Complete effect-profile assessment in association studies with multiple genetic and multiple environmental factors.

Published

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Wang, Z; Maity, A; Luo, Y; Neely, ML; Tzeng, J-Y

Published Date

  • February 2015

Published In

Volume / Issue

  • 39 / 2

Start / End Page

  • 122 - 133

PubMed ID

  • 25538034

Pubmed Central ID

  • 25538034

Electronic International Standard Serial Number (EISSN)

  • 1098-2272

International Standard Serial Number (ISSN)

  • 0741-0395

Digital Object Identifier (DOI)

  • 10.1002/gepi.21877

Language

  • eng