Genetic interactions drive heterogeneity in causal variant effect sizes for gene expression and complex traits

Like many others, we’ve long suspected that even the largest genetic studies are underpowered to detect individual gene-by-gene or gene-by-environment interactions that influence complex traits. We develop a novel statistical method that leverages recently admixed genomes to test for genetic interactions. We apply our method to two traits: gene expression in the Multi-Ethnic Study of Atherosclerosis (MESA) within TOPMed; and low-density lipoprotein cholesterol in the Million Veteran Program (MVP). We find compelling evidence that genetic interactions modify the effect sizes of causal variants for complex traits.

You can download the paper here or watch me give a talk on this project here!