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

Roshni A Patel; Shaila A Musharoff; Jeffrey P Spence; Harold Pimentel; Catherine Tcheandjieu; Hakhamanesh Mostafavi; Nasa Sinnott-Armstrong; Shoa L Clarke; Courtney J Smith; V.A. Million Veteran Program; Peter P Durda; Kent D Taylor; Russell Tracy; Yongmei Liu; W Craig Johnson; Francois Aguet; Kristin G Ardlie; Stacey Gabriel; Josh Smith; Deborah A Nickerson; Stephen S Rich; Jerome I Rotter; Philip S Tsao; Themistocles L Assimes; Jonathan K Pritchard

doi:10.1016/j.ajhg.2022.05.014

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

Am J Hum Genet. 2022 Jul 7;109(7):1286-1297. doi: 10.1016/j.ajhg.2022.05.014. Epub 2022 Jun 17.

Authors

Roshni A Patel¹, Shaila A Musharoff², Jeffrey P Spence³, Harold Pimentel⁴, Catherine Tcheandjieu⁵, Hakhamanesh Mostafavi³, Nasa Sinnott-Armstrong², Shoa L Clarke⁵, Courtney J Smith³; V.A. Million Veteran Program; Peter P Durda⁶, Kent D Taylor⁷, Russell Tracy⁶, Yongmei Liu⁸, W Craig Johnson⁹, Francois Aguet¹⁰, Kristin G Ardlie¹⁰, Stacey Gabriel¹⁰, Josh Smith¹¹, Deborah A Nickerson¹¹, Stephen S Rich¹², Jerome I Rotter⁷, Philip S Tsao⁵, Themistocles L Assimes⁵, Jonathan K Pritchard¹³

Affiliations

¹ Genetics, Stanford University School of Medicine, Stanford, CA, USA. Electronic address: rpatel7@stanford.edu.
² Genetics, Stanford University School of Medicine, Stanford, CA, USA; VA Palo Alto Health Care System, Palo Alto, CA, USA.
³ Genetics, Stanford University School of Medicine, Stanford, CA, USA.
⁴ Computational Medicine, University of California, Los Angeles, Los Angeles, CA, USA; Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA.
⁵ VA Palo Alto Health Care System, Palo Alto, CA, USA; Stanford University School of Medicine, Stanford, CA, USA.
⁶ The Robert Larner, M.D. College of Medicine at The University of Vermont, Burlington, VT, USA.
⁷ Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA.
⁸ Duke University School of Medicine, Durham, NC, USA.
⁹ Biostatistics, University of Washington, Seattle, WA, USA.
¹⁰ Broad Institute of MIT and Harvard, Cambridge, MA, USA.
¹¹ Genome Sciences, University of Washington, Seattle, WA, USA.
¹² Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA.
¹³ Genetics, Stanford University School of Medicine, Stanford, CA, USA; Biology, Stanford University, Stanford, CA, USA. Electronic address: pritch@stanford.edu.

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.

Keywords: complex traits; genetic correlation; genome-wide association; population genetics; statistical genetics.

Publication types

Research Support, Non-U.S. Gov't
Research Support, N.I.H., Extramural

MeSH terms

Cholesterol, LDL
Gene Expression
Genome-Wide Association Study*
Humans
Multifactorial Inheritance* / genetics
Polymorphism, Single Nucleotide / genetics
White People / genetics

Substances

Cholesterol, LDL

Abstract

Publication types

MeSH terms

Substances

Grants and funding