Meta-analysis of gene-environment interaction: joint estimation of SNP and SNP × environment regression coefficients

Authors

  • Alisa K. Manning,

    Corresponding author
    1. Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts
    • Department of Biostatistics, Boston University School of Public Health, Boston, MA
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  • Michael LaValley,

    1. Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts
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  • Ching-Ti Liu,

    1. Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts
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  • Kenneth Rice,

    1. Department of Biostatistics, University of Washington, Seattle, Washington
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  • Ping An,

    1. Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, Missouri
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  • Yongmei Liu,

    1. Department of Epidemiology and Prevention, Wake Forest University School of Medicine, Winston-Salem, North Carolina
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  • Iva Miljkovic,

    1. Center for Aging and Population Health, Department of Epidemiology, University of Pittsburgh, Pittsburgh, Pennsylvania
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  • Laura Rasmussen-Torvik,

    1. Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
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  • Tamara B. Harris,

    1. Geriatric Epidemiology Section, Laboratory of Epidemiology, Demography, and Biometry, National Institute on Aging, Bethesda, Maryland
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  • Michael A. Province,

    1. Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, Missouri
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  • Ingrid B. Borecki,

    1. Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, Missouri
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  • Jose C. Florez,

    1. Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts
    2. Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts
    3. Diabetes Research Center, Diabetes Unit, Massachusetts General Hospital, Boston, Massachusetts
    4. Department of Medicine, Harvard Medical School, Boston, Massachusetts
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  • James B. Meigs,

    1. Department of Medicine, Harvard Medical School, Boston, Massachusetts
    2. General Medicine Division, Massachusetts General Hospital, Boston, Massachusetts
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  • L. Adrienne Cupples,

    1. Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts
    2. National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, Massachusetts
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  • Josée Dupuis

    Corresponding author
    1. Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts
    2. National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, Massachusetts
    • Department of Biostatistics, Boston University School of Public Health, Boston, MA
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Abstract

Introduction: Genetic discoveries are validated through the meta-analysis of genome-wide association scans in large international consortia. Because environmental variables may interact with genetic factors, investigation of differing genetic effects for distinct levels of an environmental exposure in these large consortia may yield additional susceptibility loci undetected by main effects analysis. We describe a method of joint meta-analysis (JMA) of SNP and SNP by Environment (SNP × E) regression coefficients for use in gene-environment interaction studies. Methods: In testing SNP × E interactions, one approach uses a two degree of freedom test to identify genetic variants that influence the trait of interest. This approach detects both main and interaction effects between the trait and the SNP. We propose a method to jointly meta-analyze the SNP and SNP × E coefficients using multivariate generalized least squares. This approach provides confidence intervals of the two estimates, a joint significance test for SNP and SNP × E terms, and a test of homogeneity across samples. Results: We present a simulation study comparing this method to four other methods of meta-analysis and demonstrate that the JMA performs better than the others when both main and interaction effects are present. Additionally, we implemented our methods in a meta-analysis of the association between SNPs from the type 2 diabetes-associated gene PPARG and log-transformed fasting insulin levels and interaction by body mass index in a combined sample of 19,466 individuals from five cohorts. Genet. Epidemiol. 35:11–18, 2011. © 2010 Wiley-Liss, Inc.

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