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Open AccessJournal ArticleDOI

Dominance Genetic Variation Contributes Little to the Missing Heritability for Human Complex Traits

TLDR
It is suggested that dominance variation at common SNPs explains only a small fraction of phenotypic variation for human complex traits and contributes little to the missing narrow-sense heritability problem.
Abstract
For human complex traits, non-additive genetic variation has been invoked to explain "missing heritability," but its discovery is often neglected in genome-wide association studies. Here we propose a method of using SNP data to partition and estimate the proportion of phenotypic variance attributed to additive and dominance genetic variation at all SNPs (hSNP(2) and δSNP(2)) in unrelated individuals based on an orthogonal model where the estimate of hSNP(2) is independent of that of δSNP(2). With this method, we analyzed 79 quantitative traits in 6,715 unrelated European Americans. The estimate of δSNP(2) averaged across all the 79 quantitative traits was 0.03, approximately a fifth of that for additive variation (average hSNP(2) = 0.15). There were a few traits that showed substantial estimates of δSNP(2), none of which were replicated in a larger sample of 11,965 individuals. We further performed genome-wide association analyses of the 79 quantitative traits and detected SNPs with genome-wide significant dominance effects only at the ABO locus for factor VIII and von Willebrand factor. All these results suggest that dominance variation at common SNPs explains only a small fraction of phenotypic variation for human complex traits and contributes little to the missing narrow-sense heritability problem.

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Citations
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Clinical use of current polygenic risk scores may exacerbate health disparities.

TL;DR: To realize the full and equitable potential of polygenic risk scores, greater diversity must be prioritized in genetic studies, and summary statistics must be publically disseminated to ensure that health disparities are not increased for those individuals already most underserved.
Journal ArticleDOI

The personal and clinical utility of polygenic risk scores.

TL;DR: The authors review recent studies that have demonstrated the utility of polygenic risk scores for disease risk stratification and their potential impact on early disease detection, prevention, therapeutic intervention and life planning.
Journal ArticleDOI

Concepts, estimation and interpretation of SNP-based heritability

TL;DR: Recently developed methods to estimate for a complex trait (and genetic correlation between traits) using individual-level or summary GWAS data are discussed and issues that could influence the accuracy of the models are discussed.
Journal ArticleDOI

The Hunger Genes: Pathways to Obesity

TL;DR: A more detailed mechanistic understanding of the molecular, physiological, and behavioral pathways involved in the development of obesity in susceptible individuals is critical for identifying effective mechanism-based preventative and therapeutic interventions.
References
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Journal ArticleDOI

PLINK: A Tool Set for Whole-Genome Association and Population-Based Linkage Analyses

TL;DR: This work introduces PLINK, an open-source C/C++ WGAS tool set, and describes the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation, which focuses on the estimation and use of identity- by-state and identity/descent information in the context of population-based whole-genome studies.
Book

Introduction to quantitative genetics

TL;DR: The genetic constitution of a population: Hardy-Weinberg equilibrium and changes in gene frequency: migration mutation, changes of variance, and heritability are studied.
Journal ArticleDOI

Principal components analysis corrects for stratification in genome-wide association studies

TL;DR: This work describes a method that enables explicit detection and correction of population stratification on a genome-wide scale and uses principal components analysis to explicitly model ancestry differences between cases and controls.
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