Maity Arnab, Zhao Jing, Sullivan Patrick F, Tzeng Jung-Ying
Department of Statistics, North Carolina State University, Raleigh, North Carolina, United States of America.
Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina, United States of America.
Genet Epidemiol. 2018 Feb;42(1):64-79. doi: 10.1002/gepi.22096. Epub 2018 Jan 3.
We consider the problem of assessing the joint effect of a set of genetic markers on multiple, possibly correlated phenotypes of interest. We develop a kernel machine based multivariate regression framework, where the joint effect of the marker set on each of the phenotypes is modeled using prespecified kernel functions with unknown variance components. Unlike most existing methods that mainly focus on the global association between the marker set and the phenotype set, we develop estimation and testing procedures to study phenotype-specific associations. Specifically, we develop an estimation method based on the penalized likelihood approach to estimate phenotype-specific effects and their corresponding standard errors while accounting for possible correlation among the phenotypes. We develop testing procedures for the association of the marker set with any subset of phenotypes using a score-based variance components testing method. We assess the performance of our proposed methodology via a simulation study and demonstrate the utility of the proposed method using the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) data.
我们考虑评估一组遗传标记对多个可能相关的感兴趣表型的联合效应这一问题。我们开发了一个基于核机器的多变量回归框架，其中使用具有未知方差分量的预先指定的核函数对标记集对每个表型的联合效应进行建模。与大多数现有方法主要关注标记集与表型集之间的全局关联不同，我们开发了估计和检验程序来研究特定于表型的关联。具体而言，我们开发了一种基于惩罚似然法的估计方法，以估计特定于表型的效应及其相应的标准误差，同时考虑表型之间可能的相关性。我们使用基于分数的方差分量检验方法开发了标记集与任何表型子集之间关联的检验程序。我们通过模拟研究评估了我们提出的方法的性能，并使用干预有效性临床抗精神病药物试验（CATIE）数据证明了所提出方法的实用性。
