Title | Complete effect-profile assessment in association studies with multiple genetic and multiple environmental factors. |
Publication Type | Journal Article |
Year of Publication | 2015 |
Authors | Wang, Zhi, Arnab Maity, Yiwen Luo, Megan L. Neely, and Jung-Ying Tzeng |
Journal | Genet Epidemiol |
Volume | 39 |
Issue | 2 |
Pagination | 122-33 |
Date Published | 2015 Feb |
ISSN | 1098-2272 |
Keywords | Computer Simulation, Environment, Gene-Environment Interaction, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Models, Genetic, Software |
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. |
DOI | 10.1002/gepi.21877 |
Alternate Journal | Genet Epidemiol |
Original Publication | Complete effect-profile assessment in association studies with multiple genetic and multiple environmental factors. |
PubMed ID | 25538034 |
PubMed Central ID | PMC4314365 |
Grant List | P01 CA142538 / CA / NCI NIH HHS / United States R00 ES017744 / ES / NIEHS NIH HHS / United States R01 MH084022 / MH / NIMH NIH HHS / United States R00ES017744 / ES / NIEHS NIH HHS / United States |
Complete effect-profile assessment in association studies with multiple genetic and multiple environmental factors.
Project: