TY - JOUR
T1 - Inference on phenotype-specific effects of genes using multivariate kernel machine regression
AU - Maity, Arnab
AU - Zhao, Jing
AU - Sullivan, Patrick F.
AU - Tzeng, Jung Ying
N1 - Funding Information:
The authors thank two anonymous referees for their insightful comments that helped to genuinely improve the overall quality of the article, and Dr. Robert Yolken at Johns Hopkins University for providing CATIE antibody data. This work was supported by National Institutes of Health grants R00 ES017744 (to AM) and P01 CA142538 (to JYT). The authors have no conflict of interest.
PY - 2018/2/1
Y1 - 2018/2/1
N2 - 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.
AB - 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.
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U2 - 10.1002/gepi.22096
DO - 10.1002/gepi.22096
M3 - Article
C2 - 29314255
AN - SCOPUS:85040657287
VL - 42
SP - 64
EP - 79
JO - Genetic Epidemiology
JF - Genetic Epidemiology
SN - 0741-0395
IS - 1
ER -