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

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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Analysis of genetic dominance in the UK Biobank

10.1101/2021.08.15.456387 ◽

2021 ◽

Author(s):

Duncan S Palmer ◽

Wei Zhou ◽

Liam Abbott ◽

Nik Baya ◽

Claire Churchhouse ◽

...

Keyword(s):

Complex Traits ◽

Multiple Testing ◽

Model Organisms ◽

Systematic Evaluation ◽

Hair Color ◽

Phenotypic Variance ◽

Additive Effects ◽

Uk Biobank ◽

Genome Wide ◽

The Uk

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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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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Detection and quantification of inbreeding depression for complex traits from SNP data

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1621096114 ◽

2017 ◽

Vol 114 (32) ◽

pp. 8602-8607 ◽

Cited By ~ 15

Author(s):

Loic Yengo ◽

Zhihong Zhu ◽

Naomi R. Wray ◽

Bruce S. Weir ◽

Jian Yang ◽

...

Keyword(s):

Inbreeding Depression ◽

Complex Traits ◽

Genetic Architecture ◽

Handgrip Strength ◽

Uk Biobank ◽

Snp Data ◽

Causal Variants ◽

Auditory Acuity ◽

The Uk ◽

Detection And Quantification

Quantifying the effects of inbreeding is critical to characterizing the genetic architecture of complex traits. This study highlights through theory and simulations the strengths and shortcomings of three SNP-based inbreeding measures commonly used to estimate inbreeding depression (ID). We demonstrate that heterogeneity in linkage disequilibrium (LD) between causal variants and SNPs biases ID estimates, and we develop an approach to correct this bias using LD and minor allele frequency stratified inference (LDMS). We quantified ID in 25 traits measured in ∼140,000 participants of the UK Biobank, using LDMS, and confirmed previously published ID for 4 traits. We find unique evidence of ID for handgrip strength, waist/hip ratio, and visual and auditory acuity (ID between −2.3 and −5.2 phenotypic SDs for complete inbreeding; P<0.001). Our results illustrate that a careful choice of the measure of inbreeding combined with LDMS stratification improves both detection and quantification of ID using SNP data.

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Global genetic heterogeneity in adaptive traits

10.1101/2021.02.26.433043 ◽

2021 ◽

Cited By ~ 1

Author(s):

William Andres Lopez-Arboleda ◽

Stephan Reinert ◽

Magnus Nordborg ◽

Arthur Korte

Keyword(s):

Genetic Heterogeneity ◽

Complex Traits ◽

Genetic Architecture ◽

Human Genetics ◽

Association Studies ◽

Local Environment ◽

Genome Wide Association Studies ◽

Major Objective ◽

Genome Wide ◽

Wide Range

AbstractUnderstanding the genetic architecture of complex traits is a major objective in biology. The standard approach for doing so is genome-wide association studies (GWAS), which aim to identify genetic polymorphisms responsible for variation in traits of interest. In human genetics, consistency across studies is commonly used as an indicator of reliability. However, if traits are involved in adaptation to the local environment, we do not necessarily expect reproducibility. On the contrary, results may depend on where you sample, and sampling across a wide range of environments may decrease the power of GWAS because of increased genetic heterogeneity. In this study, we examine how sampling affects GWAS for a variety of phenotypes in the model plant species Arabididopsis thaliana. We show that traits like flowering time are indeed influenced by distinct genetic effects in local populations. Furthermore, using gene expression as a molecular phenotype, we show that some genes are globally affected by shared variants, while others are affected by variants specific to subpopulations. Remarkably, the former are essentially all cis-regulated, whereas the latter are predominately affected by trans-acting variants. Our result illustrate that conclusions about genetic architecture can be incredibly sensitive to sampling and population structure.

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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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Global Biobank Engine: enabling genotype-phenotype browsing for biobank summary statistics

Bioinformatics ◽

10.1093/bioinformatics/bty999 ◽

2018 ◽

Vol 35 (14) ◽

pp. 2495-2497 ◽

Cited By ~ 27

Author(s):

Gregory McInnes ◽

Yosuke Tanigawa ◽

Chris DeBoever ◽

Adam Lavertu ◽

Julia Eve Olivieri ◽

...

Keyword(s):

Association Studies ◽

Genetic Association Studies ◽

Genome Wide Association Studies ◽

Summary Statistics ◽

Uk Biobank ◽

Patient Privacy ◽

Web Based ◽

Genome Wide ◽

Wide Range ◽

The Uk

Abstract Summary Large biobanks linking phenotype to genotype have led to an explosion of genetic association studies across a wide range of phenotypes. Sharing the knowledge generated by these resources with the scientific community remains a challenge due to patient privacy and the vast amount of data. Here, we present Global Biobank Engine (GBE), a web-based tool that enables exploration of the relationship between genotype and phenotype in biobank cohorts, such as the UK Biobank. GBE supports browsing for results from genome-wide association studies, phenome-wide association studies, gene-based tests and genetic correlation between phenotypes. We envision GBE as a platform that facilitates the dissemination of summary statistics from biobanks to the scientific and clinical communities. Availability and implementation GBE currently hosts data from the UK Biobank and can be found freely available at biobankengine.stanford.edu.

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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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Quantifying the contribution of dominance effects to complex trait variation in biobank-scale data

10.1101/2020.11.10.376897 ◽

2020 ◽

Author(s):

Ali Pazokitoroudi ◽

Alec M. Chiu ◽

Kathryn S. Burch ◽

Bogdan Pasaniuc ◽

Sriram Sankararaman

Keyword(s):

Complex Traits ◽

Complex Trait ◽

Genetic Effects ◽

Trait Variation ◽

Wide Range ◽

Unbiased Estimates ◽

The Uk ◽

Additive Genetic Effects ◽

Intense Debate ◽

Scale Data

AbstractThe proportion of variation in complex traits that can be attributed to non-additive genetic effects has been a topic of intense debate. The availability of Biobank-scale datasets of genotype and trait data from unrelated individuals opens up the possibility of obtaining precise estimates of the contribution of non-additive genetic effects. We present an efficient method that can partition the variation in complex traits into variance that can be attributed to additive (additive heritability) and dominance (dominance heritability) effects across all genotyped SNPs in a large collection of unrelated individuals. Over a wide range of genetic architectures, our method yields unbiased estimates of heritability. We applied our method, in turn, to array genotypes as well as imputed genotypes (at common SNPs with minor allele frequency, MAF > 1%) and 50 quantitative traits measured in 291, 273 unrelated white British individuals in the UK Biobank. Averaged across these 50 traits, we find that additive heritability on array SNPs is 21.86% while dominance heritability is 0.13% (about 0.48% of the additive heritability) with qualitatively similar results for imputed genotypes. We find no evidence for dominance heritability ( accounting for the number of traits tested) and estimate that dominance heritability is unlikely to exceed 1% for the traits analyzed. Our analyses indicate a limited contribution of dominance heritability to complex trait variation.

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Coordinated Interaction: A model and test for globally signed epistasis in complex traits

10.1101/2020.02.14.949883 ◽

2020 ◽

Cited By ~ 1

Author(s):

Brooke Sheppard ◽

Nadav Rappoport ◽

Po-Ru Loh ◽

Stephan J. Sanders ◽

Andy Dahl ◽

...

Keyword(s):

Complex Traits ◽

Genetic Architecture ◽

Model Systems ◽

Disease Dynamics ◽

Uk Biobank ◽

Biological Models ◽

Model Sets ◽

The Uk ◽

Complex Human Traits ◽

Main Effects

AbstractInteractions between genetic variants – epistasis – is pervasive in model systems and can profoundly impact evolutionary adaption, population disease dynamics, genetic mapping, and precision medicine efforts. In this work we develop a model for structured polygenic epistasis, called Coordinated Interaction (CI), and prove that several recent theories of genetic architecture fall under the formal umbrella of CI. Unlike standard polygenic epistasis models that assume interaction and main effects are independent, in the CI model, sets of SNPs broadly interact positively or negatively, on balance skewing the penetrance of main genetic effects. To test for the existence of CI we propose the even-odd (EO) test and prove it is calibrated in a range of realistic biological models. Applying the EO test in the UK Biobank, we find evidence of CI in 14 of 26 traits spanning disease, anthropometric, and blood categories. Finally, we extend the EO test to tissue-specific enrichment and identify several plausible tissue-trait pairs. Overall, CI is a new dimension of genetic architecture that can capture structured, systemic interactions in complex human traits.

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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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