title: Complete effect-profile assessment in association studies with multiple genetic and multiple environmental factors.
abstract: Studying complex diseases in the post genome-wide association studies (GWAS) era has led to developing methods that consider factor-sets rather than individual genetic/environmental factors (i.e., Multi-G-Multi-E studies), and mining for potential gene-environment (G×E) interactions has proven to be an invaluable aid in both discovery and deciphering underlying biological mechanisms. Current approaches for examining effect profiles in Multi-G-Multi-E analyses are either underpowered due to large degrees of freedom, ill-suited for detecting G×E interactions due to imprecise modeling of the G and E effects, or lack of capacity for modeling interactions between two factor-sets (e.g., existing methods focus primarily on a single E factor). In this work, we illustrate the issues encountered in constructing kernels for investigating interactions between two factor-sets, and propose a simple yet intuitive solution to construct the G×E kernel that retains the ease-of-interpretation of classic regression. We also construct a series of kernel machine (KM) score tests to evaluate the complete effect profile (i.e., the G, E, and G×E effects individually or in combination). We show, via simulations and a data application, that the proposed KM methods outperform the classic and PC regressions across a range of scenarios, including varying effect size, effect structure, and interaction complexity. The largest power gain was observed when the underlying effect structure involved complex G×E interactions; however, the proposed methods have consistent, powerful performance when the effect profile is simple or complex, suggesting that the proposed method could be a useful tool for exploratory or confirmatory G×E analysis.
authors.affiliation: Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina, United States of America.
authors.forename: Zhi
authors.initials: Z
authors.lastname: Wang
authors.forename: Arnab
authors.initials: A
authors.lastname: Maity
authors.forename: Yiwen
authors.initials: Y
authors.lastname: Luo
authors.forename: Megan L
authors.initials: ML
authors.lastname: Neely
authors.forename: Jung-Ying
authors.initials: JY
authors.lastname: Tzeng
mesh_terms: D003198:Computer Simulation; D004777:Environment; D059647:Gene-Environment Interaction; D020022:Genetic Predisposition to Disease; D055106:Genome-Wide Association Study / Q000379:methods*; D006801:Humans; D008957:Models, Genetic; D012984:Software
keywords: factor-set association analysis; genetic-environmental interactions; joint and conditional tests; kernel machine regression