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Genotype-based association mapping of complex diseases: gene-environment interactions with multiple genetic markers and measurement error in environmental exposures


  • Iryna Lobach,

    1. Division of Biostatistics, New York University, School of Medicine, New York, New York
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  • Ruzong Fan,

    Corresponding author
    1. Department of Statistics, Texas A&M University, College Station, Texas
    2. Department of Epidemiology, MD Anderson Cancer Center, University of Texas, Houston, Texas
    3. Division of Cancer Control and Population Sciences, Surveillance Research Program, National Cancer Institute, Rockville, Maryland
    • Department of Statistics, Texas A&M University, 447 Blocker, College Station, TX 77843-3143
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  • Raymond J. Carroll

    1. Department of Statistics, Texas A&M University, College Station, Texas
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With the advent of dense single nucleotide polymorphism genotyping, population-based association studies have become the major tools for identifying human disease genes and for fine gene mapping of complex traits. We develop a genotype-based approach for association analysis of case-control studies of gene-environment interactions in the case when environmental factors are measured with error and genotype data are available on multiple genetic markers. To directly use the observed genotype data, we propose two genotype-based models: genotype effect and additive effect models. Our approach offers several advantages. First, the proposed risk functions can directly incorporate the observed genotype data while modeling the linkage disequilibrium information in the regression coefficients, thus eliminating the need to infer haplotype phase. Compared with the haplotype-based approach, an estimating procedure based on the proposed methods can be much simpler and significantly faster. In addition, there is no potential risk due to haplotype phase estimation. Further, by fitting the proposed models, it is possible to analyze the risk alleles/variants of complex diseases, including their dominant or additive effects. To model measurement error, we adopt the pseudo-likelihood method by Lobach et al. [2008]. Performance of the proposed method is examined using simulation experiments. An application of our method is illustrated using a population-based case-control study of association between calcium intake with the risk of colorectal adenoma development. Genet. Epidemiol. 34:792-802, 2010. © 2010 Wiley-Liss, Inc.