학술논문

Genetic interactions drive heterogeneity in causal variant effect sizes for gene expression and complex traits
Document Type
article
Source
American Journal of Human Genetics. 109(7)
Subject
Epidemiology
Biological Sciences
Health Sciences
Genetics
Atherosclerosis
Human Genome
2.1 Biological and endogenous factors
Aetiology
Cholesterol
LDL
Gene Expression
Genome-Wide Association Study
Humans
Multifactorial Inheritance
Polymorphism
Single Nucleotide
White People
V.A. Million Veteran Program
complex traits
genetic correlation
genome-wide association
population genetics
statistical genetics
Medical and Health Sciences
Genetics & Heredity
Biological sciences
Biomedical and clinical sciences
Health sciences
Language
Abstract
Despite the growing number of genome-wide association studies (GWASs), it remains unclear to what extent gene-by-gene and gene-by-environment interactions influence complex traits in humans. The magnitude of genetic interactions in complex traits has been difficult to quantify because GWASs are generally underpowered to detect individual interactions of small effect. Here, we develop a method to test for genetic interactions that aggregates information across all trait-associated loci. Specifically, we test whether SNPs in regions of European ancestry shared between European American and admixed African American individuals have the same causal effect sizes. We hypothesize that in African Americans, the presence of genetic interactions will drive the causal effect sizes of SNPs in regions of European ancestry to be more similar to those of SNPs in regions of African ancestry. We apply our method to two traits: gene expression in 296 African Americans and 482 European Americans in the Multi-Ethnic Study of Atherosclerosis (MESA) and low-density lipoprotein cholesterol (LDL-C) in 74K African Americans and 296K European Americans in the Million Veteran Program (MVP). We find significant evidence for genetic interactions in our analysis of gene expression; for LDL-C, we observe a similar point estimate, although this is not significant, most likely due to lower statistical power. These results suggest that gene-by-gene or gene-by-environment interactions modify the effect sizes of causal variants in human complex traits.