Testing association between disease and multiple SNPs in a candidate gene

Authors

  • W. James Gauderman,

    Corresponding author
    1. Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California
    • Department of Preventive Medicine, University of Southern California, 1540 Alcazar St., Suite 220, Los Angeles, CA 90033
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  • Cassandra Murcray,

    1. Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California
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  • Frank Gilliland,

    1. Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California
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  • David V. Conti

    1. Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California
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Abstract

Current technology allows investigators to obtain genotypes at multiple single nucleotide polymorphism (SNPs) within a candidate locus. Many approaches have been developed for using such data in a test of association with disease, ranging from genotype-based to haplotype-based tests. We develop a new approach that involves two basic steps. In the first step, we use principal components (PCs) analysis to compute combinations of SNPs that capture the underlying correlation structure within the locus. The second step uses the PCs directly in a test of disease association. The PC approach captures linkage-disequilibrium information within a candidate region, but does not require the difficult computing implicit in a haplotype analysis. We demonstrate by simulation that the PC approach is typically as or more powerful than both genotype- and haplotype-based approaches. We also analyze association between respiratory symptoms in children and four SNPs in the Glutathione-S-Transferase P1 locus, based on data from the Children's Health Study. We observe stronger evidence of an association using the PC approach (p = 0.044) than using either a genotype-based (p = 0.13) or haplotype-based (p = 0.052) approach. Genet. Epidemiol. 2007. © 2007 Wiley-Liss, Inc.

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