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A Powerful Test for SNP Effects on Multivariate Binary Outcomes Using Kernel Machine Regression

Author

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  • Clemontina A. Davenport

    (Duke University Medical Center)

  • Arnab Maity

    (North Carolina State University)

  • Patrick F. Sullivan

    (University of North Carolina at Chapel Hill)

  • Jung-Ying Tzeng

    (North Carolina State University
    National Cheng-Kung University
    National Taiwan University)

Abstract

Evaluating multiple binary outcomes is common in genetic studies of complex diseases. These outcomes are often correlated because they are collected from the same individual and they may share common marker effects. In this paper, we propose a procedure to test for effect of a single nucleotide polymorphism-set on multiple, possibly correlated, binary responses. We develop a score-based test using a non-parametric modeling framework that jointly models the global effect of the marker set. We account for the non-linear effects and potentially complicated interaction between markers using reproducing kernels. Our testing procedure only requires estimation under the null hypothesis and we use multivariate generalized estimating equations to estimate the model components to account for the correlation among the outcomes. We evaluate finite sample performance of our test via simulation study and demonstrate our methods using the Clinical Antipsychotic Trials of Intervention Effectiveness antibody study data and the CoLaus study data.

Suggested Citation

  • Clemontina A. Davenport & Arnab Maity & Patrick F. Sullivan & Jung-Ying Tzeng, 2018. "A Powerful Test for SNP Effects on Multivariate Binary Outcomes Using Kernel Machine Regression," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 10(1), pages 117-138, April.
    • Handle: RePEc:spr:stabio:v:10:y:2018:i:1:d:10.1007_s12561-017-9189-9
    DOI: 10.1007/s12561-017-9189-9
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    References listed on IDEAS

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    1. Dawei Liu & Xihong Lin & Debashis Ghosh, 2007. "Semiparametric Regression of Multidimensional Genetic Pathway Data: Least-Squares Kernel Machines and Linear Mixed Models," Biometrics, The International Biometric Society, vol. 63(4), pages 1079-1088, December.
    2. Stuart R. Lipsitz & Garrett M. Fitzmaurice & Joseph G. Ibrahim & Debajyoti Sinha & Michael Parzen & Steven Lipshultz, 2009. "Joint generalized estimating equations for multivariate longitudinal binary outcomes with missing data: an application to acquired immune deficiency syndrome data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(1), pages 3-20, January.
    3. Yang Zhao & Feng Chen & Rihong Zhai & Xihong Lin & Nancy Diao & David C Christiani, 2012. "Association Test Based on SNP Set: Logistic Kernel Machine Based Test vs. Principal Component Analysis," PLOS ONE, Public Library of Science, vol. 7(9), pages 1-11, September.
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