Reproducible Risk Loci and Psychiatric Comorbidities in Anxiety: Results from ~200,000 Million Veteran Program Participants

AbstractWe used GWAS in the Million Veteran Program sample (nearly 200,000 informative individuals) using a continuous trait for anxiety (GAD-2) to identify 5 genome-wide significant (GWS) signals for European Americans (EA) and 1 for African Americans. The strongest findings were on chromosome 3 (rs4603973, p=7.40×10−11) near the SATB1 locus, a global regulator of gene expression and on chromosome 6 (rs6557168, p=1.04×10−9) near ESR1 which encodes estrogen receptor α. A locus identified on chromosome 7 near MADIL1 (p=1.62×10−8) has been previously identified in GWAS of bipolar disorder and of schizophrenia and may represent a risk factor for psychiatric disorders broadly. SNP-based heritability was estimated to be ~6% for GAD-2. We also GWASed for self-reported anxiety disorder diagnoses (N=224,330) and identified two GWS loci, one (rs35546597, MAF=0.42, p=1.88×10−8) near the AURKB locus, and the other (rsl0534613, MAF=0.41, p=4.92×10−8) near the IQCHE and MADIL1 locus identified in the GAD-2 analysis. We demonstrate reproducibility by replicating our top findings in the summary statistics from the Anxiety NeuroGenetics Study (ANGST) and a UK Biobank neuroticism GWAS. We also replicated top findings from a large UK Biobank preprint, demonstrating stability of GWAS findings in complex traits once sufficient power is attained. Finally, we found evidence of significant genetic overlap between anxiety and major depression using polygenic risk scores, but also found that the main anxiety signals are independent of those for MDD. This work presents novel insights into the neurobiological risk underpinning anxiety and related psychiatric disorders.SignificanceAnxiety disorders are common and often disabling. They are also frequently co-morbid with other mental disorders such as major depressive disorder (MDD); these disorders may share commonalities in their underlying genetic architecture. Using one of the largest homogenously phenotyped cohorts available, the Million Veteran Program sample, we investigated common variants associated with anxiety in genome-wide association studies (GWASes), using survey results from the GAD-2 anxiety scale (as a continuous trait, n=199,611), and self-reported anxiety disorder diagnosis (as a binary trait, n=224,330). This largest GWAS to date for anxiety and related traits identified numerous novel significant associations, several of which are replicated in other datasets, and allows inference of underlying biology.

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GWAS of three molecular traits highlights core genes and pathways alongside a highly polygenic background

10.1101/2020.04.20.051631 ◽

2020 ◽

Cited By ~ 6

Author(s):

Nasa Sinnott-Armstrong ◽

Sahin Naqvi ◽

Manuel Rivas ◽

Jonathan K Pritchard

Keyword(s):

Complex Traits ◽

Genetic Basis ◽

Association Studies ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Biological Processes ◽

Uk Biobank ◽

The Core ◽

Genome Wide ◽

Core Genes

SummaryGenome-wide association studies (GWAS) have been used to study the genetic basis of a wide variety of complex diseases and other traits. However, for most traits it remains difficult to interpret what genes and biological processes are impacted by the top hits. Here, as a contrast, we describe UK Biobank GWAS results for three molecular traits—urate, IGF-1, and testosterone—that are biologically simpler than most diseases, and for which we know a great deal in advance about the core genes and pathways. Unlike most GWAS of complex traits, for all three traits we find that most top hits are readily interpretable. We observe huge enrichment of significant signals near genes involved in the relevant biosynthesis, transport, or signaling pathways. We show how GWAS data illuminate the biology of variation in each trait, including insights into differences in testosterone regulation between females and males. Meanwhile, in other respects the results are reminiscent of GWAS for more-complex traits. In particular, even these molecular traits are highly polygenic, with most of the variance coming not from core genes, but from thousands to tens of thousands of variants spread across most of the genome. Given that diseases are often impacted by many distinct biological processes, including these three, our results help to illustrate why so many variants can affect risk for any given disease.

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GWAS contribution to atrial fibrillation and atrial fibrillation-related stroke: pathophysiological implications

Pharmacogenomics ◽

10.2217/pgs-2019-0054 ◽

2019 ◽

Vol 20 (10) ◽

pp. 765-780 ◽

Cited By ~ 1

Author(s):

Diana Cruz ◽

Ricardo Pinto ◽

Margarida Freitas-Silva ◽

José Pedro Nunes ◽

Rui Medeiros

Keyword(s):

Atrial Fibrillation ◽

Genetic Variants ◽

Complex Traits ◽

Association Studies ◽

Cardioembolic Stroke ◽

Risk Scores ◽

Genome Wide Association Studies ◽

Genetic Determinants ◽

Genome Wide ◽

Genome Wide Significance

Atrial fibrillation (AF) and stroke are included in a group of complex traits that have been approached regarding of their study by susceptibility genetic determinants. Since 2007, several genome-wide association studies (GWAS) aiming to identify genetic variants modulating AF risk have been conducted. Thus, 11 GWAS have identified 26 SNPs (p < 5 × 10-2), of which 19 reached genome-wide significance (p < 5 × 10-8). From those variants, seven were also associated with cardioembolic stroke and three reached genome-wide significance in stroke GWAS. These associations may shed a light on putative shared etiologic mechanisms between AF and cardioembolic stroke. Additionally, some of these identified variants have been incorporated in genetic risk scores in order to elucidate new approaches of stroke prediction, prevention and treatment.

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Polygenic transcriptome risk scores (PTRS) can improve portability of polygenic risk scores across ancestries

Genome Biology ◽

10.1186/s13059-021-02591-w ◽

2022 ◽

Vol 23 (1) ◽

Author(s):

Yanyu Liang ◽

Milton Pividori ◽

Ani Manichaikul ◽

Abraham A. Palmer ◽

Nancy J. Cox ◽

...

Keyword(s):

Association Studies ◽

Poor Performance ◽

Genome Wide Association ◽

European Ancestry ◽

Risk Scores ◽

Genome Wide Association Studies ◽

Uk Biobank ◽

Transcript Levels ◽

Polygenic Risk ◽

Genome Wide

Abstract Background Polygenic risk scores (PRS) are valuable to translate the results of genome-wide association studies (GWAS) into clinical practice. To date, most GWAS have been based on individuals of European-ancestry leading to poor performance in populations of non-European ancestry. Results We introduce the polygenic transcriptome risk score (PTRS), which is based on predicted transcript levels (rather than SNPs), and explore the portability of PTRS across populations using UK Biobank data. Conclusions We show that PTRS has a significantly higher portability (Wilcoxon p=0.013) in the African-descent samples where the loss of performance is most acute with better performance than PRS when used in combination.

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An atlas of genetic associations in UK Biobank

10.1101/176834 ◽

2017 ◽

Cited By ~ 18

Author(s):

Oriol Canela-Xandri ◽

Konrad Rawlik ◽

Albert Tenesa

Keyword(s):

Complex Traits ◽

Statistical Power ◽

Association Studies ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Uk Biobank ◽

Genetic Associations ◽

Genome Wide ◽

Related Individuals ◽

Sufficient Statistical Power

ABSTRACTGenome-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.

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Cancer PRSweb – an Online Repository with Polygenic Risk Scores (PRS) for Major Cancer Traits and Their Phenome-wide Exploration in Two Independent Biobanks

10.1101/2020.01.22.915751 ◽

2020 ◽

Cited By ~ 1

Author(s):

Lars G. Fritsche ◽

Snehal Patil ◽

Lauren J. Beesley ◽

Peter VandeHaar ◽

Maxwell Salvatore ◽

...

Keyword(s):

Association Studies ◽

Predictive Performance ◽

Risk Scores ◽

P Value ◽

Genome Wide Association Studies ◽

Summary Statistics ◽

Uk Biobank ◽

Polygenic Risk ◽

Genome Wide ◽

Study Results

AbstractTo facilitate scientific collaboration on polygenic risk scores (PRS) research, we created an extensive PRS online repository for 49 common cancer traits integrating freely available genome-wide association studies (GWAS) summary statistics from three sources: published GWAS, the NHGRI-EBI GWAS Catalog, and UK Biobank-based GWAS. Our framework condenses these summary statistics into PRS using various approaches such as linkage disequilibrium pruning / p-value thresholding (fixed or data-adaptively optimized thresholds) and penalized, genome-wide effect size weighting. We evaluated the PRS in two biobanks: the Michigan Genomics Initiative (MGI), a longitudinal biorepository effort at Michigan Medicine, and the population-based UK Biobank (UKB). For each PRS construct, we provide measures on predictive performance, calibration, and discrimination. Besides PRS evaluation, the Cancer-PRSweb platform features construct downloads and phenome-wide PRS association study results (PRS-PheWAS) for predictive PRS. We expect this integrated platform to accelerate PRS-related cancer research.

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Psychophysiological endophenotypes to characterize mechanisms of known schizophrenia genetic loci

Psychological Medicine ◽

10.1017/s0033291716003184 ◽

2016 ◽

Vol 47 (6) ◽

pp. 1116-1125 ◽

Cited By ~ 16

Author(s):

M. Liu ◽

S. M. Malone ◽

U. Vaidyanathan ◽

M. C. Keller ◽

G. Abecasis ◽

...

Keyword(s):

Complex Traits ◽

Large Scale ◽

Electrodermal Activity ◽

Association Studies ◽

Causal Chain ◽

Event Related Potential ◽

Risk Scores ◽

Genome Wide Association Studies ◽

Genome Wide ◽

Disorder Risk

BackgroundEndophenotypes are laboratory-based measures hypothesized to lie in the causal chain between genes and clinical disorder, and to serve as a more powerful way to identify genes associated with the disorder. One promise of endophenotypes is that they may assist in elucidating the neurobehavioral mechanisms by which an associated genetic polymorphism affects disorder risk in complex traits. We evaluated this promise by testing the extent to which variants discovered to be associated with schizophrenia through large-scale meta-analysis show associations with psychophysiological endophenotypes.MethodWe genome-wide genotyped and imputed 4905 individuals. Of these, 1837 were whole-genome-sequenced at 11× depth. In a community-based sample, we conducted targeted tests of variants within schizophrenia-associated loci, as well as genome-wide polygenic tests of association, with 17 psychophysiological endophenotypes including acoustic startle response and affective startle modulation, antisaccade, multiple frequencies of resting electroencephalogram (EEG), electrodermal activity and P300 event-related potential.ResultsUsing single variant tests and gene-based tests we found suggestive evidence for an association between contactin 4 (CNTN4) and antisaccade and P300. We were unable to find any other variant or gene within the 108 schizophrenia loci significantly associated with any of our 17 endophenotypes. Polygenic risk scores indexing genetic vulnerability to schizophrenia were not related to any of the psychophysiological endophenotypes after correction for multiple testing.ConclusionsThe results indicate significant difficulty in using psychophysiological endophenotypes to characterize the genetically influenced neurobehavioral mechanisms by which risk loci identified in genome-wide association studies affect disorder risk.

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Genome-wide by environment interaction studies (GWEIS) of depressive symptoms and psychosocial stress in UK Biobank and Generation Scotland

10.1101/479691 ◽

2018 ◽

Cited By ~ 1

Author(s):

Aleix Arnau-Soler ◽

Erin Macdonald-Dunlop ◽

Mark J. Adams ◽

Toni-Kim Clarke ◽

Donald J. MacIntyre ◽

...

Keyword(s):

Depressive Symptoms ◽

Association Studies ◽

Joint Effect ◽

Risk Scores ◽

Environment Interaction ◽

Genome Wide Association Studies ◽

Uk Biobank ◽

Interaction Studies ◽

Genome Wide ◽

Generation Scotland

ABSTRACTStress is associated with poorer physical and mental health. To improve our understanding of this link, we performed genome-wide association studies (GWAS) of depressive symptoms and genome-wide by environment interaction studies (GWEIS) of depressive symptoms and stressful life events (SLE) in two UK population cohorts (Generation Scotland and UK Biobank). No SNP was individually significant in either GWAS, but gene-based tests identified six genes associated with depressive symptoms in UK Biobank (DCC, ACSS3, DRD2, STAG1, FOXP2 and KYNU; p < 2.77×10-6). Two SNPs with genome-wide significant GxE effects were identified by GWEIS in Generation Scotland: rs12789145 (53kb downstream PIWIL4; p = 4.95×10-9; total SLE) and rs17070072 (intronic to ZCCHC2; p = 1.46×10-8; dependent SLE). A third locus upstream CYLC2 (rs12000047 and rs12005200, p < 2.00×10-8; dependent SLE) when the joint effect of the SNP main and GxE effects was considered. GWEIS gene-based tests identified: MTNR1B with GxE effect with dependent SLE in Generation Scotland; and PHF2 with the joint effect in UK Biobank (p < 2.77×10-6). Polygenic risk scores (PRS) analyses incorporating GxE effects improved the prediction of depressive symptom scores, when using weights derived from either the UK Biobank GWAS of depressive symptoms (p = 0.01) or the PGC GWAS of major depressive disorder (p = 5.91×10-3). Using an independent sample, PRS derived using GWEIS GxE effects provided evidence of shared aetiologies between depressive symptoms and schizotypal personality, heart disease and COPD. Further such studies are required and may result in improved treatments for depression and other stress-related conditions.

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Embracing polygenicity: a review of methods and tools for psychiatric genetics research

Psychological Medicine ◽

10.1017/s0033291717002318 ◽

2017 ◽

Vol 48 (7) ◽

pp. 1055-1067 ◽

Cited By ~ 33

Author(s):

R. M. Maier ◽

P. M. Visscher ◽

M. R. Robinson ◽

N. R. Wray

Keyword(s):

Psychiatric Disorders ◽

Genetic Risk ◽

Complex Traits ◽

Association Studies ◽

Genome Wide Association Studies ◽

Nucleotide Polymorphisms ◽

Genetics Research ◽

Genome Wide ◽

Analyse Data ◽

Shared Genetic

AbstractThe availability of genome-wide genetic data on hundreds of thousands of people has led to an equally rapid growth in methodologies available to analyse these data. While the motivation for undertaking genome-wide association studies (GWAS) is identification of genetic markers associated with complex traits, once generated these data can be used for many other analyses. GWAS have demonstrated that complex traits exhibit a highly polygenic genetic architecture, often with shared genetic risk factors across traits. New methods to analyse data from GWAS are increasingly being used to address a diverse set of questions about the aetiology of complex traits and diseases, including psychiatric disorders. Here, we give an overview of some of these methods and present examples of how they have contributed to our understanding of psychiatric disorders. We consider: (i) estimation of the extent of genetic influence on traits, (ii) uncovering of shared genetic control between traits, (iii) predictions of genetic risk for individuals, (iv) uncovering of causal relationships between traits, (v) identifying causal single-nucleotide polymorphisms and genes or (vi) the detection of genetic heterogeneity. This classification helps organise the large number of recently developed methods, although some could be placed in more than one category. While some methods require GWAS data on individual people, others simply use GWAS summary statistics data, allowing novel well-powered analyses to be conducted at a low computational burden.

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A comparative study of data integration methods, integrating genetic association and functional annotation summary statistics

10.1101/2020.11.25.396721 ◽

2020 ◽

Author(s):

Jianhui Gao ◽

Lei Sun

Keyword(s):

Data Integration ◽

Sample Size ◽

Complex Traits ◽

Functional Annotation ◽

Association Studies ◽

Genome Wide Association Studies ◽

Summary Statistics ◽

Uk Biobank ◽

Integration Methods ◽

Genome Wide

AbstractPower of many genome-wide association studies (GWAS) remains low despite of increasing sample size, because the genetic effects for complex traits are small, the case sample size may not be large, and the variants analyzed may be rare. One direction is to integrate available functional annotation meta-score such as CADD and Eigen to increase power of a GWAS. Here we examine four data-integration methods, including meta-analysis, Fisher’s method, weighted p-value, and stratified FDR control, all based on summary statistics only. We focus on robustness study, considering settings where the functional meta-score mayor may not be informative, or possibly be misleading. In addition to extensive simulation studies, we also apply the four methods to 945 binary outcomes in the UK Biobank data, including all 633 traits with ICD-10 codes, 28 self-reported cancers and 284 self-reported non-cancer diseases, integrating publicly available GWAS summary statistics (http://www.nealelab.is/uk-biobank/) with CADD or Eigen scores. While the trade-off between power and robustness observation is expected, our application shows some but limited utility of current functional meta-score in terms of leading to new genome-wide significant association findings.

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GWAS of three molecular traits highlights core genes and pathways alongside a highly polygenic background

eLife ◽

10.7554/elife.58615 ◽

2021 ◽

Vol 10 ◽

Author(s):

Nasa Sinnott-Armstrong ◽

Sahin Naqvi ◽

Manuel Rivas ◽

Jonathan K Pritchard

Keyword(s):

Complex Traits ◽

Genetic Basis ◽

Association Studies ◽

Core Gene ◽

Genome Wide Association Studies ◽

Uk Biobank ◽

Gene Sets ◽

Genome Wide ◽

Trait Variance ◽

Core Genes

Genome-wide association studies (GWAS) have been used to study the genetic basis of a wide variety of complex diseases and other traits. We describe UK Biobank GWAS results for three molecular traits—urate, IGF-1, and testosterone—with better-understood biology than most other complex traits. We find that many of the most significant hits are readily interpretable. We observe huge enrichment of associations near genes involved in the relevant biosynthesis, transport, or signaling pathways. We show how GWAS data illuminate the biology of each trait, including differences in testosterone regulation between females and males. At the same time, even these molecular traits are highly polygenic, with many thousands of variants spread across the genome contributing to trait variance. In summary, for these three molecular traits we identify strong enrichment of signal in putative core gene sets, even while most of the SNP-based heritability is driven by a massively polygenic background.

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