and correlated with genetic determinants of ICV. Investigating up to 46,000 children and adults of European descent, we identify association with final HC and/or final ICV+HC at 9 novel common and low-frequency loci, illustrating that genetic variation from a wide allele frequency spectrum contributes to cranial growth. The largest effects are reported for low-frequency variants within TP53, with 0.5 cm wider heads in increaser-allele carriers versus non-carriers during mid-childhood.
]]></description>
<dc:creator>Haworth, S.</dc:creator>
<dc:creator>Shapland, C. Y.</dc:creator>
<dc:creator>Hayward, C.</dc:creator>
<dc:creator>Prins, B. P.</dc:creator>
<dc:creator>Felix, J. F.</dc:creator>
<dc:creator>Medina-Gomez, C.</dc:creator>
<dc:creator>Rivadeneira, F.</dc:creator>
<dc:creator>Wang, C.</dc:creator>
<dc:creator>Ahluwalia, T. S.</dc:creator>
<dc:creator>Vrijheid, M.</dc:creator>
<dc:creator>Guxens, M.</dc:creator>
<dc:creator>Sunyer, J.</dc:creator>
<dc:creator>Tachmazidou, I.</dc:creator>
<dc:creator>Walter, K.</dc:creator>
<dc:creator>Iotchkova, V.</dc:creator>
<dc:creator>Jackson, A.</dc:creator>
<dc:creator>Cleal, L.</dc:creator>
<dc:creator>Huffmann, J.</dc:creator>
<dc:creator>Min, J. L.</dc:creator>
<dc:creator>Sass, L.</dc:creator>
<dc:creator>Timmers, P. R. H. J.</dc:creator>
<dc:creator>UK10K consortium,</dc:creator>
<dc:creator>Davey Smith, G.</dc:creator>
<dc:creator>Fisher, S. E.</dc:creator>
<dc:creator>Wilson, J. F.</dc:creator>
<dc:creator>Cole, T. J.</dc:creator>
<dc:creator>Fernandez-Orth, D.</dc:creator>
<dc:creator>Bonnelykke, K.</dc:creator>
<dc:creator>Bisgaard, H.</dc:creator>
<dc:creator>Pennell, C. E.</dc:creator>
<dc:creator>Jaddoe, V. W. V.</dc:creator>
<dc:creator>Dedoussis, G.</dc:creator>
<dc:creator>Timpson, N. J.</dc:creator>
<dc:creator>Zeggini, E.</dc:creator>
<dc:creator>Vitart, V.</dc:creator>
<dc:creator>St Pourcain, B.</dc:creator>
<dc:date>2018-06-18</dc:date>
<dc:identifier>doi:10.1101/349845</dc:identifier>
<dc:title><![CDATA[Low-frequency variation in TP53 has large effects on head circumference and intracranial volume]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-06-18</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/343293v1?rss=1">
<title>
<![CDATA[
New genetic signals for lung function highlight pathways and pleiotropy, and chronic obstructive pulmonary disease associations across multiple ancestries 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/343293v1?rss=1"
</link>
<description><![CDATA[
Reduced lung function predicts mortality and is key to the diagnosis of COPD. In a genome-wide association study in 400,102 individuals of European ancestry, we define 279 lung function signals, one-half of which are new. In combination these variants strongly predict COPD in deeply-phenotyped patient populations. Furthermore, the combined effect of these variants showed generalisability across smokers and never-smokers, and across ancestral groups. We highlight biological pathways, known and potential drug targets for COPD and, in phenome-wide association studies, autoimmune-related and other pleiotropic effects of lung function associated variants. This new genetic evidence has potential to improve future preventive and therapeutic strategies for COPD.
]]></description>
<dc:creator>Shrine, N.</dc:creator>
<dc:creator>Guyatt, A. L.</dc:creator>
<dc:creator>Erzurumluoglu, A. M.</dc:creator>
<dc:creator>Jackson, V. E.</dc:creator>
<dc:creator>Hobbs, B. D.</dc:creator>
<dc:creator>Melbourne, C.</dc:creator>
<dc:creator>Batini, C.</dc:creator>
<dc:creator>Fawcett, K. A.</dc:creator>
<dc:creator>Song, K.</dc:creator>
<dc:creator>Sakornsakolpat, P.</dc:creator>
<dc:creator>Li, X.</dc:creator>
<dc:creator>Boxall, R.</dc:creator>
<dc:creator>Reeve, N. F.</dc:creator>
<dc:creator>Obeidat, M.</dc:creator>
<dc:creator>Zhao, J. H.</dc:creator>
<dc:creator>Wielscher, M.</dc:creator>
<dc:creator>Understanding Society Scientific Group,</dc:creator>
<dc:creator>Weiss, S.</dc:creator>
<dc:creator>Kentistou, K. A.</dc:creator>
<dc:creator>Cook, J. P.</dc:creator>
<dc:creator>Sun, B. B.</dc:creator>
<dc:creator>Zhou, J.</dc:creator>
<dc:creator>Hui, J.</dc:creator>
<dc:creator>Karrasch, S.</dc:creator>
<dc:creator>Imboden, M.</dc:creator>
<dc:creator>Harris, S. E.</dc:creator>
<dc:creator>Marten, J.</dc:creator>
<dc:creator>Enroth, S.</dc:creator>
<dc:creator>Kerr, S. M.</dc:creator>
<dc:creator>Surakka, I.</dc:creator>
<dc:creator>Vitart, V.</dc:creator>
<dc:creator>Lehtimäki, T.</dc:creator>
<dc:creator>Allen, R. J.</dc:creator>
<dc:creator>Bakke, P. S.</dc:creator>
<dc:creator>Beaty, T. H.</dc:creator>
<dc:creator>Bleecker, E. R.</dc:creator>
<dc:creator>Bosse, Y.</dc:creator>
<dc:creator>Brandsma, C.-A.</dc:creator>
<dc:creator>Chen, Z.</dc:creator>
<dc:creator>Crapo, J. D.</dc:creator>
<dc:creator>Danesh, J.</dc:creator>
<dc:creator>DeMeo,</dc:creator>
<dc:date>2018-06-12</dc:date>
<dc:identifier>doi:10.1101/343293</dc:identifier>
<dc:title><![CDATA[New genetic signals for lung function highlight pathways and pleiotropy, and chronic obstructive pulmonary disease associations across multiple ancestries]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-06-12</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/338863v1?rss=1">
<title>
<![CDATA[
An Atlas of Human and Murine Genetic Influences on Osteoporosis 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/338863v1?rss=1"
</link>
<description><![CDATA[
Osteoporosis is a common debilitating chronic disease diagnosed primarily using bone mineral density (BMD). We undertook a comprehensive assessment of human genetic determinants of bone density in 426,824 individuals, identifying a total of 518 genome-wide significant loci, (301 novel), explaining 20% of the total variance in BMD--as estimated by heel quantitative ultrasound (eBMD). Next, meta-analysis identified 13 bone fracture loci in ~1.2M individuals, which were also associated with BMD. We then identified target genes from cell-specific genomic landscape features, including chromatin conformation and accessible chromatin sites, that were strongly enriched for genes known to influence bone density and strength (maximum odds ratio = 58, P = 10-75). We next performed rapid throughput skeletal phenotyping of 126 knockout mice lacking eBMD Target Genes and showed that these mice had an increased frequency of abnormal skeletal phenotypes compared to 526 unselected lines (P < 0.0001). In-depth analysis of one such Target Gene, DAAM2, showed a disproportionate decrease in bone strength relative to mineralization. This comprehensive human and murine genetic atlas provides empirical evidence testing how to link associated SNPs to causal genes, offers new insights into osteoporosis pathophysiology and highlights opportunities for drug development.
]]></description>
<dc:creator>Morris, J. A.</dc:creator>
<dc:creator>Kemp, J. P.</dc:creator>
<dc:creator>Youlten, S. E.</dc:creator>
<dc:creator>Laurent, L.</dc:creator>
<dc:creator>Logan, J. G.</dc:creator>
<dc:creator>Chai, R.</dc:creator>
<dc:creator>Vulpescu, N. A.</dc:creator>
<dc:creator>Forgetta, V.</dc:creator>
<dc:creator>Kleinman, A.</dc:creator>
<dc:creator>Mohanty, S.</dc:creator>
<dc:creator>Sergio, C. M.</dc:creator>
<dc:creator>Quinn, J.</dc:creator>
<dc:creator>Nguyen-Yamamoto, L.</dc:creator>
<dc:creator>Luco, A.-L.</dc:creator>
<dc:creator>Vijay, J.</dc:creator>
<dc:creator>Simon, M.-M.</dc:creator>
<dc:creator>Pramatarova, A.</dc:creator>
<dc:creator>Medina-Gomez, C.</dc:creator>
<dc:creator>Trajanoska, K.</dc:creator>
<dc:creator>Ghirardello, E. J.</dc:creator>
<dc:creator>Butterfield, N. C.</dc:creator>
<dc:creator>Curry, K. F.</dc:creator>
<dc:creator>Leitch, V. D.</dc:creator>
<dc:creator>Sparkes, P. C.</dc:creator>
<dc:creator>Adoum, A.-T.</dc:creator>
<dc:creator>Mannan, N. S.</dc:creator>
<dc:creator>Komla-Ebri, D.</dc:creator>
<dc:creator>Pollard, A. S.</dc:creator>
<dc:creator>Dewhurst, H. F.</dc:creator>
<dc:creator>Hassell, T.</dc:creator>
<dc:creator>Beltejar, M.-J. G.</dc:creator>
<dc:creator>Adams, D. J.</dc:creator>
<dc:creator>Vaillancourt, S. M.</dc:creator>
<dc:creator>Kaptoge, S.</dc:creator>
<dc:creator>Baldock, P.</dc:creator>
<dc:creator>Cooper, C.</dc:creator>
<dc:creator>Reeve, J.</dc:creator>
<dc:creator>Ntzani, E.</dc:creator>
<dc:creator>Evangelou, E.</dc:creator>
<dc:creator>Ohlsson, C.</dc:creator>
<dc:creator>Karas</dc:creator>
<dc:date>2018-06-11</dc:date>
<dc:identifier>doi:10.1101/338863</dc:identifier>
<dc:title><![CDATA[An Atlas of Human and Murine Genetic Influences on Osteoporosis]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-06-11</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/335877v1?rss=1">
<title>
<![CDATA[
PCSK9 genetic variants, life-long lowering of LDL-cholesterol and cognition: a large-scale Mendelian randomization study 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/335877v1?rss=1"
</link>
<description><![CDATA[
AimsPCSK9 inhibitors lower LDL cholesterol and are efficacious at reducing risk of vascular disease, however questions remain about potential adverse effects on cognitive function. We examined the association of LDL cholesterol-lowering genetic variants in PCSK9 with continuous measures of cognitive abilitynnMethods and ResultsSix independent SNPs in PCSK9 were used in up to 337,348 individuals from the UK Biobank who underwent measures of cognitive ability (fluid reasoning, reaction time, trial making test and digit symbol coding. Scaled to a 50mg/dL lower LDL cholesterol, the PCSK9 allele score was associated with a lower risk of CHD (odds ratio 0.73; 95% CI: 0.60 to 0.90, P = 0.003). The scaled PCSK9 allele score nominally associated with worse log reaction time (0.04 standard deviations; 95%CI: 0.00, 0.08; P=0.038). Although no strong associations of the PCSK9 allele score were identified with any cognitive trait, the imprecision around the estimates meant that we could not exclude a similar magnitude of effect of genetic inhibition of PCSK9 to that seen with established risk factors, including APOEe4 or smoking status for any of the individual cognition traits. Point estimates for the PCSK9 allele score and cognition traits were all on the harmful side of unity.nnConclusionsUsing currently available data in UK Biobank, we are not able to rule out meaningful associations of PCSK9 genetic variants with cognition traits. These data highlight the need for further large-scale genetic analyses and, in parallel, continued pharmacovigilance for patients currently treated with PCSK9 inhibitors.
]]></description>
<dc:creator>Lyall, D.</dc:creator>
<dc:creator>Ward, J.</dc:creator>
<dc:creator>Banach, M.</dc:creator>
<dc:creator>Davey Smith, G.</dc:creator>
<dc:creator>Gill, J.</dc:creator>
<dc:creator>Pell, J.</dc:creator>
<dc:creator>Holmes, M.</dc:creator>
<dc:creator>Sattar, N.</dc:creator>
<dc:date>2018-05-31</dc:date>
<dc:identifier>doi:10.1101/335877</dc:identifier>
<dc:title><![CDATA[PCSK9 genetic variants, life-long lowering of LDL-cholesterol and cognition: a large-scale Mendelian randomization study]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-05-31</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/329052v1?rss=1">
<title>
<![CDATA[
Phenome-wide association analysis of LDL-cholesterol lowering genetic variants in PCSK9 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/329052v1?rss=1"
</link>
<description><![CDATA[
BackgroundWe characterised the phenotypic consequence of genetic variation at the PCSK9 locus and compared findings with recent trials of pharmacological inhibitors of PCSK9.nnMethodsPublished and individual participant level data (300,000+ participants) were combined to construct a weighted PCSK9 gene-centric score (GS). Fourteen randomized placebo controlled PCSK9 inhibitor trials were included, providing data on 79,578 participants. Results were scaled to a one mmol/L lower LDL-C concentrationnnResultsThe PCSK9 GS (comprising 4 SNPs) associations with plasma lipid and apolipoprotein levels were consistent in direction with treatment effects. The GS odds ratio (OR) for myocardial infarction (MI) was 0.53 (95%CI 0.42; 0.68), compared to a PCSK9 inhibitor effect of 0.90 (95%CI 0.86; 0.93). For ischemic stroke ORs were 0.84 (95%CI 0.57; 1.22) for the GS, compared to 0.85 (95%CI 0.78; 0.93) in the drug trials. ORs with type 2 diabetes mellitus (T2DM) were 1.29 (95% CI 1.11; 1.50) for the GS, as compared to 1.00 (95%CI 0.96; 1.04) for incident T2DM in PCSK9 inhibitor trials. No genetic associations were observed for cancer, heart failure, atrial fibrillation, chronic obstructive pulmonary disease, or Alzheimers disease - outcomes for which large-scale trial data were unavailable.nnConclusionsGenetic variation at the PCSK9 locus recapitulates the effects of therapeutic inhibition of PCSK9 on major blood lipid fractions and MI. Apparent discordance between genetic associations and trial outcome for T2DM might be explained lack by a of statistical precision, or differences in the nature and duration of genetic versus pharmacological perturbation of PCSK9.nnFundingThis research was funded by the British Heart Foundation (SP/13/6/30554, RG/10/12/28456, FS/18/23/33512), UCL Hospitals NIHR Biomedical Research Centre, by the Rosetrees and Stoneygate Trusts.nnCondensed abstractEvidence on the long-term efficacy and safety of therapeutic inhibition of PCSK9 is lacking. To explore potential long-term effects of PCSK9 inhibition, we characterised the phenotypic consequence of LDL-cholesterol lowering variants at the PCSK9 locus. A PCSK9 gene score comprising 4 SNPs recapitulated the effects of therapeutic inhibition of PCSK9 on major blood lipid fractions and risk of myocardial infarction, and was associated with an increased risk of type 2 diabetes. No associations with safety outcomes such as cancer, COPD, Alzheimers disease or atrial fibrillation were identified. Our findings suggest PCSK9 inhibition may be safe and effective during prolonged use.
]]></description>
<dc:creator>Schmidt, A. F.</dc:creator>
<dc:creator>Holmes, M. V.</dc:creator>
<dc:creator>Preiss, D.</dc:creator>
<dc:creator>Swerdlow, D. I.</dc:creator>
<dc:creator>Denaxas, S.</dc:creator>
<dc:creator>155 additional authors,</dc:creator>
<dc:creator>Hemingway, H.</dc:creator>
<dc:creator>Asselbergs, F.</dc:creator>
<dc:creator>Patel, R.</dc:creator>
<dc:creator>Keating, B. J.</dc:creator>
<dc:creator>Sattar, N.</dc:creator>
<dc:creator>Houlston, R.</dc:creator>
<dc:creator>Casas, J.</dc:creator>
<dc:creator>Hingorani, A. D.</dc:creator>
<dc:date>2018-05-25</dc:date>
<dc:identifier>doi:10.1101/329052</dc:identifier>
<dc:title><![CDATA[Phenome-wide association analysis of LDL-cholesterol lowering genetic variants in PCSK9]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-05-25</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/327205v1?rss=1">
<title>
<![CDATA[
Modelling the distribution of white matter hyperintensities due to ageing on MRI images using Bayesian inference 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/327205v1?rss=1"
</link>
<description><![CDATA[
White matter hyperintensities (WMH), also known as white matter lesions, are localised white matter areas that appear hyperintense on MRI scans. WMH commonly occur in the ageing population, and are often associated with several factors such as cognitive disorders, cardiovascular risk factors, cerebrovascular and neurodegenerative diseases. Despite the fact that some links between lesion location and parametric factors such as age have already been established, the relationship between voxel-wise spatial distribution of lesions and these factors is not yet well understood. Hence, it would be of clinical importance to model the distribution of lesions at the population-level and quantitatively analyse the effect of various factors on the lesion distribution model.nnIn this work we compare various methods, including our proposed method, to generate voxel-wise distributions of WMH within a population with respect to various factors. Our proposed Bayesian spline method models the spatio-temporal distribution of WMH with respect to a parametric factor of interest, in this case age, within a population. Our probabilistic model takes as input the lesion segmentation binary maps of subjects belonging to various age groups and provides a population-level parametric lesion probability map as output. We used a spline representation to ensure a degree of smoothness in space and the dimension associated with the parameter, and formulated our model using a Bayesian framework.nnWe tested our algorithm output on simulated data and compared our results with those obtained using various existing methods with different levels of algorithmic and computational complexity. We then compared the better performing methods on a real dataset, consisting of 1000 subjects of the UK Biobank, divided in two groups based on hypertension diagnosis. Finally, we applied our method on a clinical dataset of patients with vascular disease.nnOn simulated dataset, the results from our algorithm showed a mean square error (MSE) value of 7.27 x 10-5, which was lower than the MSE value reported in the literature, with the advantage of being robust and computationally efficient. In the UK Biobank data, we found that the lesion probabilities are higher for the hypertension group compared to the non-hypertension group and further verified this finding using a statistical t-test. Finally, when applying our method on patients with vascular disease, we observed that the overall probability of lesions is significantly higher in later age groups, which is in line with the current literature.
]]></description>
<dc:creator>Sundaresan, V.</dc:creator>
<dc:creator>Griffanti, L.</dc:creator>
<dc:creator>Kindalova, P.</dc:creator>
<dc:creator>Alfaro-Almagro, F.</dc:creator>
<dc:creator>Zamboni, G.</dc:creator>
<dc:creator>Rothwell, P. M.</dc:creator>
<dc:creator>Nichols, T. E.</dc:creator>
<dc:creator>Jenkinson, M.</dc:creator>
<dc:date>2018-05-21</dc:date>
<dc:identifier>doi:10.1101/327205</dc:identifier>
<dc:title><![CDATA[Modelling the distribution of white matter hyperintensities due to ageing on MRI images using Bayesian inference]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-05-21</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/318618v1?rss=1">
<title>
<![CDATA[
Refining fine-mapping: effect sizes and regional heritability 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/318618v1?rss=1"
</link>
<description><![CDATA[
Recent statistical approaches have shown that the set of all available genetic variants explains considerably more phenotypic variance of complex traits and diseases than the individual variants that are robustly associated with these phenotypes. However, rapidly increasing sample sizes constantly improve detection and prioritization of individual variants driving the associations between genomic regions and phenotypes. Therefore, it is useful to routinely estimate how much phenotypic variance the detected variants explain for each region by taking into account the correlation structure of variants and the uncertainty in their causal status. Here we extend the FINEMAP software to estimate the effect sizes and regional heritability under the probabilistic model that assumes a handful of causal variants per each region. Using the UK Biobank data to simulate GWAS regions with only a few causal variants, we demonstrate that FINEMAP provides higher precision and enables more detailed decomposition of regional heritability into individual variants than the variance component model implemented in BOLT or the fixed-effect model implemented in HESS. Using data from 51 serum biomarkers and four lipid traits from the FINRISK study, we estimate that FINEMAP captures on average 24% more regional heritability than the variant with the lowest P-value alone and 20% less than BOLT. Our simulations suggest how an upward bias of BOLT and a downward bias of FINEMAP could together explain the observed difference between the methods. We conclude that FINEMAP enables computationally efficient estimation of effect sizes and regional heritability in the era of biobank scale data.
]]></description>
<dc:creator>Benner, C.</dc:creator>
<dc:creator>Havulinna, A.</dc:creator>
<dc:creator>Salomaa, V.</dc:creator>
<dc:creator>Ripatti, S.</dc:creator>
<dc:creator>Pirinen, M.</dc:creator>
<dc:date>2018-05-10</dc:date>
<dc:identifier>doi:10.1101/318618</dc:identifier>
<dc:title><![CDATA[Refining fine-mapping: effect sizes and regional heritability]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-05-10</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/319509v1?rss=1">
<title>
<![CDATA[
Clustering of Type 2 Diabetes Genetic Loci by Multi-Trait Associations Identifies Disease Mechanisms and Subtypes 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/319509v1?rss=1"
</link>
<description><![CDATA[
BackgroundType 2 diabetes (T2D) is a heterogeneous disease for which 1) disease-causing pathways are incompletely understood and 2) sub-classification may improve patient management. Unlike other biomarkers, germline genetic markers do not change with disease progression or treatment. In this paper we test whether a germline genetic approach informed by physiology can be used to deconstruct T2D heterogeneity. First, we aimed to categorize genetic loci into groups representing likely disease mechanistic pathways. Second, we asked whether the novel clusters of genetic loci we identified have any broad clinical consequence, as assessed in four independent cohorts of individuals with T2D.nnMethods and FindingsIn an effort to identify mechanistic pathways driven by established T2D genetic loci, we applied Bayesian nonnegative matrix factorization clustering to genome-wide association results for 94 independent T2D genetic loci and 47 diabetes-related traits. We identified five robust clusters of T2D loci and traits, each with distinct tissue-specific enhancer enrichment based on analysis of epigenomic data from 28 cell types. Two clusters contained variant-trait associations indicative of reduced beta-cell function, differing from each other by high vs. low proinsulin levels. The three other clusters displayed features of insulin resistance: obesity-mediated (high BMI, waist circumference), "lipodystrophy-like" fat distribution (low BMI, adiponectin, HDL-cholesterol, and high triglycerides), and disrupted liver lipid metabolism (low triglycerides). Increased cluster GRSs were associated with distinct clinical outcomes, including increased blood pressure, coronary artery disease, and stroke risk. We evaluated the potential for clinical impact of these clusters in four studies containing participants with T2D (METSIM, N=487; Ashkenazi, N=509; Partners Biobank, N=2,065; UK Biobank N=14,813). Individuals with T2D in the top genetic risk score decile for each cluster reproducibly exhibited the predicted cluster-associated phenotypes, with ~30% of all participants assigned to just one cluster top decile.nnConclusionOur approach identifies salient T2D genetically anchored and physiologically informed pathways, and supports use of genetics to deconstruct T2D heterogeneity. Classification of patients by these genetic pathways may offer a step toward genetically informed T2D patient management.
]]></description>
<dc:creator>Udler, M. S.</dc:creator>
<dc:creator>Kim, J.</dc:creator>
<dc:creator>von Grotthuss, M.</dc:creator>
<dc:creator>Bonas-Guarch, S.</dc:creator>
<dc:creator>Mercader, J. M.</dc:creator>
<dc:creator>Cole, J. B.</dc:creator>
<dc:creator>Chiou, J.</dc:creator>
<dc:creator>Anderson, C. D.</dc:creator>
<dc:creator>Boehnke, M.</dc:creator>
<dc:creator>Laakso, M.</dc:creator>
<dc:creator>Atzmon, G.</dc:creator>
<dc:creator>Glaser, B.</dc:creator>
<dc:creator>Gaulton, K.</dc:creator>
<dc:creator>Flannick, J.</dc:creator>
<dc:creator>Getz, G.</dc:creator>
<dc:creator>Florez, J. C.</dc:creator>
<dc:date>2018-05-10</dc:date>
<dc:identifier>doi:10.1101/319509</dc:identifier>
<dc:title><![CDATA[Clustering of Type 2 Diabetes Genetic Loci by Multi-Trait Associations Identifies Disease Mechanisms and Subtypes]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-05-10</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/285304v1?rss=1">
<title>
<![CDATA[
Shared genetic contribution to type 1 and type 2 diabetes risk 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/285304v1?rss=1"
</link>
<description><![CDATA[
The role of shared genetic risk in the etiology of type 1 diabetes (T1D) and type 2 diabetes (T2D) and the mechanisms of these effects is unknown. In this study, we generated T1D association data of 15k samples imputed into the HRC reference panel which we compared to T2D association data of 159k samples imputed into 1000 Genomes. The effects of genetic variants on T1D and T2D risk at known loci and genome-wide were positively correlated, which we replicated using data from the UK Biobank and clinically-defined diabetes in the WTCCC. Increased risk of T1D and T2D was correlated with higher fasting insulin and fasting glucose level and decreased birth weight, among T1D- and T2D-specifc correlations, and T1D and T2D associated variants were enriched in regulatory elements for pancreatic, insulin resistance (adipose, CD19+ B cell), and developmental (CD184+ endoderm) cell types. We fine-mapped causal variants at known T1D and T2D loci and found evidence for co-localization at five signals, four of which had same direction of effect, including CENPW and GLIS3. Shared risk variants at GLIS3 and other signals were associated with measures of islet function, while CENPW was associated with early growth, and we identified shared risk variants at GLIS3 in islet accessible chromatin with allelic effects on islet regulatory activity. Our findings support shared genetic risk involving variants affecting islet function as well as insulin resistance, growth and development in the etiology of T1D and T2D.
]]></description>
<dc:creator>Aylward, A. J.</dc:creator>
<dc:creator>Chiou, J.</dc:creator>
<dc:creator>Okino, M.-L.</dc:creator>
<dc:creator>Kadakia, N.</dc:creator>
<dc:creator>Gaulton, K. J.</dc:creator>
<dc:date>2018-05-07</dc:date>
<dc:identifier>doi:10.1101/285304</dc:identifier>
<dc:title><![CDATA[Shared genetic contribution to type 1 and type 2 diabetes risk]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-05-07</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/315259v1?rss=1">
<title>
<![CDATA[
Genetic analyses in UK Biobank identifies 78 novel loci associated with urinary biomarkers providing new insights into the biology of kidney function and chronic disease 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/315259v1?rss=1"
</link>
<description><![CDATA[
BackgroundUrine biomarkers, such as creatinine, microalbumin, potassium and sodium are strongly associated with several common diseases including chronic kidney disease, cardiovascular disease and diabetes mellitus. Knowledge about the genetic determinants of the levels of these biomarker may shed light on pathophysiological mechanisms underlying the development of these diseases.nnMethodsWe performed genome-wide association studies of urinary levels of creatinine, microalbumin, potassium, and sodium in up to 326,441 unrelated individuals of European ancestry from the UK Biobank, a large population-based cohort study of over 500,000 individuals recruited across the United Kingdom in 2006-2010. Further, we explored genetic correlations, tissue-specific gene expression and possible causal genes related to these biomarkers.nnResultsWe identified 23 genome-wide significant independent loci associated with creatinine, 20 for microalbumin, 12 for potassium, and 38 for sodium. We confirmed several established associations including between the CUBN locus and microalbumin (rs141640975, p=3.11e-68). Variants associated with the levels of urinary creatinine, potassium, and sodium mapped to loci previously associated with obesity (GIPR, rs1800437, p=9.81e-10), caffeine metabolism (CYP1A1, rs2472297, p=1.61e-8) and triglycerides (GCKR, rs1260326, p=4.37e-16), respectively. We detected high pairwise genetic correlation between the levels of four urinary biomarkers, and significant genetic correlation between their levels and several anthropometric, cardiovascular, glycemic, lipid and kidney traits. We highlight GATM as causally implicated in the genetic control of urine creatinine, and GIPR, a potential diabetes drug target, as a plausible causal gene involved in regulation of urine creatinine and sodium.nnConclusionWe report 78 novel genome-wide significant associations with urinary levels of creatinine, microalbumin, potassium and sodium in the UK Biobank, confirming several previously established associations and providing new insights into the genetic basis of these traits and their connection to chronic diseases.nnAuthor SummaryUrine biomarkers, such as creatinine, microalbumin, potassium and sodium are strongly associated with several common diseases including chronic kidney disease, cardiovascular disease and diabetes mellitus. Knowledge about the genetic determinants of the levels of these biomarker may shed light on pathophysiological mechanisms underlying the development of these diseases. Here, we performed genome-wide association studies of urinary levels of creatinine, microalbumin, potassium and sodium in up to 326,441 unrelated individuals of European ancestry from the UK Biobank. Further, we explored genetic correlations, tissue-specific gene expression and possible causal genes related to these biomarkers. We identified 78 novel genome-wide significant associations with urinary biomarkers, confirming several previously established associations and providing new insights into the genetic basis of these traits and their connection to chronic diseases. Further, we highlight GATM as causally implicated in the genetic control of urine creatinine, and GIPR, a potential diabetes drug target, as a plausible causal gene involved in regulation of urine creatinine and sodium. The knowledge arising from our work may improve the predictive utility of the respective biomarker and point to new therapeutic strategies to prevent common diseases.
]]></description>
<dc:creator>Zanetti, D.</dc:creator>
<dc:creator>Rao, A.</dc:creator>
<dc:creator>Gustafsson, S.</dc:creator>
<dc:creator>Assimes, T.</dc:creator>
<dc:creator>Montgomery, S. B.</dc:creator>
<dc:creator>Ingelsson, E.</dc:creator>
<dc:date>2018-05-05</dc:date>
<dc:identifier>doi:10.1101/315259</dc:identifier>
<dc:title><![CDATA[Genetic analyses in UK Biobank identifies 78 novel loci associated with urinary biomarkers providing new insights into the biology of kidney function and chronic disease]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-05-05</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/311332v1?rss=1">
<title>
<![CDATA[
The landscape of pervasive horizontal pleiotropy in human genetic variation is driven by extreme polygenicity of human traits and diseases 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/311332v1?rss=1"
</link>
<description><![CDATA[
Horizontal pleiotropy, where one variant has independent effects on multiple traits, is important for our understanding of the genetic architecture of human phenotypes. We develop a method to quantify horizontal pleiotropy using genome-wide association summary statistics and apply it to 372 heritable phenotypes measured in 361,194 UK Biobank individuals. Horizontal pleiotropy is pervasive throughout the human genome, prominent among highly polygenic phenotypes, and enriched in active regulatory regions. Our results highlight the central role horizontal pleiotropy plays in the genetic architecture of human phenotypes. The HOrizontal Pleiotropy Score (HOPS) method is available on Github at https://github.com/rondolab/HOPS.
]]></description>
<dc:creator>Jordan, D. M.</dc:creator>
<dc:creator>Verbanck, M.</dc:creator>
<dc:creator>Do, R.</dc:creator>
<dc:date>2018-04-30</dc:date>
<dc:identifier>doi:10.1101/311332</dc:identifier>
<dc:title><![CDATA[The landscape of pervasive horizontal pleiotropy in human genetic variation is driven by extreme polygenicity of human traits and diseases]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-04-30</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/114405v1?rss=1">
<title>
<![CDATA[
Genetics of educational attainment aid in identifying biological subcategories of schizophrenia 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/114405v1?rss=1"
</link>
<description><![CDATA[
Higher educational attainment (EA) is negatively associated with schizophrenia (SZ). However, recent studies found a positive genetic correlation between EA and SZ. We investigated possible causes of this counterintuitive finding using genome-wide association study results for EA and SZ (N = 443,581) and a replication cohort (1,169 controls; 1,067 cases) with deeply phenotyped SZ patients. We found strong genetic dependence between EA and SZ that cannot be explained by chance, linkage disequilibrium, or assortative mating. Instead, several genes seem to have pleiotropic effects on EA and SZ, but without a clear pattern of sign concordance. Genetic heterogeneity of SZ contributes to this finding. We demonstrate this by showing that the polygenic prediction of clinical SZ symptoms can be improved by taking the sign concordance of loci for EA and SZ into account. Furthermore, using EA as a proxy phenotype, we isolate FOXO6 and SLITRK1 as novel candidate genes for SZ.
]]></description>
<dc:creator>Bansal, V.</dc:creator>
<dc:creator>Mitjans, M.</dc:creator>
<dc:creator>Burik, C. A. P.</dc:creator>
<dc:creator>Karlsson Linner, R.</dc:creator>
<dc:creator>Okbay, A.</dc:creator>
<dc:creator>Rietveld, C. A.</dc:creator>
<dc:creator>Begemann, M.</dc:creator>
<dc:creator>Bonn, S.</dc:creator>
<dc:creator>Ripke, S.</dc:creator>
<dc:creator>Nivard, M. G.</dc:creator>
<dc:creator>Ehrenreich, H.</dc:creator>
<dc:creator>Koellinger, P. D.</dc:creator>
<dc:date>2017-03-08</dc:date>
<dc:identifier>doi:10.1101/114405</dc:identifier>
<dc:title><![CDATA[Genetics of educational attainment aid in identifying biological subcategories of schizophrenia]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-03-08</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/242776v1?rss=1">
<title>
<![CDATA[
The Shared Genetic Basis of Human Fluid Intelligence and Brain Morphology 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/242776v1?rss=1"
</link>
<description><![CDATA[
Individual differences in educational attainment are linked to differences in intelligence, and predict important social, economic and health outcomes. Previous studies have found common genetic factors that influence educational achievement, cognitive performance and total brain volume (i.e., brain size). Here, in a large sample of participants from the UK Biobank, we investigate the shared genetic basis between educational attainment and fine-grained cerebral cortical morphological features, and associate this genetic variation with a related aspect of cognitive ability. Importantly, we execute novel statistical methods that enable high-dimensional genetic correlation analysis, and compute high-resolution surface maps for the genetic correlations between educational attainment and vertex-wise morphological measurements. We conduct secondary analyses, using the UK Biobank verbal-numerical reasoning score, to confirm that variation in educational attainment that is genetically correlated with cortical morphology is related to differences in cognitive performance. Our analyses reveal the genetic overlap between cognitive ability and cortical thickness measurements in bilateral primary motor cortex and predominantly left superior temporal cortex and proximal regions. These findings may contribute to our understanding of the neurobiology that connects genetic variation to individual differences in educational attainment and cognitive performance.
]]></description>
<dc:creator>Ge, T.</dc:creator>
<dc:creator>Chen, C.-Y.</dc:creator>
<dc:creator>Vettermann, R.</dc:creator>
<dc:creator>Tuominen, L. J.</dc:creator>
<dc:creator>Holt, D. J.</dc:creator>
<dc:creator>Sabuncu, M. R.</dc:creator>
<dc:creator>Smoller, J. W.</dc:creator>
<dc:date>2018-01-04</dc:date>
<dc:identifier>doi:10.1101/242776</dc:identifier>
<dc:title><![CDATA[The Shared Genetic Basis of Human Fluid Intelligence and Brain Morphology]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-01-04</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/306209v1?rss=1">
<title>
<![CDATA[
An examination of multivariable Mendelian randomization in the single sample and two-sample summary data settings. 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/306209v1?rss=1"
</link>
<description><![CDATA[
BackgroundMendelian Randomisation (MR) is a powerful tool in epidemiology which can be used to estimate the causal effect of an exposure on an outcome in the presence of unobserved confounding, by utilising genetic variants that are instrumental variables (IVs) for the exposure. This has been extended to Multivariable MR (MVMR) to estimate the effect of two or more exposures on an outcome.nnMethods/ResultsWe use simulations and theory to clarify the interpretation of estimated effects in a MVMR analysis under a range of underlying scenarios, where a secondary exposure acts variously as a confounder, a mediator, a pleiotropic pathway and a collider. We then describe how instrument strength and validity can be assessed for an MVMR analysis in the single sample setting, and develop tests to assess these assumptions in the popular two-sample summary data setting. We illustrate our methods using data from UK biobank to estimate the effect of education and cognitive ability on body mass index.nnConclusionMVMR analysis consistently estimates the effect of an exposure, or exposures, of interest and provides a powerful tool for determining causal effects in a wide range of scenarios with either individual or summary level data.
]]></description>
<dc:creator>Sanderson, E.</dc:creator>
<dc:creator>Davey Smith, G.</dc:creator>
<dc:creator>Windmeijer, F.</dc:creator>
<dc:creator>Bowden, J.</dc:creator>
<dc:date>2018-04-27</dc:date>
<dc:identifier>doi:10.1101/306209</dc:identifier>
<dc:title><![CDATA[An examination of multivariable Mendelian randomization in the single sample and two-sample summary data settings.]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-04-27</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/194019v1?rss=1">
<title>
<![CDATA[
Estimation of genetic correlation using linkage disequilibrium score regression and genomic restricted maximum likelihood 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/194019v1?rss=1"
</link>
<description><![CDATA[
Genetic correlation is a key population parameter that describes the shared genetic architecture of complex traits and diseases. It can be estimated by current state-of-art methods, i.e. linkage disequilibrium score regression (LDSC) and genomic restricted maximum likelihood (GREML). The massively reduced computing burden of LDSC compared to GREML makes it an attractive tool, although the accuracy (i.e., magnitude of standard errors) of LDSC estimates has not been thoroughly studied. In simulation, we show that the accuracy of GREML is generally higher than that of LDSC. When there is genetic heterogeneity between the actual sample and reference data from which LD scores are estimated, the accuracy of LDSC decreases further. In real data analyses estimating the genetic correlation between schizophrenia (SCZ) and body mass index, we show that GREML estimates based on ~150,000 individuals give a higher accuracy than LDSC estimates based on ~400,000 individuals (from combined meta-data). A GREML genomic partitioning analysis reveals that the genetic correlation between SCZ and height is significantly negative for regulatory regions, which whole genome or LDSC approach has less power to detect. We conclude that LDSC estimates should be carefully interpreted as there can be uncertainty about homogeneity among combined meta-data sets. We suggest that any interesting findings from massive LDSC analysis for a large number of complex traits should be followed up, where possible, with more detailed analyses with GREML methods, even if sample sizes are lesser.
]]></description>
<dc:creator>Ni, G.</dc:creator>
<dc:creator>Moser, G.</dc:creator>
<dc:creator>Schizophrenia Working Group of the Psychiatric Gen,</dc:creator>
<dc:creator>Wray, N. R.</dc:creator>
<dc:creator>Lee, S. H.</dc:creator>
<dc:date>2017-09-27</dc:date>
<dc:identifier>doi:10.1101/194019</dc:identifier>
<dc:title><![CDATA[Estimation of genetic correlation using linkage disequilibrium score regression and genomic restricted maximum likelihood]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-09-27</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/187625v1?rss=1">
<title>
<![CDATA[
Statistical machine learning of sleep and physical activity phenotypes from sensor data in 96,609 UK Biobank participants 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/187625v1?rss=1"
</link>
<description><![CDATA[
Current public health guidelines on physical activity and sleep duration are limited by a reliance on subjective self-reported evidence. Using data from simple wrist-worn activity monitors, we developed a tailored machine learning model, using balanced random forests with Hidden Markov Models, to reliably detect a number of activity modes. We show that physical activity and sleep behaviours can be classified with 87% accuracy in 159,504 minutes of recorded free-living behaviours from 132 adults. These trained models can be used to infer fine resolution activity patterns at the population scale in 96,220 participants. For example, we find that men spend more time in both low- and high-intensity behaviours, while women spend more time in mixed behaviours. Walking time is highest in spring and sleep time lowest during the summer. This work opens the possibility of future public health guidelines informed by the health consequences associated with specific, objectively measured, physical activity and sleep behaviours.
]]></description>
<dc:creator>Willetts, M.</dc:creator>
<dc:creator>Hollowell, S.</dc:creator>
<dc:creator>Aslett, L.</dc:creator>
<dc:creator>Holmes, C.</dc:creator>
<dc:creator>Doherty, A.</dc:creator>
<dc:date>2017-09-12</dc:date>
<dc:identifier>doi:10.1101/187625</dc:identifier>
<dc:title><![CDATA[Statistical machine learning of sleep and physical activity phenotypes from sensor data in 96,609 UK Biobank participants]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-09-12</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/304741v1?rss=1">
<title>
<![CDATA[
Testing the causal effects between subjective wellbeing and physical health using Mendelian randomisation 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/304741v1?rss=1"
</link>
<description><![CDATA[
ObjectivesTo investigate whether the association between subjective wellbeing (subjective happiness and life satisfaction) and physical health is causal.nnDesignWe conducted two-sample bidirectional Mendelian randomisation between subjective wellbeing and six measures of physical health: coronary artery disease, myocardial infarction, total cholesterol, HDL cholesterol, LDL cholesterol and body mass index (BMI).nnParticipantsWe used summary data from four large genome-wide association study consortia: CARDIoGRAMplusC4D for coronary artery disease and myocardial infarction; the Global Lipids Genetics Consortium for cholesterol measures; the Genetic Investigation of Anthropometric Traits consortium for BMI; and the Social Science Genetics Association Consortium for subjective wellbeing. A replication analysis was conducted using 337,112 individuals from the UK Biobank (54% female, mean age =56.87, SD=8.00 years at recruitment).nnMain outcome measuresCoronary artery disease, myocardial infarction, total cholesterol, HDL cholesterol, LDL cholesterol, BMI and subjective wellbeing.nnResultsThere was evidence of a causal effect of BMI on subjective wellbeing such that each 1 kg/m2 increase in BMI caused a 0.045 (95%CI 0.006 to 0.084, p=0.023) SD reduction in subjective wellbeing. Replication analyses provided strong evidence of an effect of BMI on satisfaction with health ({beta}=0.034 (95% CI: -0.042 to -0.026) unit decrease in health satisfaction per SD increase in BMI, p<2-16). There was no clear evidence of a causal effect between subjective wellbeing and the other physical health measures in either direction.nnConclusionsOur results suggest that a higher BMI lowers subjective wellbeing. Our replication analysis confirmed this finding, suggesting the effect in middle-age is driven by satisfaction with health. BMI is a modifiable determinant and therefore, our study provides further motivation to tackle the obesity epidemic because of the knock-on effects of higher BMI on subjective wellbeing.
]]></description>
<dc:creator>Wootton, R. E.</dc:creator>
<dc:creator>Lawn, R. B.</dc:creator>
<dc:creator>Millard, L. A. C.</dc:creator>
<dc:creator>Davies, N. M.</dc:creator>
<dc:creator>Taylor, A. E.</dc:creator>
<dc:creator>Munafo, M. R.</dc:creator>
<dc:creator>Timpson, N. J.</dc:creator>
<dc:creator>Davis, O. S. P.</dc:creator>
<dc:creator>Davey Smith, G.</dc:creator>
<dc:creator>Haworth, C. M. A.</dc:creator>
<dc:date>2018-04-20</dc:date>
<dc:identifier>doi:10.1101/304741</dc:identifier>
<dc:title><![CDATA[Testing the causal effects between subjective wellbeing and physical health using Mendelian randomisation]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-04-20</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/303925v1?rss=1">
<title>
<![CDATA[
Genetic studies of accelerometer-based sleep measures in 85,670 individuals yield new insights into human sleep behaviour 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/303925v1?rss=1"
</link>
<description><![CDATA[
Sleep is an essential human function but its regulation is poorly understood. Identifying genetic variants associated with quality, quantity and timing of sleep will provide biological insights into the regulation of sleep and potential links with disease. Using accelerometer data from 85,670 individuals in the UK Biobank, we performed a genome-wide association study of 8 accelerometer-derived sleep traits, 5 of which are not accessible through self-report alone. We identified 47 genetic associations across the sleep traits (P<5x10-8) and replicated our findings in 5,819 individuals from 3 independent studies. These included 26 novel associations for sleep quality and 10 for nocturnal sleep duration. The majority of newly identified variants were associated with a single sleep trait, except for variants previously associated with restless legs syndrome that were associated with multiple sleep traits. Of the new associated and replicated sleep duration loci, we were able to fine-map a missense variant (p.Tyr727Cys) in PDE11A, a dual-specificity 3,5-cyclic nucleotide phosphodiesterase expressed in the hippocampus, as the likely causal variant. As a group, sleep quality loci were enriched for serotonin processing genes and all sleep traits were enriched for cerebellar-expressed genes. These findings provide new biological insights into sleep characteristics.
]]></description>
<dc:creator>Jones, S. E.</dc:creator>
<dc:creator>van Hees, V. T.</dc:creator>
<dc:creator>Mazzotti, D. R.</dc:creator>
<dc:creator>Marques-Vidal, P.</dc:creator>
<dc:creator>Sabia, S.</dc:creator>
<dc:creator>van der Spek, A.</dc:creator>
<dc:creator>Dashti, H. S.</dc:creator>
<dc:creator>Engmann, J.</dc:creator>
<dc:creator>Kocevska, D.</dc:creator>
<dc:creator>Tyrrell, J.</dc:creator>
<dc:creator>Beaumont, R. N.</dc:creator>
<dc:creator>Hillsdon, M.</dc:creator>
<dc:creator>Ruth, K. S.</dc:creator>
<dc:creator>Tuke, M. A.</dc:creator>
<dc:creator>Yaghootkar, H.</dc:creator>
<dc:creator>Sharp, S.</dc:creator>
<dc:creator>Jie, Y.</dc:creator>
<dc:creator>Harrison, J. W.</dc:creator>
<dc:creator>Freathy, R. M.</dc:creator>
<dc:creator>Murray, A.</dc:creator>
<dc:creator>Luik, A. I.</dc:creator>
<dc:creator>Amin, N.</dc:creator>
<dc:creator>Lane, J. M.</dc:creator>
<dc:creator>Saxena, R.</dc:creator>
<dc:creator>Rutter, M. K.</dc:creator>
<dc:creator>Tiemeier, H.</dc:creator>
<dc:creator>Kutalik, Z.</dc:creator>
<dc:creator>Kumari, M.</dc:creator>
<dc:creator>Frayling, T. M.</dc:creator>
<dc:creator>Weedon, M. N.</dc:creator>
<dc:creator>Gehrman, P.</dc:creator>
<dc:creator>Wood, A. R.</dc:creator>
<dc:date>2018-04-19</dc:date>
<dc:identifier>doi:10.1101/303925</dc:identifier>
<dc:title><![CDATA[Genetic studies of accelerometer-based sleep measures in 85,670 individuals yield new insights into human sleep behaviour]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-04-19</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/274977v1?rss=1">
<title>
<![CDATA[
GWAS in 446,118 European adults identifies 78 genetic loci for self-reported habitual sleep duration supported by accelerometer-derived estimates 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/274977v1?rss=1"
</link>
<description><![CDATA[
Sleep is an essential homeostatically-regulated state of decreased activity and alertness conserved across animal species, and both short and long sleep duration associate with chronic disease and all-cause mortality1,2. Defining genetic contributions to sleep duration could point to regulatory mechanisms and clarify causal disease relationships. Through genome-wide association analyses in 446,118 participants of European ancestry from the UK Biobank, we discover 78 loci for self-reported sleep duration that further impact accelerometer-derived measures of sleep duration, daytime inactivity duration, sleep efficiency and number of sleep bouts in a subgroup (n=85,499) with up to 7-day accelerometry. Associations are enriched for genes expressed in several brain regions, and for pathways including striatum and subpallium development, mechanosensory response, dopamine binding, synaptic neurotransmission, catecholamine production, synaptic plasticity, and unsaturated fatty acid metabolism. Genetic correlation analysis indicates shared biological links between sleep duration and psychiatric, cognitive, anthropometric and metabolic traits and Mendelian randomization highlights a causal link of longer sleep with schizophrenia.
]]></description>
<dc:creator>Dashti, H.</dc:creator>
<dc:creator>Jones, S. E.</dc:creator>
<dc:creator>Wood, A. R.</dc:creator>
<dc:creator>Lane, J.</dc:creator>
<dc:creator>van Hees, V. T.</dc:creator>
<dc:creator>Wang, H.</dc:creator>
<dc:creator>Rhodes, J. A.</dc:creator>
<dc:creator>Song, Y.</dc:creator>
<dc:creator>Patel, K.</dc:creator>
<dc:creator>Anderson, S. G.</dc:creator>
<dc:creator>Beaumont, R. N.</dc:creator>
<dc:creator>Bechtold, D. A.</dc:creator>
<dc:creator>Bowden, J.</dc:creator>
<dc:creator>Cade, B. E.</dc:creator>
<dc:creator>Garaulet, M.</dc:creator>
<dc:creator>Kyle, S. D.</dc:creator>
<dc:creator>Little, M. A.</dc:creator>
<dc:creator>Loudon, A. S.</dc:creator>
<dc:creator>Luik, A. I.</dc:creator>
<dc:creator>Scheer, F. A.</dc:creator>
<dc:creator>Spiegelhalder, K.</dc:creator>
<dc:creator>Tyrrell, J.</dc:creator>
<dc:creator>Gottlieb, D. J.</dc:creator>
<dc:creator>Tiemeier, H.</dc:creator>
<dc:creator>Ray, D. W.</dc:creator>
<dc:creator>Purcell, S. M.</dc:creator>
<dc:creator>Frayling, T. M.</dc:creator>
<dc:creator>Redline, S.</dc:creator>
<dc:creator>Lawlor, D. A.</dc:creator>
<dc:creator>Rutter, M. K.</dc:creator>
<dc:creator>Weedon, M. N.</dc:creator>
<dc:creator>Saxena, R.</dc:creator>
<dc:date>2018-04-19</dc:date>
<dc:identifier>doi:10.1101/274977</dc:identifier>
<dc:title><![CDATA[GWAS in 446,118 European adults identifies 78 genetic loci for self-reported habitual sleep duration supported by accelerometer-derived estimates]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-04-19</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/303941v1?rss=1">
<title>
<![CDATA[
Genome-wide association analyses of chronotype in 697,828 individuals provides new insights into circadian rhythms in humans and links to disease 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/303941v1?rss=1"
</link>
<description><![CDATA[
Using genome-wide data from 697,828 research participants from 23andMe and UK Biobank, we increase the number of identified loci associated with being a morning person, a behavioural indicator of a persons underlying circadian rhythm, from 24 to 351. Using data from 85,760 individuals with activity-monitor derived measures of sleep timing we show that the chronotype loci influence sleep timing: the mean sleep timing of the 5% of individuals carrying the most "morningness" alleles was 25 minutes earlier than the 5% carrying the fewest. The loci were enriched for genes involved in circadian regulation, cAMP, glutamate and insulin signalling pathways, and those expressed in the retina, hindbrain, hypothalamus, and pituitary. We provide evidence that being a morning person is causally associated with better mental health but does not appear to affect BMI or Type 2 diabetes. This study offers new insights into the biology of circadian rhythms and links to disease in humans.
]]></description>
<dc:creator>Jones, S. E.</dc:creator>
<dc:creator>Lane, J. M.</dc:creator>
<dc:creator>Wood, A. R.</dc:creator>
<dc:creator>van Hees, V. T.</dc:creator>
<dc:creator>Tyrrell, J.</dc:creator>
<dc:creator>Beaumont, R. N.</dc:creator>
<dc:creator>Jeffries, A. R.</dc:creator>
<dc:creator>Dashti, H. S.</dc:creator>
<dc:creator>Hillsdon, M.</dc:creator>
<dc:creator>Ruth, K. S.</dc:creator>
<dc:creator>Tuke, M. A.</dc:creator>
<dc:creator>Yagohootkar, H.</dc:creator>
<dc:creator>Sharp, S. A.</dc:creator>
<dc:creator>Ji, Y.</dc:creator>
<dc:creator>Harrison, J. W.</dc:creator>
<dc:creator>Dawes, A.</dc:creator>
<dc:creator>Byrne, E. M.</dc:creator>
<dc:creator>Tiemeier, H.</dc:creator>
<dc:creator>Allebrandt, K. V.</dc:creator>
<dc:creator>Bowden, J.</dc:creator>
<dc:creator>Ray, D. W.</dc:creator>
<dc:creator>Freathy, R. M.</dc:creator>
<dc:creator>Murray, A.</dc:creator>
<dc:creator>Mazzotti, D. R.</dc:creator>
<dc:creator>Gehrman, P. R.</dc:creator>
<dc:creator>23andMe Research Team,</dc:creator>
<dc:creator>Lawlor, D. A.</dc:creator>
<dc:creator>Frayling, T. M.</dc:creator>
<dc:creator>Rutter, M. K.</dc:creator>
<dc:creator>Hinds, D. A.</dc:creator>
<dc:creator>Saxena, R.</dc:creator>
<dc:creator>Weedon, M. N.</dc:creator>
<dc:date>2018-04-19</dc:date>
<dc:identifier>doi:10.1101/303941</dc:identifier>
<dc:title><![CDATA[Genome-wide association analyses of chronotype in 697,828 individuals provides new insights into circadian rhythms in humans and links to disease]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-04-19</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/299826v1?rss=1">
<title>
<![CDATA[
The effect of education and general cognitive ability on smoking: A Mendelian randomisation study 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/299826v1?rss=1"
</link>
<description><![CDATA[
Recent analyses have shown educational attainment to be associated with a number of health outcomes. This association may, in part, be due to an effect of educational attainment on smoking behaviour. In this study we apply a multivariable Mendelian randomisation design to determine whether the effect of educational attainment on smoking behaviour could be due to educational attainment or general cognitive ability. We use individual data from the UK Biobank study (N = 120,050) and summary data from large GWAS studies of educational attainment, cognitive ability and smoking behaviour. Our results show that more years of education are associated with a reduced likelihood of smoking which is not due to an effect of general cognitive ability on smoking behaviour. Given the considerable physical harms associated with smoking, the effect of educational attainment on smoking is likely to contribute to the health inequalities associated with differences in educational attainment.
]]></description>
<dc:creator>Sanderson, E.</dc:creator>
<dc:creator>Davey Smith, G.</dc:creator>
<dc:creator>Munafo, M.</dc:creator>
<dc:date>2018-04-16</dc:date>
<dc:identifier>doi:10.1101/299826</dc:identifier>
<dc:title><![CDATA[The effect of education and general cognitive ability on smoking: A Mendelian randomisation study]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-04-16</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/300889v1?rss=1">
<title>
<![CDATA[
Identification of 12 genetic loci associated with human healthspan 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/300889v1?rss=1"
</link>
<description><![CDATA[
The mounting challenge of preserving the quality of life in an aging population directs the focus of longevity science to the regulatory pathways controlling healthspan. To understand the nature of the relationship between the healthspan and lifespan and uncover the genetic architecture of the two phenotypes, we studied the incidence of major age-related diseases in the UK Biobank (UKB) cohort. We observed that the incidence rates of major chronic diseases increase exponentially. The risk of disease acquisition doubled approximately every eight years, i.e., at a rate compatible with the doubling time of the Gompertz mortality law. Assuming that aging is the single underlying factor behind the morbidity rates dynamics, we built a proportional hazards model to predict the risks of the diseases and therefore the age corresponding to the end of healthspan of an individual depending on their age, gender, and the genetic background. We suggested a computationally efficient procedure for the determination of the effect size and statistical significance of individual gene variants associations with healthspan in a form suitable for a Genome-Wide Association Studies (GWAS). Using the UKB sub-population of 300,447 genetically Caucasian, British individuals as a discovery cohort, we identified 12 loci associated with healthspan and reaching the whole-genome level of significance. We observed strong (|{rho}g| > 0.3) genetic correlations between healthspan and the incidence of specific age-related disease present in our healthspan definition (with the notable exception of dementia). Other examples included all-cause mortality (as derived from parental survival, with{rho} g = -0.76), life-history traits (metrics of obesity, age at first birth), levels of different metabolites (lipids, amino acids, glycemic traits), and psychological traits (smoking behaviour, cognitive performance, depressive symptoms, insomnia). We conclude by noting that the healthspan phenotype, suggested and characterized here, offers a promising new way to investigate human longevity by exploiting the data from genetic and clinical data on living individuals.
]]></description>
<dc:creator>Zenin, A.</dc:creator>
<dc:creator>Tsepilov, Y.</dc:creator>
<dc:creator>Sharapov, S.</dc:creator>
<dc:creator>Getmantsev, E.</dc:creator>
<dc:creator>Menshikov, L.</dc:creator>
<dc:creator>Fedichev, P.</dc:creator>
<dc:creator>Aulchenko, Y.</dc:creator>
<dc:date>2018-04-16</dc:date>
<dc:identifier>doi:10.1101/300889</dc:identifier>
<dc:title><![CDATA[Identification of 12 genetic loci associated with human healthspan]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-04-16</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/294876v1?rss=1">
<title>
<![CDATA[
Common genetic variants and health outcomes appear geographically structured in the UK Biobank sample: Old concerns returning and their implications. 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/294876v1?rss=1"
</link>
<description><![CDATA[
Introductory paragraphThe inclusion of genetic data in large studies has enabled the discovery of genetic contributions to complex traits and their application in applied analyses including those using genetic risk scores (GRS) for the prediction of phenotypic variance. If genotypes show structure by location and coincident structure exists for the trait of interest, analyses can be biased. Having illustrated structure in an apparently homogeneous collection, we aimed to a) test for geographical stratification of genotypes in UK Biobank and b) assess whether stratification might induce bias in genetic association analysis.nnWe found that single genetic variants are associated with birth location within UK Biobank and that geographic structure in genetic data could not be accounted for using routine adjustment for study centre and principal components (PCs) derived from genotype data. We found that GRS for complex traits do appear geographically structured and analysis using GRS can yield biased associations. We discuss the likely origins of these observations and potential implications for analysis within large-scale population based genetic studies.
]]></description>
<dc:creator>Haworth, S.</dc:creator>
<dc:creator>Mitchell, R.</dc:creator>
<dc:creator>Corbin, L.</dc:creator>
<dc:creator>Wade, K. H.</dc:creator>
<dc:creator>Dudding, T.</dc:creator>
<dc:creator>Budu-Aggrey, A.</dc:creator>
<dc:creator>Carslake, D.</dc:creator>
<dc:creator>Hemani, G.</dc:creator>
<dc:creator>Paternoster, L.</dc:creator>
<dc:creator>Davey Smith, G.</dc:creator>
<dc:creator>Davies, N.</dc:creator>
<dc:creator>Lawson, D.</dc:creator>
<dc:creator>Timpson, N.</dc:creator>
<dc:date>2018-04-11</dc:date>
<dc:identifier>doi:10.1101/294876</dc:identifier>
<dc:title><![CDATA[Common genetic variants and health outcomes appear geographically structured in the UK Biobank sample: Old concerns returning and their implications.]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-04-11</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/297572v1?rss=1">
<title>
<![CDATA[
Low-frequency variant functional architectures reveal strength of negative selection across coding and non-coding annotations 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/297572v1?rss=1"
</link>
<description><![CDATA[
Common variant heritability is known to be concentrated in variants within cell-type-specific non-coding functional annotations, with a limited role for common coding variants. However, little is known about the functional distribution of low-frequency variant heritability. Here, we partitioned the heritability of both low-frequency (0.5% [&le;] MAF < 5%) and common (MAF [&ge;] 5%) variants in 40 UK Biobank traits (average N = 363K) across a broad set of coding and non-coding functional annotations, employing an extension of stratified LD score regression to low-frequency variants that produces robust results in simulations. We determined that non-synonymous coding variants explain 17{+/-}1% of low-frequency variant heritability [Formula] versus only 2.1{+/-}0.2% of common variant heritability [Formula], and that regions conserved in primates explain nearly half of [Formula] (43{+/-}2%). Other annotations previously linked to negative selection, including non-synonymous variants with high PolyPhen-2 scores, non-synonymous variants in genes under strong selection, and low-LD variants, were also significantly more enriched for [Formula] as compared to [Formula]. Cell-type-specific non-coding annotations that were significantly enriched for [Formula] of corresponding traits tended to be similarly enriched for [Formula] for most traits, but more enriched for brain-related annotations and traits. For example, H3K4me3 marks in brain DPFC explain 57{+/-}12% of [Formula] vs. 12{+/-}2% of [Formula] for neuroticism, implicating the action of negative selection on low-frequency variants affecting gene regulation in the brain. Forward simulations confirmed that the ratio of low-frequency variant enrichment vs. common variant enrichment primarily depends on the mean selection coefficient of causal variants in the annotation, and can be used to predict the effect size variance of causal rare variants (MAF < 0.5%) in the annotation, informing their prioritization in whole-genome sequencing studies. Our results provide a deeper understanding of low-frequency variant functional architectures and guidelines for the design of association studies targeting functional classes of low-frequency and rare variants.
]]></description>
<dc:creator>Gazal, S.</dc:creator>
<dc:creator>Loh, P.-R.</dc:creator>
<dc:creator>Finucane, H.</dc:creator>
<dc:creator>Ganna, A.</dc:creator>
<dc:creator>Schoech, A.</dc:creator>
<dc:creator>Sunyaev, S.</dc:creator>
<dc:creator>Price, A.</dc:creator>
<dc:date>2018-04-09</dc:date>
<dc:identifier>doi:10.1101/297572</dc:identifier>
<dc:title><![CDATA[Low-frequency variant functional architectures reveal strength of negative selection across coding and non-coding annotations]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-04-09</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/294116v1?rss=1">
<title>
<![CDATA[
Epigenetic prediction of complex traits and death 
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</title>
<link>
https://biorxiv.org/cgi/content/short/294116v1?rss=1"
</link>
<description><![CDATA[
BackgroundGenome-wide DNA methylation (DNAm) profiling has allowed for the development of molecular predictors for a multitude of traits and diseases. Such predictors may be more accurate than the self-reported phenotypes, and could have clinical applications. Here, penalised regression models were used to develop DNAm predictors for body mass index (BMI), smoking status, alcohol consumption, and educational attainment in a cohort of 5,100 individuals. Using an independent test cohort comprising 906 individuals, the proportion of phenotypic variance explained in each trait was examined for DNAm-based and genetic predictors. Receiver operator characteristic curves were generated to investigate the predictive performance of DNAm-based predictors, using dichotomised phenotypes. The relationship between DNAm scores and all-cause mortality (n = 214 events) was assessed via Cox proportional-hazards models.nnResultsThe DNAm-based predictors explained different proportions of the phenotypic variance for BMI (12%), smoking (60%), alcohol consumption (12%) and education (3%). The combined genetic and DNAm predictors explained 20% of the variance in BMI, 61% in smoking, 13% in alcohol consumption, and 6% in education. DNAm predictors for smoking, alcohol, and education but not BMI predicted mortality in univariate models. The predictors showed moderate discrimination of obesity (AUC=0.67) and alcohol consumption (AUC=0.75), and excellent discrimination of current smoking status (AUC=0.98). There was poorer discrimination of college-educated individuals (AUC=0.59).nnConclusionsDNAm predictors correlate with lifestyle factors that are associated with health and mortality. They may supplement DNAm-based predictors of age to identify the lifestyle profiles of individuals and predict disease risk.nnList of abbreviations
]]></description>
<dc:creator>McCartney, D. L.</dc:creator>
<dc:creator>Stevenson, A. J.</dc:creator>
<dc:creator>Ritchie, S. J.</dc:creator>
<dc:creator>Walker, R. M.</dc:creator>
<dc:creator>Zhang, Q.</dc:creator>
<dc:creator>Morris, S. W.</dc:creator>
<dc:creator>Campbell, A.</dc:creator>
<dc:creator>Murray, A. D.</dc:creator>
<dc:creator>Whalley, H. C.</dc:creator>
<dc:creator>Gale, C. R.</dc:creator>
<dc:creator>Porteous, D. J.</dc:creator>
<dc:creator>Haley, C. S.</dc:creator>
<dc:creator>McRae, A. F.</dc:creator>
<dc:creator>Wray, N. R.</dc:creator>
<dc:creator>Visscher, P. M.</dc:creator>
<dc:creator>McIntosh, A. M.</dc:creator>
<dc:creator>Evans, K. L.</dc:creator>
<dc:creator>Deary, I. J.</dc:creator>
<dc:creator>Marioni, R. E.</dc:creator>
<dc:date>2018-04-03</dc:date>
<dc:identifier>doi:10.1101/294116</dc:identifier>
<dc:title><![CDATA[Epigenetic prediction of complex traits and death]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-04-03</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/291872v1?rss=1">
<title>
<![CDATA[
Relationships between estimated autozygosity and complex traits in the UK Biobank 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/291872v1?rss=1"
</link>
<description><![CDATA[
Inbreeding increases the risk of certain Mendelian disorders in humans but may also reduce fitness through its effects on complex traits and diseases. Such inbreeding depression is thought to occur due to increased homozygosity at causal variants that are recessive with respect to fitness. Until recently it has been difficult to amass large enough sample sizes to investigate the effects of inbreeding depression on complex traits using genome-wide single nucleotide polymorphism (SNP) data in population-based samples. Further, it is difficult to infer causation in analyses that relate degree of inbreeding to complex traits because confounding variables (e.g., education) may influence both the likelihood for parents to outbreed and offspring trait values. The present study used runs of homozygosity in genome-wide SNP data in up to 400,000 individuals in the UK Biobank to estimate the proportion of the autosome that exists in autozygous tracts--stretches of the genome which are identical due to a shared common ancestor. After multiple testing corrections and controlling for possible sociodemographic confounders, we found significant relationships in the predicted direction between estimated autozygosity and three of the 26 traits we investigated: age at first sexual intercourse, fluid intelligence, and forced expiratory volume in 1 second. Our findings for fluid intelligence and forced expiratory volume corroborate those of several published studies while the finding for age at first sexual intercourse was novel. These results may suggest that these traits have been associated with Darwinian fitness over evolutionary time, although there are other possible explanations for these associations that cannot be eliminated. Some of the autozygosity-trait relationships were attenuated after controlling for background sociodemographic characteristics, suggesting that care needs to be taken in the design and interpretation of ROH studies in order to glean reliable information about the genetic architecture and evolutionary history of complex traits.nnAuthor SummaryInbreeding is well known to increase the risk of rare, monogenic diseases, and there has been some evidence that it also affects complex traits, such as cognition and educational attainment. However, difficulties can arise when inferring causation in these types of analyses because of the potential for confounding variables (e.g., socioeconomic status) to bias the observed relationships between distant inbreeding and complex traits. In this investigation, we used single-nucleotide polymorphism data in a very large (N > 400,000) sample of seemingly outbred individuals to quantify the degree to which distant inbreeding is associated with 26 complex traits. We found robust evidence that distant inbreeding is inversely associated with fluid intelligence and a measure of lung function, and is positively associated with age at first sex, while other trait associations with inbreeding were attenuated after controlling for background sociodemographic characteristics. Our findings are consistent with evolutionary predictions that fluid intelligence, lung function, and age at first sex have been under selection pressures over time; however, they also suggest that confounding variables must be accounted for in order to reliably interpret results from these types of analyses.
]]></description>
<dc:creator>Johnson, E. C.</dc:creator>
<dc:creator>Evans, L. M.</dc:creator>
<dc:creator>Keller, M. C.</dc:creator>
<dc:date>2018-03-29</dc:date>
<dc:identifier>doi:10.1101/291872</dc:identifier>
<dc:title><![CDATA[Relationships between estimated autozygosity and complex traits in the UK Biobank]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-03-29</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/281436v1?rss=1">
<title>
<![CDATA[
Body mass index and mortality in UK Biobank: revised estimates using Mendelian randomization 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/281436v1?rss=1"
</link>
<description><![CDATA[
ObjectiveObtain estimates of the causal relationship between different levels of body mass index (BMI) and mortality.nnMethodsMendelian randomization (MR) was conducted using genotypic variation reliably associated with BMI to test the causal effect of increasing BMI on all-cause and cause-specific mortality in participants of White British ancestry in UK Biobank.nnResultsMR analyses supported existing evidence for a causal association between higher levels of BMI and greater risk of all-cause mortality (hazard ratio (HR) per 1kg/m2: 1.02; 95% CI: 0.97,1.06) and mortality from cardiovascular diseases (HR: 1.12; 95% CI: 1.02, 1.23), specifically coronary heart disease (HR: 1.19; 95% CI: 1.05, 1.35) and those other than stroke/aortic aneurysm (HR: 1.13; 95% CI: 0.93, 1.38), stomach cancer (HR: 1.30; 95% CI: 0.91, 1.86) and oesophageal cancer (HR: 1.08; 95% CI: 0.84, 1.38), and with decreased risk of lung cancer mortality (HR: 0.97; 95% CI: 0.84, 1.11). Sex-stratified analyses supported a causal role of higher BMI in increasing the risk of mortality from bladder cancer in males and other causes in females, but in decreasing the risk of respiratory disease mortality in males. The characteristic J-shaped observational association between BMI and mortality was visible with MR analyses but with a smaller value of BMI at which mortality risk was lowest and apparently flatter over a larger range of BMI.nnConclusionResults support a causal role of higher BMI in increasing the risk of all-cause mortality and mortality from other causes. However, studies with greater numbers of deaths are needed to confirm the current findings.
]]></description>
<dc:creator>Wade, K. H.</dc:creator>
<dc:creator>Carslake, D.</dc:creator>
<dc:creator>Sattar, N.</dc:creator>
<dc:creator>Davey Smith, G.</dc:creator>
<dc:creator>Timpson, N. J.</dc:creator>
<dc:date>2018-03-26</dc:date>
<dc:identifier>doi:10.1101/281436</dc:identifier>
<dc:title><![CDATA[Body mass index and mortality in UK Biobank: revised estimates using Mendelian randomization]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-03-26</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/287490v1?rss=1">
<title>
<![CDATA[
A Polygenic Score for Higher Educational Attainment is Associated with Larger Brains 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/287490v1?rss=1"
</link>
<description><![CDATA[
People who score higher on intelligence tests tend to have larger brains. Twin studies suggest the same genetic factors influence both brain size and intelligence. This has led to the hypothesis that genetics influence intelligence partly by contributing to development of larger brains. We tested this hypothesis with molecular genetic data using discoveries from a genome-wide association study (GWAS) of educational attainment, a correlate of intelligence. We analyzed genetic, brain imaging, and cognitive test data from the UK Biobank, the Dunedin Study, the Brain Genomics Superstruct Project (GSP), and the Duke Neurogenetics Study (DNS) (combined N=8,271). We measured genetics using polygenic scores based on published GWAS. We conducted meta-analysis to test associations among participants genetics, total brain volume (i.e., brain size), and cognitive test performance. Consistent with previous findings, participants with higher polygenic scores achieved higher scores on cognitive tests, as did participants with larger brains. Participants with higher polygenic scores also had larger brains. We found some evidence that brain size partly mediated associations between participants education polygenic scores and their cognitive test performance. Effect-sizes were larger in the population-based UK Biobank and Dunedin samples than in the GSP and DNS samples. Sensitivity analysis suggested this effect-size difference partly reflected restricted range of cognitive performance in the GSP and DNS samples. Recruitment and retention of population-representative samples should be a priority for neuroscience research. Findings suggest promise for studies integrating GWAS discoveries with brain imaging data to understand neurobiology linking genetics with individual differences in cognitive performance.
]]></description>
<dc:creator>Elliott, M. L.</dc:creator>
<dc:creator>Belsky, D. W.</dc:creator>
<dc:creator>Anderson, K.</dc:creator>
<dc:creator>Corcoran, D. L.</dc:creator>
<dc:creator>Ge, T.</dc:creator>
<dc:creator>Knodt, A.</dc:creator>
<dc:creator>Prinz, J. A.</dc:creator>
<dc:creator>Sugden, K.</dc:creator>
<dc:creator>Williams, B.</dc:creator>
<dc:creator>Ireland, D.</dc:creator>
<dc:creator>Poulton, R.</dc:creator>
<dc:creator>Caspi, A.</dc:creator>
<dc:creator>Holmes, A.</dc:creator>
<dc:creator>Moffitt, T.</dc:creator>
<dc:creator>Hariri, A. R.</dc:creator>
<dc:date>2018-03-23</dc:date>
<dc:identifier>doi:10.1101/287490</dc:identifier>
<dc:title><![CDATA[A Polygenic Score for Higher Educational Attainment is Associated with Larger Brains]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-03-23</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/274654v1?rss=1">
<title>
<![CDATA[
Meta-analysis of genome-wide association studies for height and body mass index in ~700,000 individuals of European ancestry 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/274654v1?rss=1"
</link>
<description><![CDATA[
Genome-wide association studies (GWAS) stand as powerful experimental designs for identifying DNA variants associated with complex traits and diseases. In the past decade, both the number of such studies and their sample sizes have increased dramatically. Recent GWAS of height and body mass index (BMI) in [~]250,000 European participants have led to the discovery of [~]700 and [~]100 nearly independent SNPs associated with these traits, respectively. Here we combine summary statistics from those two studies with GWAS of height and BMI performed in [~]450,000 UK Biobank participants of European ancestry. Overall, our combined GWAS meta-analysis reaches N[~]700,000 individuals and substantially increases the number of GWAS signals associated with these traits. We identified 3,290 and 716 near-independent SNPs associated with height and BMI, respectively (at a revised genome-wide significance threshold of p<1 x 10-8), including 1,185 height-associated SNPs and 554 BMI-associated SNPs located within loci not previously identified by these two GWAS. The genome-wide significant SNPs explain [~]24.6% of the variance of height and [~]5% of the variance of BMI in an independent sample from the Health and Retirement Study (HRS). Correlations between polygenic scores based upon these SNPs with actual height and BMI in HRS participants were 0.44 and 0.20, respectively. From analyses of integrating GWAS and eQTL data by Summary-data based Mendelian Randomization (SMR), we identified an enrichment of eQTLs amongst lead height and BMI signals, prioritisting 684 and 134 genes, respectively. Our study demonstrates that, as previously predicted, increasing GWAS sample sizes continues to deliver, by discovery of new loci, increasing prediction accuracy and providing additional data to achieve deeper insight into complex trait biology. All summary statistics are made available for follow up studies.
]]></description>
<dc:creator>Yengo, L.</dc:creator>
<dc:creator>Sidorenko, J.</dc:creator>
<dc:creator>Kemper, K. E.</dc:creator>
<dc:creator>Zheng, Z.</dc:creator>
<dc:creator>Wood, A. R.</dc:creator>
<dc:creator>Weedon, M. N.</dc:creator>
<dc:creator>Frayling, T. M.</dc:creator>
<dc:creator>Hirschhorn, J.</dc:creator>
<dc:creator>Yang, J.</dc:creator>
<dc:creator>Visscher, P. M.</dc:creator>
<dc:creator>GIANT Consortium,</dc:creator>
<dc:date>2018-03-02</dc:date>
<dc:identifier>doi:10.1101/274654</dc:identifier>
<dc:title><![CDATA[Meta-analysis of genome-wide association studies for height and body mass index in ~700,000 individuals of European ancestry]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-03-02</prism:publicationDate>
<prism:section></prism:section>
</item>
</rdf:RDF>
