Sexual differences in genetic architecture in UK Biobank

ABSTRACTSex is arguably the most important differentiating characteristic in most mammalian species, separating populations into different groups, with varying behaviors, morphologies, and physiologies based on their complement of sex chromosomes. In humans, despite males and females sharing nearly identical genomes, there are differences between the sexes in complex traits and in the risk of a wide array of diseases. Gene by sex interactions (GxS) are thought to account for some of this sexual dimorphism. However, the extent and basis of these interactions are poorly understood.Here we provide insights into both the scope and mechanism of GxS across the genome of circa 450,000 individuals of European ancestry and 530 complex traits in the UK Biobank. We found small yet widespread differences in genetic architecture across traits through the calculation of sex-specific heritability, genetic correlations, and sex-stratified genome-wide association studies (GWAS). We also found that, in some cases, sex-agnostic GWAS efforts might be missing loci of interest, and looked into possible improvements in the prediction of high-level phenotypes. Finally, we studied the potential functional role of the dimorphism observed through sex-biased eQTL and gene-level analyses.This study marks a broad examination of the genetics of sexual dimorphism. Our findings parallel previous reports, suggesting the presence of sexual genetic heterogeneity across complex traits of generally modest magnitude. Our results suggest the need to consider sex-stratified analyses for future studies in order to shed light into possible sex-specific molecular mechanisms.

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Shared Genetic Architecture between Asthma and Allergic Diseases: A Genome-Wide Cross Trait Analysis of 112,000 Individuals from UK Biobank

10.1101/133322 ◽

2017 ◽

Cited By ~ 3

Author(s):

Zhaozhong Zhu ◽

Phil H. Lee ◽

Mark D. Chaffin ◽

Wonil Chung ◽

Po-Ru Loh ◽

...

Keyword(s):

Molecular Mechanisms ◽

Association Studies ◽

Allergic Diseases ◽

European Ancestry ◽

Genome Wide Association Studies ◽

Uk Biobank ◽

Trait Analysis ◽

Genome Wide ◽

A Genome ◽

Shared Genetic

AbstractClinical 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 = 6.84×10−62). 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.

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Investigating the genetic architecture of eye colour in a Canadian cohort

10.1101/2021.09.29.462299 ◽

2021 ◽

Author(s):

Frida Lona-Durazo ◽

Rohit Thakur ◽

Erola Pairo-Castineira ◽

Karen Funderburk ◽

Tongwu Zhang ◽

...

Keyword(s):

Genetic Architecture ◽

Association Studies ◽

Meta Analysis ◽

Genetic Correlations ◽

European Ancestry ◽

Chromosome 15 ◽

Genome Wide Association Studies ◽

Eye Colour ◽

Causal Variants ◽

Genome Level

The main factors that determine eye colour are the amount of melanin concentrated in iris melanocytes, as well as the shape and distribution of melanosomes. Eye colour is highly variable in populations with European ancestry, in which eye colour categories cover a continuum of low to high quantities of melanin accumulated in the iris. A few polymorphisms in the HERC2/OCA2 locus in chromosome 15 have the largest effect on eye colour in these populations, although there is evidence of other variants in the locus and across the genome also influencing eye colour. To improve our understanding of the genetic loci determining eye colour, we performed a meta-analysis of genome-wide association studies in a Canadian cohort of European ancestry (N= 5,641) and investigated putative causal variants. Our fine-mapping results indicate that there are several candidate causal signals in the HERC2/OCA2 region, whereas other significant loci in the genome likely harbour a single causal signal (TYR, TYRP1, IRF4, SLC24A4). Furthermore, a short subset of the associated eye colour regions was colocalized with the gene expression or methylation profiles of cultured melanocytes (HERC2, OCA2), and transcriptome-wide association studies highlighted the expression of two genes associated with eye colour: SLC24A4 and OCA2. Finally, genetic correlations of eye and hair colour from the same cohort suggest high pleiotropy at the genome level, but locus-level evidence hints at several differences in the genetic architecture of both traits. Overall, we provide a better picture of how polymorphisms modulate eye colour variation, particularly in the HERC2/OCA2 locus, which may be a consequence of specific molecular processes in the iris melanocytes.

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Analysis across Taiwan Biobank, Biobank Japan and UK Biobank identifies hundreds of novel loci for 36 quantitative traits

10.1101/2021.04.12.21255236 ◽

2021 ◽

Author(s):

Chia-Yen Chen ◽

Tzu-Ting Chen ◽

Yen-Chen Anne Feng ◽

Ryan J. Longchamps ◽

Shu-Chin Lin ◽

...

Keyword(s):

Fine Mapping ◽

Complex Traits ◽

Quantitative Traits ◽

Disease Risk ◽

Association Studies ◽

Genetic Research ◽

European Ancestry ◽

Genome Wide Association Studies ◽

Uk Biobank ◽

Genetic Loci

AbstractGenome-wide association studies (GWAS) have identified tens of thousands of genetic loci associated with human complex traits and diseases1,2. However, the majority of GWAS were conducted in individuals of European ancestry3. Failure to capture global genetic diversity has limited biological discovery and impeded equitable delivery of genomic knowledge to diverse populations4. Here we report findings from 102,900 individuals across 36 human quantitative traits in the Taiwan Biobank (TWB), a major biobank effort that broadens the population diversity of genetic studies in East Asia (EAS). We identified 979 novel genetic loci, pinpointed novel causal variants through fine-mapping, compared the genetic architecture across TWB, Biobank Japan (BBJ)5–7 and UK Biobank (UKBB)8,9, and demonstrated the utility of cross-phenotype, cross-population polygenic risk scores (PRS) in disease risk prediction. We release all GWAS summary statistics, fine-mapping results, and single nucleotide polymorphism (SNP) weights and TWB-based PRS reference distributions for polygenic prediction (link to appear upon publication) to facilitate within-EAS and cross-population genetic research.

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Genetics of complex traits: prediction of phenotype, identification of causal polymorphisms and genetic architecture

Proceedings of The Royal Society B Biological Sciences ◽

10.1098/rspb.2016.0569 ◽

2016 ◽

Vol 283 (1835) ◽

pp. 20160569 ◽

Cited By ~ 52

Author(s):

M. E. Goddard ◽

K. E. Kemper ◽

I. M. MacLeod ◽

A. J. Chamberlain ◽

B. J. Hayes

Keyword(s):

Complex Traits ◽

Genetic Architecture ◽

Quantitative Traits ◽

Association Studies ◽

Genome Wide Association Studies ◽

Nucleotide Polymorphisms ◽

Crop Breeding ◽

Single Nucleotide ◽

Genome Wide ◽

Phenotype Identification

Complex or quantitative traits are important in medicine, agriculture and evolution, yet, until recently, few of the polymorphisms that cause variation in these traits were known. Genome-wide association studies (GWAS), based on the ability to assay thousands of single nucleotide polymorphisms (SNPs), have revolutionized our understanding of the genetics of complex traits. We advocate the analysis of GWAS data by a statistical method that fits all SNP effects simultaneously, assuming that these effects are drawn from a prior distribution. We illustrate how this method can be used to predict future phenotypes, to map and identify the causal mutations, and to study the genetic architecture of complex traits. The genetic architecture of complex traits is even more complex than previously thought: in almost every trait studied there are thousands of polymorphisms that explain genetic variation. Methods of predicting future phenotypes, collectively known as genomic selection or genomic prediction, have been widely adopted in livestock and crop breeding, leading to increased rates of genetic improvement.

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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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Better estimation of SNP heritability from summary statistics provides a new understanding of the genetic architecture of complex traits

10.1101/284976 ◽

2018 ◽

Cited By ~ 6

Author(s):

Doug Speed ◽

David J Balding

Keyword(s):

Complex Traits ◽

Genetic Architecture ◽

Large Scale ◽

Association Studies ◽

Genome Wide Association Studies ◽

Summary Statistics ◽

Confounding Bias ◽

Conserved Regions ◽

Genome Wide ◽

Variation Explained

LD Score Regression (LDSC) has been widely applied to the results of genome-wide association studies. However, its estimates of SNP heritability are derived from an unrealistic model in which each SNP is expected to contribute equal heritability. As a consequence, LDSC tends to over-estimate confounding bias, under-estimate the total phenotypic variation explained by SNPs, and provide misleading estimates of the heritability enrichment of SNP categories. Therefore, we present SumHer, software for estimating SNP heritability from summary statistics using more realistic heritability models. After demonstrating its superiority over LDSC, we apply SumHer to the results of 24 large-scale association studies (average sample size 121 000). First we show that these studies have tended to substantially over-correct for confounding, and as a result the number of genome-wide significant loci has under-reported by about 20%. Next we estimate enrichment for 24 categories of SNPs defined by functional annotations. A previous study using LDSC reported that conserved regions were 13-fold enriched, and found a further twelve categories with above 2-fold enrichment. By contrast, our analysis using SumHer finds that conserved regions are only 1.6-fold (SD 0.06) enriched, and that no category has enrichment above 1.7-fold. SumHer provides an improved understanding of the genetic architecture of complex traits, which enables more efficient analysis of future genetic data.

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Insights into the genetic basis of retinal detachment

Human Molecular Genetics ◽

10.1093/hmg/ddz294 ◽

2019 ◽

Vol 29 (4) ◽

pp. 689-702 ◽

Cited By ~ 2

Author(s):

Thibaud S Boutin ◽

David G Charteris ◽

Aman Chandra ◽

Susan Campbell ◽

Caroline Hayward ◽

...

Keyword(s):

Retinal Detachment ◽

Association Studies ◽

Genetic Correlations ◽

Self Report ◽

Cataract Operation ◽

Genome Wide Association Studies ◽

Uk Biobank ◽

Genetic Associations ◽

Data Set ◽

Genome Wide

Abstract Retinal detachment (RD) is a serious and common condition, but genetic studies to date have been hampered by the small size of the assembled cohorts. In the UK Biobank data set, where RD was ascertained by self-report or hospital records, genetic correlations between RD and high myopia or cataract operation were, respectively, 0.46 (SE = 0.08) and 0.44 (SE = 0.07). These correlations are consistent with known epidemiological associations. Through meta-analysis of genome-wide association studies using UK Biobank RD cases (N = 3 977) and two cohorts, each comprising ~1 000 clinically ascertained rhegmatogenous RD patients, we uncovered 11 genome-wide significant association signals. These are near or within ZC3H11B, BMP3, COL22A1, DLG5, PLCE1, EFEMP2, TYR, FAT3, TRIM29, COL2A1 and LOXL1. Replication in the 23andMe data set, where RD is self-reported by participants, firmly establishes six RD risk loci: FAT3, COL22A1, TYR, BMP3, ZC3H11B and PLCE1. Based on the genetic associations with eye traits described to date, the first two specifically impact risk of a RD, whereas the last four point to shared aetiologies with macular condition, myopia and glaucoma. Fine-mapping prioritized the lead common missense variant (TYR S192Y) as causal variant at the TYR locus and a small set of credible causal variants at the FAT3 locus. The larger study size presented here, enabled by resources linked to health records or self-report, provides novel insights into RD aetiology and underlying pathological pathways.

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GPA-MDS: A Visualization Approach to Investigate Genetic Architecture among Phenotypes Using GWAS Results

International Journal of Genomics ◽

10.1155/2016/6589843 ◽

2016 ◽

Vol 2016 ◽

pp. 1-6 ◽

Cited By ~ 2

Author(s):

Wei Wei ◽

Paula S. Ramos ◽

Kelly J. Hunt ◽

Bethany J. Wolf ◽

Gary Hardiman ◽

...

Keyword(s):

Complex Traits ◽

Genetic Architecture ◽

Association Studies ◽

Genetic Relationships ◽

Joint Analysis ◽

Genome Wide Association Studies ◽

Risk Variants ◽

Novel Approach ◽

Medical Benefits ◽

Rigorous Framework

Genome-wide association studies (GWAS) have identified tens of thousands of genetic variants associated with hundreds of phenotypes and diseases, which have provided clinical and medical benefits to patients with novel biomarkers and therapeutic targets. Recently, there has been accumulating evidence suggesting that different complex traits share a common risk basis, namely, pleiotropy. Previously, a statistical method, namely, GPA (Genetic analysis incorporating Pleiotropy and Annotation), was developed to improve identification of risk variants and to investigate pleiotropic structure through a joint analysis of multiple GWAS datasets. While GPA provides a statistically rigorous framework to evaluate pleiotropy between phenotypes, it is still not trivial to investigate genetic relationships among a large number of phenotypes using the GPA framework. In order to address this challenge, in this paper, we propose a novel approach, GPA-MDS, to visualize genetic relationships among phenotypes using the GPA algorithm and multidimensional scaling (MDS). This tool will help researchers to investigate common etiology among diseases, which can potentially lead to development of common treatments across diseases. We evaluate the proposed GPA-MDS framework using a simulation study and apply it to jointly analyze GWAS datasets examining 18 unique phenotypes, which helps reveal the shared genetic architecture of these phenotypes.

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A global overview of pleiotropy and genetic architecture in complex traits

10.1101/500090 ◽

2018 ◽

Cited By ~ 19

Author(s):

Kyoko Watanabe ◽

Sven Stringer ◽

Oleksandr Frei ◽

Maša Umićević Mirkov ◽

Tinca J.C. Polderman ◽

...

Keyword(s):

Genetic Variants ◽

Complex Traits ◽

Genetic Architecture ◽

Human Genetics ◽

Association Studies ◽

Regulatory Elements ◽

Genome Wide Association Studies ◽

Multiple Trait ◽

Current Availability ◽

Flanking Regions

ABSTRACTAfter a decade of genome-wide association studies (GWASs), fundamental questions in human genetics are still unanswered, such as the extent of pleiotropy across the genome, the nature of trait-associated genetic variants and the disparate genetic architecture across human traits. The current availability of hundreds of GWAS results provide the unique opportunity to gain insight into these questions. In this study, we harmonized and systematically analysed 4,155 publicly available GWASs. For a subset of well-powered GWAS on 558 unique traits, we provide an extensive overview of pleiotropy and genetic architecture. We show that trait associated loci cover more than half of the genome, and 90% of those loci are associated with multiple trait domains. We further show that potential causal genetic variants are enriched in coding and flanking regions, as well as in regulatory elements, and how trait-polygenicity is related to an estimate of the required sample size to detect 90% of causal genetic variants. Our results provide novel insights into how genetic variation contributes to trait variation. All GWAS results can be queried and visualized at the GWAS ATLAS resource (http://atlas.ctglab.nl).

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Sleep Deficits and Cannabis Use Behaviors: An Analysis of Shared Genetics Using Linkage Disequilibrium Score Regression and Polygenic Risk Prediction

10.1101/2020.05.02.053983 ◽

2020 ◽

Author(s):

Evan A. Winiger ◽

Jarrod M. Ellingson ◽

Claire L. Morrison ◽

Robin P. Corley ◽

Joëlle A. Pasman ◽

...

Keyword(s):

Linkage Disequilibrium ◽

Sleep Duration ◽

Association Studies ◽

Genetic Correlations ◽

Cannabis Use ◽

European Ancestry ◽

Risk Scores ◽

Genome Wide Association Studies ◽

Short Sleep Duration ◽

Polygenic Risk

AbstractStudy ObjectivesEstimate the genetic relationship of cannabis use with sleep deficits and eveningness chronotype.MethodsWe used linkage disequilibrium score regression (LDSC) to analyze genetic correlations between sleep deficits and cannabis use behaviors. Secondly, we generated sleep deficit polygenic risk scores (PRSs) and estimated their ability to predict cannabis use behaviors using logistic regression. Summary statistics came from existing genome wide association studies (GWASs) of European ancestry that were focused on sleep duration, insomnia, chronotype, lifetime cannabis use, and cannabis use disorder (CUD). A target sample for PRS prediction consisted of high-risk participants and participants from twin/family community-based studies (n = 796, male = 66%; mean age = 26.81). Target data consisted of self-reported sleep (sleep duration, feeling tired, and taking naps) and cannabis use behaviors (lifetime use, number of lifetime uses, past 180-day use, age of first use, and lifetime CUD symptoms).ResultsSignificant genetic correlation between lifetime cannabis use and eveningness chronotype (rG = 0.24, p < 0.01), as well as between CUD and both short sleep duration (<7 h) (rG = 0.23, p = 0.02) and insomnia (rG = 0.20, p = 0.02). Insomnia PRS predicted earlier age of first cannabis use (β = −0.09, p = 0.02) and increased lifetime CUD symptom count use (β = 0.07, p = 0.03).ConclusionCannabis use is genetically associated with both sleep deficits and an eveningness chronotype, suggesting that there are genes that predispose individuals to both cannabis use and sleep deficits.

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