Davenport Clemontina A, Maity Arnab, Sullivan Patrick F, Tzeng Jung-Ying
Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, NC 27707, USA.
Department of Statistics, North Carolina State University, Raleigh, NC 27695, USA.
Stat Biosci. 2018 Apr;10(1):117-138. doi: 10.1007/s12561-017-9189-9. Epub 2017 Mar 24.
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 SNP-set on multiple, possibly correlated, binary responses. We develop a score-based test using a nonparametric modeling framework that jointly models the global effect of the marker set. We account for the nonlinear 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 (GEEs) 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 demonstrated our methods using the CATIE antibody study data and the CoLaus Study data.
在复杂疾病的基因研究中，评估多个二元结局很常见。这些结局通常是相关的，因为它们是从同一个体收集的，并且可能共享共同的标记效应。在本文中，我们提出了一种程序来检验一个单核苷酸多态性（SNP）集对多个可能相关的二元反应的效应。我们使用非参数建模框架开发了一种基于分数的检验方法，该框架联合对标记集的全局效应进行建模。我们使用再生核来考虑标记之间的非线性效应以及潜在的复杂相互作用。我们的检验程序只需要在原假设下进行估计，并且我们使用多元广义估计方程（GEEs）来估计模型成分，以考虑结局之间的相关性。我们通过模拟研究评估了我们检验的有限样本性能，并使用CATIE抗体研究数据和CoLaus研究数据展示了我们的方法。
