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

2017 ◽  
Vol 114 (32) ◽  
pp. 8602-8607 ◽  
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.

scholarly journals Estimation of non-additive genetic variance in human complex traits from a large sample of unrelated individuals

2020 ◽  
Author(s):  
Valentin Hivert ◽  
Julia Sidorenko ◽  
Florian Rohart ◽  
Michael E Goddard ◽  
Jian Yang ◽  
...  
Keyword(s):  
Complex Traits ◽  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Sampling Variance ◽  
Analysis Model ◽  
Additive Variance ◽  
Snp Data ◽  
Causal Variants ◽  
The Uk ◽  
Epistatic Variance

AbstractNon-additive genetic variance for complex traits is traditionally estimated from data on relatives. It is notoriously difficult to estimate without bias in non-laboratory species, including humans, because of possible confounding with environmental covariance among relatives. In principle, non-additive variance attributable to common DNA variants can be estimated from a random sample of unrelated individuals with genome-wide SNP data. Here, we jointly estimate the proportion of variance explained by additive , dominance and additive-by-additive genetic variance in a single analysis model. We first show by simulations that our model leads to unbiased estimates and provide new theory to predict standard errors estimated using either least squares or maximum likelihood. We then apply the model to 70 complex traits using 254,679 unrelated individuals from the UK Biobank and 1.1M genotyped and imputed SNPs. We found strong evidence for additive variance (average across traits . In contrast, the average estimate of across traits was 0.001, implying negligible dominance variance at causal variants tagged by common SNPs. The average epistatic variance across the traits was 0.058, not significantly different from zero because of the large sampling variance. Our results provide new evidence that genetic variance for complex traits is predominantly additive, and that sample sizes of many millions of unrelated individuals are needed to estimate epistatic variance with sufficient precision.

scholarly journals Multi-resolution localization of causal variants across the genome

2019 ◽  
Author(s):  
Matteo Sesia ◽  
Eugene Katsevich ◽  
Stephen Bates ◽  
Emmanuel Candès ◽  
Chiara Sabatti
Keyword(s):  
Complex Traits ◽  
Computational Cost ◽  
Mixed Effects Models ◽  
Uk Biobank ◽  
False Discovery ◽  
New Findings ◽  
Multiple Resolutions ◽  
Causal Variants ◽  
The Uk ◽  
Negative Controls

AbstractWe present KnockoffZoom, a flexible method for the genetic mapping of complex traits at multiple resolutions. KnockoffZoom localizes causal variants by testing the conditional associations of genetic segments of decreasing width while provably controlling the false discovery rate using artificial genotypes as negative controls. Our method is equally valid for quantitative and binary phenotypes, making no assumptions about their genetic architectures. Instead, we rely on well-established genetic models of linkage disequilibrium. We demonstrate that our method can detect more associations than mixed effects models and achieve fine-mapping precision, at comparable computational cost. Lastly, we apply KnockoffZoom to data from 350k subjects in the UK Biobank and report many new findings.

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

2020 ◽  
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.

scholarly journals A model and test for coordinated polygenic epistasis in complex traits

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.

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

2019 ◽  
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.

scholarly journals The impact of identity-by-descent on fitness and disease in natural and domesticated Canid populations

2020 ◽  
Author(s):  
Jazlyn A. Mooney ◽  
Abigail Yohannes ◽  
Kirk E. Lohmueller
Keyword(s):  
Inbreeding Depression ◽  
Artificial Selection ◽  
Complex Traits ◽  
Genetic Architecture ◽  
Mendelian Inheritance ◽  
New Disease ◽  
Identity By Descent ◽  
Victorian Era ◽  
Causal Variants ◽  
The Impact

AbstractDomestic dogs have experienced population bottlenecks, recent inbreeding, and strong artificial selection. These processes have simplified the genetic architecture of complex traits, allowed deleterious variation to persist, and increased both identity-by-descent (IBD) segments and runs of homozygosity (ROH). As such, dogs provide an excellent model for examining how these evolutionary processes influence disease. We assembled a dataset containing 4,414 breed dogs, 327 village dogs, and 380 wolves genotyped at 117,288 markers and phenotype data for clinical and morphological phenotypes. Breed dogs have an enrichment of IBD and ROH, relative to both village dogs and wolves and we use these patterns to show that breed dogs have experienced differing severities of bottlenecks in their recent past. We then found that ROH burden is associated with phenotypes in breed dogs, such as lymphoma. We next test the prediction that breeds with greater ROH have more disease alleles reported in Online Mendelian Inheritance in Animals (OMIA). Surprisingly, the number of causal variants identified correlates with the popularity of that breed rather than the ROH or IBD burden, suggesting an ascertainment bias in OMIA. Lastly, we use the distribution of ROH across the genome to identify genes with depletions of ROH as potential hotspots for inbreeding depression and find multiple exons where ROH are never observed. Our results suggest that inbreeding has played a large role in shaping genetic and phenotypic variation in dogs, and that there remains an excess of understudied breeds that can reveal new disease-causing variation.Significance StatementDogs and humans have coexisted together for thousands of years, but it was not until the Victorian Era that humans practiced selective breeding to produce the modern standards we see today. Strong artificial selection during the breed formation period has simplified the genetic architecture of complex traits and caused an enrichment of identity-by-descent (IBD) segments in the dog genome. This study demonstrates the value of IBD segments and utilizes them to infer the recent demography of canids, predict case-control status for complex traits, locate regions of the genome potentially linked to inbreeding depression, and to identify understudied breeds where there is potential to discover new disease-associated variants.

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

2020 ◽  
Author(s):  
Marion Patxot ◽  
Daniel Trejo Banos ◽  
Athanasios Kousathanas ◽  
Etienne J Orliac ◽  
Sven E Ojavee ◽  
...  
Keyword(s):  
Effect Size ◽  
Complex Traits ◽  
Genetic Architecture ◽  
Penalized Regression ◽  
Functional Enrichment ◽  
Effect Sizes ◽  
Uk Biobank ◽  
Regulatory Regions ◽  
Coding Regions ◽  
The Uk

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.

scholarly journals Assortative Mating Biases Marker-based Heritability Estimators

2021 ◽  
Author(s):  
Richard Border ◽  
Sean O'Rourke ◽  
Teresa de Candia ◽  
Michael E Goddard ◽  
Peter M Visscher ◽  
...  
Keyword(s):  
Assortative Mating ◽  
Complex Traits ◽  
Genetic Architecture ◽  
Molecular Genetic ◽  
Simulation Based ◽  
Theoretical Predictions ◽  
Causal Variants ◽  
The Uk ◽  
Moments Estimators ◽  
Biased Estimates

Many complex traits are subject to assortative mating (AM), with recent molecular genetic findings confirming longstanding theoretical predictions that AM alters genetic architecture by inducing long range dependence across causal variants. However, all marker-based heritability estimators assume mating is random. We provide mathematical and simulation-based evidence demonstrating that both method-of-moments estimators and likelihood-based estimators produce biased estimates in the presence of AM and that common approaches to account for population structure fail to mitigate this bias. Then, examining height and educational attainment in the UK Biobank, we demonstrate that these biases affect real world traits. Finally, we derive corrected heritability estimators for traits under equilibrium AM.

scholarly journals Analysis of genetic dominance in the UK Biobank

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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