|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|
|Date Published||2015 Feb|
|Keywords||Computer Simulation, Environment, Gene-Environment Interaction, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Models, Genetic, Software|
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.
|Alternate Journal||Genet Epidemiol|
|Original Publication||Complete effect-profile assessment in association studies with multiple genetic and multiple environmental factors.|
|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.