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

Clemontina A. Davenport, Arnab Maity, Patrick F. Sullivan, Jung Ying Tzeng

研究成果: Article同行評審

1 引文 斯高帕斯(Scopus)

摘要

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.

原文English
頁(從 - 到)117-138
頁數22
期刊Statistics in Biosciences
10
發行號1
DOIs
出版狀態Published - 2018 四月 1

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Biochemistry, Genetics and Molecular Biology (miscellaneous)

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