Abstract
AbstractThe inflation of test statistics in genome-wide association (GWA) studies due to confounding factors such as cryptic relatedness, population stratification, and spurious non-zero genetic effects driven by linkage disequilibrium (LD) has been well characterized in the literature. The key theoretical contribution of this work is that epistasis (i.e., the interaction between multiple loci and/or genes) can also lead to misestimated GWA summary statistics. To address this challenge, we develop marginal epistatic LD score regression and the accompanying software package MELD: an extended framework which takes in GWA test statistics and accurately partitions true additive genetic variation from non-additive genetic variation, as well as other biases. By re-analyzing 25 well-studied quantitative phenotypes from 349,468 individuals of European ancestry in the UK Biobank and up to 159,095 individuals in BioBank Japan, we illustrate that nonlinear effects are a significant source of signal in reported GWA summary statistics and provide evidence that epistasis is more widespread in human phenotypes than previously reported. Of the 25 complex traits we analyzed in the UK Biobank, 23 phenotypes have a significant amount of tagged epistasis captured within additive summary statistics, including height, urate level, and cholesterol levels. The MELD software and its application to these biobanks represent a significant step towards resolving the true contribution of epistasis to human complex traits.
Publisher
Cold Spring Harbor Laboratory
Cited by
3 articles.
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