Probabilistic inference of the genetic architecture of functional enrichment of complex traits

Due to the complexity of linkage disequilibrium (LD) and gene regulation, understanding the genetic basis of common complex traits remains a major challenge. We develop a Bayesian model (BayesRR-RC) implemented in a hybrid-parallel algorithm that scales to whole-genome sequence data on many hundreds of thousands of individuals, taking 22 seconds per iteration to estimate the inclusion probabilities and effect sizes of 8.4 million markers and 78 SNP-heritability parameters in the UK Biobank. Unlike naive penalized regression or mixed-linear model approaches, BayesRR-RC accurately estimates annotation-specific genetic architecture, determines the underlying joint effect size distribution and provides a probabilistic determination of association within marker groups in a single step. Of the genetic variation captured for height, body mass index, cardiovascular disease, and type-2 diabetes in the UK Biobank, only ≤ 10% is attributable to proximal regulatory regions within 10kb upstream of genes, while 12-25% is attributed to coding regions, up to 40% to intronic regions, and 22-28% to distal 10-500kb upstream regions. ≥60% of the variance contributed by these exonic, intronic and distal 10-500kb regions is underlain by many thousands of common variants, each with larger average effect sizes compared to the rest of the genome. We also find differences in the relationship between effect size and heterozygosity across annotation groups and across traits. Up to 24% of all cis and coding regions of each chromosome are associated with each trait, with over 3,100 independent exonic and intronic regions and over 5,400 independent regulatory regions having ≥95% probability of contributing ≥0.001% to the genetic variance for just these four traits. In the Estonian Biobank, we show improved prediction accuracy over other approaches and generate a posterior predictive distribution for each individual.

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Probabilistic inference of the genetic architecture underlying functional enrichment of complex traits

Nature Communications ◽

10.1038/s41467-021-27258-9 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Marion Patxot ◽

Daniel Trejo Banos ◽

Athanasios Kousathanas ◽

Etienne J. Orliac ◽

Sven E. Ojavee ◽

...

Keyword(s):

Open Source Software ◽

Complex Traits ◽

Genetic Architecture ◽

Functional Enrichment ◽

Regulatory Regions ◽

Coding Regions ◽

Heritability Estimation ◽

The Uk ◽

Marker Discovery

AbstractWe develop a Bayesian model (BayesRR-RC) that provides robust SNP-heritability estimation, an alternative to marker discovery, and accurate genomic prediction, taking 22 seconds per iteration to estimate 8.4 million SNP-effects and 78 SNP-heritability parameters in the UK Biobank. We find that only ≤10% of the genetic variation captured for height, body mass index, cardiovascular disease, and type 2 diabetes is attributable to proximal regulatory regions within 10kb upstream of genes, while 12-25% is attributed to coding regions, 32–44% to introns, and 22-28% to distal 10-500kb upstream regions. Up to 24% of all cis and coding regions of each chromosome are associated with each trait, with over 3,100 independent exonic and intronic regions and over 5,400 independent regulatory regions having ≥95% probability of contributing ≥0.001% to the genetic variance of these four traits. Our open-source software (GMRM) provides a scalable alternative to current approaches for biobank data.

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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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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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A model and test for coordinated polygenic epistasis in complex traits

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1922305118 ◽

2021 ◽

Vol 118 (15) ◽

pp. e1922305118

Author(s):

Brooke Sheppard ◽

Nadav Rappoport ◽

Po-Ru Loh ◽

Stephan J. Sanders ◽

Noah Zaitlen ◽

...

Keyword(s):

Genetic Variants ◽

Complex Traits ◽

Genetic Architecture ◽

Model Systems ◽

Disease Dynamics ◽

Uk Biobank ◽

Biological Models ◽

The Uk ◽

Complex Human Traits ◽

Main Effects

Interactions 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 epistasis (CE), and prove that several recent theories of genetic architecture fall under the formal umbrella of CE. Unlike standard epistasis models that assume epistasis and main effects are independent, CE captures systematic correlations between epistasis and main effects that result from pathway-level epistasis, on balance skewing the penetrance of genetic effects. To test for the existence of CE, 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 CE in 18 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, CE is a dimension of genetic architecture that can capture structured, systemic forms of epistasis in complex human traits.

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Negative short-range genomic autocorrelation of causal effects on human complex traits

10.1101/2020.09.23.310748 ◽

2020 ◽

Cited By ~ 1

Author(s):

Armin P. Schoech ◽

Omer Weissbrod ◽

Luke J. O’Connor ◽

Nick Patterson ◽

Huwenbo Shi ◽

...

Keyword(s):

Complex Traits ◽

Genetic Architecture ◽

Causal Effect ◽

Complex Trait ◽

Minor Allele ◽

Effect Sizes ◽

Point Estimate ◽

Genetic Risk Prediction ◽

Heritability Estimation ◽

The Uk

AbstractMost models of complex trait genetic architecture assume that signed causal effect sizes of each SNP (defined with respect to the minor allele) are uncorrelated with those of nearby SNPs, but it is currently unknown whether this is the case. We develop a new method, autocorrelation LD regression (ACLR), for estimating the genome-wide autocorrelation of causal minor allele effect sizes as a function of genomic distance. Our method estimates these autocorrelations by regressing the products of summary statistics on distance-dependent LD scores. We determined that ACLR robustly assesses the presence or absence of nonzero autocorrelation, producing unbiased estimates with well-calibrated standard errors in null simulations regardless of genetic architecture; if true autocorrelation is nonzero, ACLR correctly detects its sign, although estimates of the autocorrelation magnitude are susceptible to bias in cases of certain genetic architectures. We applied ACLR to 31 diseases and complex traits from the UK Biobank (average N=331K), meta-analyzing results across traits. We determined that autocorrelations were significantly negative at distances of 1-50bp (P = 8 × 10−6, point estimate −0.35 ±0.08) and 50-100bp (P = 2 × 10−3, point estimate −0.33 ± 0.11). We show that the autocorrelation is primarily driven by pairs of SNPs in positive LD, which is consistent with the expectation that linked SNPs with opposite effects are less impacted by natural selection. Our findings suggest that this mechanism broadly affects complex trait genetic architectures, and we discuss implications for association mapping, heritability estimation, and genetic risk prediction.

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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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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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Reproducibility in the UK Biobank of Genome-Wide Significant Signals Discovered in Earlier Genome-wide Association Studies

10.1101/2020.06.24.20139576 ◽

2020 ◽

Cited By ~ 1

Author(s):

Jack W. O’Sullivan ◽

John P. A. Ioannidis

Keyword(s):

Effect Size ◽

Association Studies ◽

Genome Wide Association ◽

P Value ◽

Genome Wide Association Studies ◽

Uk Biobank ◽

Single Nucleotide ◽

Genome Wide ◽

The Uk ◽

Open Question

AbstractWith the establishment of large biobanks, discovery of single nucleotide polymorphism (SNPs) that are associated with various phenotypes has been accelerated. An open question is whether SNPs identified with genome-wide significance in earlier genome-wide association studies (GWAS) are replicated also in later GWAS conducted in biobanks. To address this question, the authors examined a publicly available GWAS database and identified two, independent GWAS on the same phenotype (an earlier, “discovery” GWAS and a later, replication GWAS done in the UK biobank). The analysis evaluated 136,318,924 SNPs (of which 6,289 had reached p<5e-8 in the discovery GWAS) from 4,397,962 participants across nine phenotypes. The overall replication rate was 85.0% and it was lower for binary than for quantitative phenotypes (58.1% versus 94.8% respectively). There was a18.0% decrease in SNP effect size for binary phenotypes, but a 12.0% increase for quantitative phenotypes. Using the discovery SNP effect size, phenotype trait (binary or quantitative), and discovery p-value, we built and validated a model that predicted SNP replication with area under the Receiver Operator Curve = 0.90. While non-replication may often reflect lack of power rather than genuine false-positive findings, these results provide insights about which discovered associations are likely to be seen again across subsequent GWAS.

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