A general framework for robust and efficient association analysis in family-based designs: quantitative and dichotomous phenotypes

Authors

  • Sungho Won,

    Corresponding author
    1. The Research Center for Data Science, Chung-Ang University, Seoul, Korea
    • Department of Applied Statistics, Chung-Ang University, Seoul, Korea
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  • Christoph Lange

    1. Harvard Medical School, Boston, MA, U.S.A.
    2. Center for Genomic Medicine, Brigham and Women's Hospital, Boston, MA, U.S.A.
    3. Department of Biostatistics, Harvard School of Public Health, Boston, MA, U.S.A.
    4. Institute for Genomic Mathematics, University of Bonn, Bonn, Germany
    5. German Center for Neurodegenerative Diseases, Bonn, Germany
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Correspondence to: Sungho Won, Department of Applied Statistics, Chung-Ang Univ, 221 Heukseok-dong, Dongjak-gu, Seoul 156-756, Korea.

E-mail: swon@cau.ac.kr

Abstract

Although transmission disequilibrium tests (TDT) and the FBAT statistic are robust against population substructure, they have reduced statistical power, as compared with fully efficient tests that are not guarded against confounding because of population substructure. This has often limited the application of transmission disequilibrium tests/FBATs to candidate gene analysis, because, in a genome-wide association study, population substructure can be adjusted by approaches such as genomic control and EIGENSTRAT. Here, we provide new statistical methods for the analysis of quantitative and dichotomous phenotypes in extended families. Although the approach utilizes the polygenic model to maximize the efficiency, it still preserves the robustness to non-normality and misspecified covariance structures. In addition, the proposed method performs better than the existing methods for dichotomous phenotype, and the new transmission disequilibrium test for candidate gene analysis is more efficient than FBAT statistics. Copyright © 2013 John Wiley & Sons, Ltd.

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