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Efficient Two-Step Testing of Gene-Gene Interactions in Genome-Wide Association Studies

Authors

  • Juan Pablo Lewinger,

    Corresponding author
    • Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California
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  • John L. Morrison,

    1. Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California
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  • Duncan C. Thomas,

    1. Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California
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  • Cassandra E. Murcray,

    1. Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California
    2. Department of Global Biostatistical Sciences, Amgen, Thousand Oaks, California
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    • These authors contributed to this study while working at the University of Southern California.

  • David V. Conti,

    1. Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California
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  • Dalin Li,

    1. Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California
    2. Medical Genetics Institute, Cedars-Sinai Medical Center, Los Angeles, California
    3. David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California
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    • These authors contributed to this study while working at the University of Southern California.

  • W. James Gauderman

    1. Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California
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Correspondence to: Juan Pablo Lewinger, Department of Preventive Medicine, University of Southern California, 2001 Soto Street, Suite 202U, Los Angeles, CA 90032. E-mail: Lewinger@usc.edu

ABSTRACT

Exhaustive testing of all possible SNP pairs in a genome-wide association study (GWAS) generally yields low power to detect gene-gene (G × G) interactions because of small effect sizes and stringent requirements for multiple-testing correction. We introduce a new two-step procedure for testing G × G interactions in case-control GWAS to detect interacting single nucleotide polymorphisms (SNPs) regardless of their marginal effects. In an initial screening step, all SNP pairs are tested for gene-gene association in the combined sample of cases and controls. In the second step, the pairs that pass the screening are followed up with a traditional test for G × G interaction. We show that the two-step method is substantially more powerful to detect G × G interactions than the exhaustive testing approach. For example, with 2,000 cases and 2,000 controls, the two-step method can have more than 90% power to detect an interaction odds ratio of 2.0 compared to less than 50% power for the exhaustive testing approach. Moreover, we show that a hybrid two-step approach that combines our newly proposed two-step test and the two-step test that screens for marginal effects retains the best power properties of both. The two-step procedures we introduce have the potential to uncover genetic signals that have not been previously identified in an initial single-SNP GWAS. We demonstrate the computational feasibility of the two-step G × G procedure by performing a G × G scan in the asthma GWAS of the University of Southern California Children's Health Study.

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