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Genetic interactions drive heterogeneity in causal variant effect sizes for gene expression and complex traits.
Patel, Roshni A; Musharoff, Shaila A; Spence, Jeffrey P; Pimentel, Harold; Tcheandjieu, Catherine; Mostafavi, Hakhamanesh; Sinnott-Armstrong, Nasa; Clarke, Shoa L; Smith, Courtney J; Durda, Peter P; Taylor, Kent D; Tracy, Russell; Liu, Yongmei; Johnson, W Craig; Aguet, Francois; Ardlie, Kristin G; Gabriel, Stacey; Smith, Josh; Nickerson, Deborah A; Rich, Stephen S; Rotter, Jerome I; Tsao, Philip S; Assimes, Themistocles L; Pritchard, Jonathan K.
Afiliação
  • Patel RA; Genetics, Stanford University School of Medicine, Stanford, CA, USA. Electronic address: rpatel7@stanford.edu.
  • Musharoff SA; Genetics, Stanford University School of Medicine, Stanford, CA, USA; VA Palo Alto Health Care System, Palo Alto, CA, USA.
  • Spence JP; Genetics, Stanford University School of Medicine, Stanford, CA, USA.
  • Pimentel H; Computational Medicine, University of California, Los Angeles, Los Angeles, CA, USA; Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA.
  • Tcheandjieu C; VA Palo Alto Health Care System, Palo Alto, CA, USA; Stanford University School of Medicine, Stanford, CA, USA.
  • Mostafavi H; Genetics, Stanford University School of Medicine, Stanford, CA, USA.
  • Sinnott-Armstrong N; Genetics, Stanford University School of Medicine, Stanford, CA, USA; VA Palo Alto Health Care System, Palo Alto, CA, USA.
  • Clarke SL; VA Palo Alto Health Care System, Palo Alto, CA, USA; Stanford University School of Medicine, Stanford, CA, USA.
  • Smith CJ; Genetics, Stanford University School of Medicine, Stanford, CA, USA.
  • Durda PP; The Robert Larner, M.D. College of Medicine at The University of Vermont, Burlington, VT, USA.
  • Taylor KD; Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA.
  • Tracy R; The Robert Larner, M.D. College of Medicine at The University of Vermont, Burlington, VT, USA.
  • Liu Y; Duke University School of Medicine, Durham, NC, USA.
  • Johnson WC; Biostatistics, University of Washington, Seattle, WA, USA.
  • Aguet F; Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Ardlie KG; Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Gabriel S; Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Smith J; Genome Sciences, University of Washington, Seattle, WA, USA.
  • Nickerson DA; Genome Sciences, University of Washington, Seattle, WA, USA.
  • Rich SS; Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA.
  • Rotter JI; Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA.
  • Tsao PS; VA Palo Alto Health Care System, Palo Alto, CA, USA; Stanford University School of Medicine, Stanford, CA, USA.
  • Assimes TL; VA Palo Alto Health Care System, Palo Alto, CA, USA; Stanford University School of Medicine, Stanford, CA, USA.
  • Pritchard JK; Genetics, Stanford University School of Medicine, Stanford, CA, USA; Biology, Stanford University, Stanford, CA, USA. Electronic address: pritch@stanford.edu.
Am J Hum Genet ; 109(7): 1286-1297, 2022 07 07.
Article em En | MEDLINE | ID: mdl-35716666
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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Herança Multifatorial / Estudo de Associação Genômica Ampla Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article
Texto completo
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Herança Multifatorial / Estudo de Associação Genômica Ampla Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article