Analysis of genetic dominance in the UK Biobank

In classical statistical genetic theory, a dominance effect is defined as the deviation from a purely additive genetic effect for a biallelic variant. Dominance effects are well documented in model organisms. However, evidence in humans is limited to a handful of traits, particularly those with strong single locus effects such as hair color. We carried out the largest systematic evaluation of dominance effects on phenotypic variance in the UK Biobank. We curated and tested over 1,000 phenotypes for dominance effects through GWAS scans, identifying 175 loci at genome-wide significance correcting for multiple testing (P < 4.7 × 10-11). Power to detect non-additive loci is much lower than power to detect additive effects for complex traits: based on the relative effect sizes at genome-wide significant additive loci, we estimate a factor of 20-30 increase in sample size will be necessary to capture clear evidence of dominance similar to those currently observed for additive effects. However, these localised dominance hits do not extend to a significant aggregate contribution to phenotypic variance genome-wide. By deriving a version of LD-score regression to detect dominance effects tagged by common variation genome-wide (minor allele frequency > 0.05), we found no strong evidence of a contribution to phenotypic variance when accounting for multiple testing. Across the 267 continuous and 793 binary traits the median contribution was 5.73 × 10-4, with unbiased point estimates ranging from -0.261 to 0.131. Finally, we introduce dominance fine-mapping to explore whether the more rapid decay of dominance LD can be leveraged to find causal variants. These results provide the most comprehensive assessment of dominance trait variation in humans to date.

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Accurate estimation of SNP-heritability from biobank-scale data irrespective of genetic architecture

10.1101/526855 ◽

2019 ◽

Cited By ~ 3

Author(s):

Kangcheng Hou ◽

Kathryn S. Burch ◽

Arunabha Majumdar ◽

Huwenbo Shi ◽

Nicholas Mancuso ◽

...

Keyword(s):

Complex Traits ◽

Genetic Architecture ◽

Accurate Estimation ◽

Phenotypic Variance ◽

Uk Biobank ◽

Genome Wide ◽

Wide Range ◽

Fundamental Quantity ◽

The Uk ◽

Scale Data

AbstractThe proportion of phenotypic variance attributable to the additive effects of a given set of genotyped SNPs (i.e. SNP-heritability) is a fundamental quantity in the study of complex traits. Recent works have shown that existing methods to estimate genome-wide SNP-heritability often yield biases when their assumptions are violated. While various approaches have been proposed to account for frequency- and LD-dependent genetic architectures, it remains unclear which estimates of SNP-heritability reported in the literature are reliable. Here we show that genome-wide SNP-heritability can be accurately estimated from biobank-scale data irrespective of the underlying genetic architecture of the trait, without specifying a heritability model or partitioning SNPs by minor allele frequency and/or LD. We use theoretical justifications coupled with extensive simulations starting from real genotypes from the UK Biobank (N=337K) to show that, unlike existing methods, our closed-form estimator for SNP-heritability is highly accurate across a wide range of architectures. We provide estimates of SNP-heritability for 22 complex traits and diseases in the UK Biobank and show that, consistent with our results in simulations, existing biobank-scale methods yield estimates up to 30% different from our theoretically-justified approach.

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HLA-B27 and not variation in MICA is responsible for genotype by sex interaction in risk of Ankylosing Spondylitis

10.1101/2021.12.16.21267808 ◽

2021 ◽

Author(s):

Zhixiu Li ◽

Allan F McRae ◽

Geng Wang ◽

Jonathan J Ellis ◽

Tony J Kenna ◽

...

Keyword(s):

Ankylosing Spondylitis ◽

Complex Traits ◽

Hla B27 ◽

Uk Biobank ◽

Association Analyses ◽

Genome Wide ◽

Conditional Association ◽

A Genome ◽

Mhc Locus ◽

The Uk

Ankylosing Spondylitis (AS) is a highly heritable inflammatory arthritis which occurs more frequently in men than women. In their recent publication examining sex differences in the genetic aetiology of common complex traits and diseases, Bernabeu et al. (2021) observe differences in heritability of AS between sexes, and a genome-wide significant genotype by sex interaction in risk of AS at the major histocompatability (MHC) locus. The authors then present evidence suggesting that this genotype by sex interaction arises primarily as a result of differential expression of the gene MICA across the sexes in skeletal muscle tissue. Through a series of conditional association analyses in the UK Biobank, reanalysis of the GTEx gene expression resource and RNASeq experiments on peripheral blood cells from AS cases and controls, we show that the genotype by sex interaction the authors' report is unlikely to be a result of variation in MICA, but probably reflects a known interaction between the HLA-B gene, sex and risk of AS. We demonstrate that the diagnostic accuracy of AS in the UK Biobank is low, particularly amongst women, likely explaining some of the observed differences in heritability across the sexes and the difficulty in precisely locating association signals in the cohort.

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An integrative analysis of genomic and exposomic data for complex traits and phenotypic prediction

Scientific Reports ◽

10.1038/s41598-021-00427-y ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Xuan Zhou ◽

S. Hong Lee

Keyword(s):

Complex Traits ◽

Prediction Accuracy ◽

Mixed Model ◽

Linear Mixed Model ◽

Complex Trait ◽

Great Promise ◽

Phenotypic Variance ◽

Additive Effects ◽

Mixed Model Approach ◽

The Uk

AbstractComplementary to the genome, the concept of exposome has been proposed to capture the totality of human environmental exposures. While there has been some recent progress on the construction of the exposome, few tools exist that can integrate the genome and exposome for complex trait analyses. Here we propose a linear mixed model approach to bridge this gap, which jointly models the random effects of the two omics layers on phenotypes of complex traits. We illustrate our approach using traits from the UK Biobank (e.g., BMI and height for N ~ 35,000) with a small fraction of the exposome that comprises 28 lifestyle factors. The joint model of the genome and exposome explains substantially more phenotypic variance and significantly improves phenotypic prediction accuracy, compared to the model based on the genome alone. The additional phenotypic variance captured by the exposome includes its additive effects as well as non-additive effects such as genome–exposome (gxe) and exposome–exposome (exe) interactions. For example, 19% of variation in BMI is explained by additive effects of the genome, while additional 7.2% by additive effects of the exposome, 1.9% by exe interactions and 4.5% by gxe interactions. Correspondingly, the prediction accuracy for BMI, computed using Pearson’s correlation between the observed and predicted phenotypes, improves from 0.15 (based on the genome alone) to 0.35 (based on the genome and exposome). We also show, using established theories, that integrating genomic and exposomic data can be an effective way of attaining a clinically meaningful level of prediction accuracy for disease traits. In conclusion, the genomic and exposomic effects can contribute to phenotypic variation via their latent relationships, i.e. genome-exposome correlation, and gxe and exe interactions, and modelling these effects has a potential to improve phenotypic prediction accuracy and thus holds a great promise for future clinical practice.

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The molecular genetics of hand preference revisited

10.1101/447177 ◽

2018 ◽

Cited By ~ 1

Author(s):

Carolien G.F. de Kovel ◽

Clyde Francks

Keyword(s):

Complex Traits ◽

Large Population ◽

Enrichment Analysis ◽

Hand Preference ◽

Population Based ◽

Gene Set Enrichment Analysis ◽

Uk Biobank ◽

Genome Wide ◽

A Genome ◽

The Uk

AbstractHand preference is a prominent behavioural trait linked to human brain asymmetry. A handful of genetic variants have been reported to associate with hand preference or quantitative measures related to it. Most of these reports were on the basis of limited sample sizes, by current standards for genetic analysis of complex traits. Here we performed a genome-wide association analysis of hand preference in the large, population-based UK Biobank cohort (N=331,037). We used gene-set enrichment analysis to investigate whether genes involved in visceral asymmetry are particularly relevant to hand preference, following one previous report. We found no evidence implicating any specific candidate variants previously reported. We also found no evidence that genes involved in visceral laterality play a role in hand preference. It remains possible that some of the previously reported genes or pathways are relevant to hand preference as assessed in other ways, or else are relevant within specific disorder populations. However, some or all of the earlier findings are likely to be false positives, and none of them appear relevant to hand preference as defined categorically in the general population. Within the UK Biobank itself, a significant association implicates the gene MAP2 in handedness.

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Common genetic variants and health outcomes appear geographically structured in the UK Biobank sample: Old concerns returning and their implications

10.1101/294876 ◽

2018 ◽

Cited By ~ 12

Author(s):

Simon Haworth ◽

Ruth Mitchell ◽

Laura Corbin ◽

Kaitlin H Wade ◽

Tom Dudding ◽

...

Keyword(s):

Genetic Variants ◽

Complex Traits ◽

Large Scale ◽

Genetic Data ◽

Population Based ◽

Risk Scores ◽

Phenotypic Variance ◽

Uk Biobank ◽

Common Genetic Variants ◽

The Uk

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.We 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.

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An integrative analysis of genomic and exposomic data for complex traits and phenotypic prediction

10.1101/2020.11.09.373704 ◽

2020 ◽

Cited By ~ 1

Author(s):

Xuan Zhou ◽

S. Hong Lee

Keyword(s):

Complex Traits ◽

Prediction Accuracy ◽

Mixed Model ◽

Linear Mixed Model ◽

Complex Trait ◽

Great Promise ◽

Phenotypic Variance ◽

Additive Effects ◽

Mixed Model Approach ◽

The Uk

AbstractComplementary to the genome, the concept of exposome has been proposed to capture the totality of human environmental exposures. While there has been some recent progress on the construction of the exposome, few tools exist that can integrate the genome and exposome for complex trait analyses. Here we propose a linear mixed model approach to bridge this gap, which jointly models the random effects of the two omics layers on phenotypes of complex traits. We illustrate our approach using traits from the UK Biobank (e.g., BMI & height for N ~ 40,000) with a small fraction of the exposome that comprises 28 lifestyle factors. The joint model of the genome and exposome explains substantially more phenotypic variance and significantly improves phenotypic prediction accuracy, compared to the model based on the genome alone. The additional phenotypic variance captured by the exposome includes its additive effects as well as non-additive effects such as genome-exposome (gxe) and exposome-exposome (exe) interactions. For example, 19% of variation in BMI is explained by additive effects of the genome, while additional 7.2% by additive effects of the exposome, 1.9% by exe interactions and 4.5% by gxe interactions. Correspondingly, the prediction accuracy for BMI, computed using Pearson’s correlation between the observed and predicted phenotypes, improves from 0.15 (based on the genome alone) to 0.35 (based on the genome & exposome). We also show, using established theories, integrating genomic and exposomic data is essential to attaining a clinically meaningful level of prediction accuracy for disease traits. In conclusion, the genomic and exposomic effects can contribute to phenotypic variation via their latent relationships, i.e. genome-exposome correlation, and gxe and exe interactions, and modelling these effects has a great potential to improve phenotypic prediction accuracy and thus holds a great promise for future clinical practice.

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Parent-of-origin effects in the UK Biobank

10.1101/2021.11.03.467079 ◽

2021 ◽

Author(s):

Robin J Hofmeister ◽

Simone Rubinacci ◽

Diogo M Ribeiro ◽

Zoltan Kutalik ◽

Alfonso Buil ◽

...

Keyword(s):

Platelet Count ◽

Complex Traits ◽

Probabilistic Approach ◽

Uk Biobank ◽

Chromosome X ◽

Identity By Descent ◽

Large Dataset ◽

Genome Wide ◽

Parent Of Origin ◽

The Uk

Identical genetic variations can have different phenotypic effects depending on their parent of origin (PofO). Yet, studies focussing on PofO effects have been largely limited in terms of sample size due to the need of parental genomes or known genealogies. Here, we used a novel probabilistic approach to infer PofO of individual alleles in the UK Biobank that does not require parental genomes nor prior knowledge of genealogy. Our model uses Identity-By-Descent (IBD) sharing with second- and third-degree relatives to assign alleles to parental groups and leverages chromosome X data in males to distinguish maternal from paternal groups. When combined with robust haplotype inference and haploid imputation, this allowed us to infer the PofO at 5.4 million variants genome-wide for 26,393 UK Biobank individuals. We used this large dataset to systematically screen 59 biomarkers and 38 anthropomorphic phenotypes for PofO effects and discovered 101 significant associations, demonstrating that this type of effects contributes to the genetics of complex traits. Notably, we retrieved well known PofO effects, such as the MEG3/DLK1 locus on platelet count, and we discovered many new ones at loci often unsuspected of being imprinted and, in some cases, previously thought to harbour additive associations.

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A powerful and efficient two-stage method for detecting gene-to-gene interactions in GWAS

Biostatistics ◽

10.1093/biostatistics/kxw060 ◽

2017 ◽

Vol 18 (3) ◽

pp. 477-494 ◽

Cited By ~ 5

Author(s):

Jakub Pecanka ◽

Marianne A. Jonker ◽

Zoltan Bochdanovits ◽

Aad W. Van Der Vaart ◽

Keyword(s):

Complex Traits ◽

Multiple Testing ◽

Statistical Power ◽

Genome Wide Association Study ◽

Score Test ◽

Interaction Model ◽

Type I ◽

Two Stage ◽

Genome Wide ◽

Strong Control

Summary For over a decade functional gene-to-gene interaction (epistasis) has been suspected to be a determinant in the “missing heritability” of complex traits. However, searching for epistasis on the genome-wide scale has been challenging due to the prohibitively large number of tests which result in a serious loss of statistical power as well as computational challenges. In this article, we propose a two-stage method applicable to existing case-control data sets, which aims to lessen both of these problems by pre-assessing whether a candidate pair of genetic loci is involved in epistasis before it is actually tested for interaction with respect to a complex phenotype. The pre-assessment is based on a two-locus genotype independence test performed in the sample of cases. Only the pairs of loci that exhibit non-equilibrium frequencies are analyzed via a logistic regression score test, thereby reducing the multiple testing burden. Since only the computationally simple independence tests are performed for all pairs of loci while the more demanding score tests are restricted to the most promising pairs, genome-wide association study (GWAS) for epistasis becomes feasible. By design our method provides strong control of the type I error. Its favourable power properties especially under the practically relevant misspecification of the interaction model are illustrated. Ready-to-use software is available. Using the method we analyzed Parkinson’s disease in four cohorts and identified possible interactions within several SNP pairs in multiple cohorts.

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GxEsum: a novel approach to estimate the phenotypic variance explained by genome-wide GxE interaction based on GWAS summary statistics for biobank-scale data

Genome Biology ◽

10.1186/s13059-021-02403-1 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Jisu Shin ◽

Sang Hong Lee

Keyword(s):

Complex Traits ◽

Error Rates ◽

Type I ◽

Phenotypic Variance ◽

Environment Interaction ◽

Summary Statistics ◽

Gxe Interaction ◽

Genome Wide ◽

Scale Data ◽

Variance Explained

AbstractGenetic variation in response to the environment, that is, genotype-by-environment interaction (GxE), is fundamental in the biology of complex traits and diseases. However, existing methods are computationally demanding and infeasible to handle biobank-scale data. Here, we introduce GxEsum, a method for estimating the phenotypic variance explained by genome-wide GxE based on GWAS summary statistics. Through comprehensive simulations and analysis of UK Biobank with 288,837 individuals, we show that GxEsum can handle a large-scale biobank dataset with controlled type I error rates and unbiased GxE estimates, and its computational efficiency can be hundreds of times higher than existing GxE methods.

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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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