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

Zhi Wang, Arnab Maity, Yiwen Luo, Megan L. Neely, Jung Ying Tzeng

研究成果: Article同行評審

8 引文 斯高帕斯(Scopus)

摘要

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.

原文English
頁(從 - 到)122-133
頁數12
期刊Genetic Epidemiology
39
發行號2
DOIs
出版狀態Published - 2015 2月 1

All Science Journal Classification (ASJC) codes

  • 流行病學
  • 遺傳學(臨床)

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