Inference on phenotype-specific effects of genes using multivariate kernel machine regression.

TitleInference on phenotype-specific effects of genes using multivariate kernel machine regression.
Publication TypeJournal Article
Year of Publication2018
AuthorsMaity, Arnab, Jing Zhao, Patrick F. Sullivan, and Jung-Ying Tzeng
JournalGenet Epidemiol
Volume42
Issue1
Pagination64-79
Date Published2018 02
ISSN1098-2272
KeywordsAge Factors, Antipsychotic Agents, Computer Simulation, Genetic Markers, Humans, Likelihood Functions, Models, Genetic, Phenotype, Sex Factors
Abstract

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.

DOI10.1002/gepi.22096
Alternate JournalGenet Epidemiol
Original PublicationInference on phenotype-specific effects of genes using multivariate kernel machine regression.
PubMed ID29314255
PubMed Central IDPMC5768462
Grant ListP01 CA142538 / CA / NCI NIH HHS / United States
R00 ES017744 / ES / NIEHS NIH HHS / United States
U01 MH109528 / MH / NIMH NIH HHS / United States