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Recommended Joint and Meta-Analysis Strategies for Case-Control Association Testing of Single Low-Count Variants

Authors

  • Clement Ma,

    1. Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan
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  • Tom Blackwell,

    1. Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan
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  • Michael Boehnke,

    1. Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan
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  • Laura J. Scott,

    Corresponding author
    1. Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan
    • Correspondence to: Laura J. Scott, Department of Biostatistics and Center for Statistical Genetics, University of Michigan, M4134 SPH II, 1415 Washington Heights, Ann Arbor, MI 48109-2029. E-mail: ljst@umich.edu

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  • the GoT2D investigators

    1. Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan
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ABSTRACT

In genome-wide association studies of binary traits, investigators typically use logistic regression to test common variants for disease association within studies, and combine association results across studies using meta-analysis. For common variants, logistic regression tests are well calibrated, and meta-analysis of study-specific association results is only slightly less powerful than joint analysis of the combined individual-level data. In recent sequencing and dense chip based association studies, investigators increasingly test low-frequency variants for disease association. In this paper, we seek to (1) identify the association test with maximal power among tests with well controlled type I error rate and (2) compare the relative power of joint and meta-analysis tests. We use analytic calculation and simulation to compare the empirical type I error rate and power of four logistic regression based tests: Wald, score, likelihood ratio, and Firth bias-corrected. We demonstrate for low-count variants (roughly minor allele count [MAC] < 400) that: (1) for joint analysis, the Firth test has the best combination of type I error and power; (2) for meta-analysis of balanced studies (equal numbers of cases and controls), the score test is best, but is less powerful than Firth test based joint analysis; and (3) for meta-analysis of sufficiently unbalanced studies, all four tests can be anti-conservative, particularly the score test. We also establish MAC as the key parameter determining test calibration for joint and meta-analysis.

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