Premium
Complete Effect‐Profile Assessment in Association Studies With Multiple Genetic and Multiple Environmental Factors
Author(s) -
Wang Zhi,
Maity Arnab,
Luo Yiwen,
Neely Megan L.,
Tzeng JungYing
Publication year - 2015
Publication title -
genetic epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.301
H-Index - 98
eISSN - 1098-2272
pISSN - 0741-0395
DOI - 10.1002/gepi.21877
Subject(s) - construct (python library) , genome wide association study , kernel (algebra) , computer science , genetic association , kernel method , regression , epistasis , association (psychology) , machine learning , data mining , simple (philosophy) , degrees of freedom (physics and chemistry) , statistics , mathematics , biology , support vector machine , genetics , psychology , genotype , gene , philosophy , physics , epistemology , combinatorics , quantum mechanics , single nucleotide polymorphism , psychotherapist , programming language
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.