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

被引：5
作者：
Davenport C.A. [1 ]
Maity A. [2 ]
Sullivan P.F. [3 ]
Tzeng J.-Y. [4 ,5 ,6 ]
机构：
[1] Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, 27707, NC
[2] Department of Statistics, North Carolina State University, Raleigh, 27695, NC
[3] Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, 27599, NC
[4] Department of Statistics, Bioinformatics Research Center, North Carolina State University, Raleigh, 27695, NC
[5] Department of Statistics, National Cheng-Kung University, Tainan
[6] Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei
来源：
Statistics in Biosciences | 2018年 / 10卷 / 1期
基金：
美国国家卫生研究院;
关键词：
Correlated binary responses; Generalized estimating equations; IBS kernel; Kernel machine; Non-parametric regression;
D O I：
10.1007/s12561-017-9189-9
中图分类号：
学科分类号：
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. © 2017, International Chinese Statistical Association.
引用
收藏
页码：117 / 138
页数：21
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