Assessing gene-environment interactions for common and rare variants with binary traits using gene-trait similarity regression.

TitleAssessing gene-environment interactions for common and rare variants with binary traits using gene-trait similarity regression.
Publication TypeJournal Article
Year of Publication2015
AuthorsZhao, Guolin, Rachel Marceau, Daowen Zhang, and Jung-Ying Tzeng
JournalGenetics
Volume199
Issue3
Pagination695-710
Date Published2015 Mar
ISSN1943-2631
KeywordsAlgorithms, Computer Simulation, Gene Frequency, Gene-Environment Interaction, Humans, Models, Genetic, Polymorphism, Genetic, Regression Analysis
Abstract

Accounting for gene-environment (G×E) interactions in complex trait association studies can facilitate our understanding of genetic heterogeneity under different environmental exposures, improve the ability to discover susceptible genes that exhibit little marginal effect, provide insight into the biological mechanisms of complex diseases, help to identify high-risk subgroups in the population, and uncover hidden heritability. However, significant G×E interactions can be difficult to find. The sample sizes required for sufficient power to detect association are much larger than those needed for genetic main effects, and interactions are sensitive to misspecification of the main-effects model. These issues are exacerbated when working with binary phenotypes and rare variants, which bear less information on association. In this work, we present a similarity-based regression method for evaluating G×E interactions for rare variants with binary traits. The proposed model aggregates the genetic and G×E information across markers, using genetic similarity, thus increasing the ability to detect G×E signals. The model has a random effects interpretation, which leads to robustness against main-effect misspecifications when evaluating G×E interactions. We construct score tests to examine G×E interactions and a computationally efficient EM algorithm to estimate the nuisance variance components. Using simulations and data applications, we show that the proposed method is a flexible and powerful tool to study the G×E effect in common or rare variant studies with binary traits.

DOI10.1534/genetics.114.171686
Alternate JournalGenetics
Original PublicationAssessing gene-environment interactions for common and rare variants with binary traits using gene-trait similarity regression.
PubMed ID25585620
PubMed Central IDPMC4349065
Grant ListR01 CA85848-12 / CA / NCI NIH HHS / United States
R01 MH084022 / MH / NIMH NIH HHS / United States
R01 CA085848 / CA / NCI NIH HHS / United States
T32 GM081057 / GM / NIGMS NIH HHS / United States
P01 CA142538 / CA / NCI NIH HHS / United States
T32GM081057 / GM / NIGMS NIH HHS / United States
Project: