A Fast Multiple-Kernel Method With Applications to Detect Gene-Environment Interaction.

TitleA Fast Multiple-Kernel Method With Applications to Detect Gene-Environment Interaction.
Publication TypeJournal Article
Year of Publication2015
AuthorsMarceau, Rachel, Wenbin Lu, Shannon Holloway, Michèle M. Sale, Bradford B. Worrall, Stephen R. Williams, Fang-Chi Hsu, and Jung-Ying Tzeng
JournalGenet Epidemiol
Date Published2015 Sep
KeywordsAlgorithms, Gene-Environment Interaction, Genetic Variation, Homocysteine, Humans, Models, Genetic, Quantitative Trait Loci, Regression Analysis, Risk Factors, Software, Stroke, Vitamins

Kernel machine (KM) models are a powerful tool for exploring associations between sets of genetic variants and complex traits. Although most KM methods use a single kernel function to assess the marginal effect of a variable set, KM analyses involving multiple kernels have become increasingly popular. Multikernel analysis allows researchers to study more complex problems, such as assessing gene-gene or gene-environment interactions, incorporating variance-component based methods for population substructure into rare-variant association testing, and assessing the conditional effects of a variable set adjusting for other variable sets. The KM framework is robust, powerful, and provides efficient dimension reduction for multifactor analyses, but requires the estimation of high dimensional nuisance parameters. Traditional estimation techniques, including regularization and the "expectation-maximization (EM)" algorithm, have a large computational cost and are not scalable to large sample sizes needed for rare variant analysis. Therefore, under the context of gene-environment interaction, we propose a computationally efficient and statistically rigorous "fastKM" algorithm for multikernel analysis that is based on a low-rank approximation to the nuisance effect kernel matrices. Our algorithm is applicable to various trait types (e.g., continuous, binary, and survival traits) and can be implemented using any existing single-kernel analysis software. Through extensive simulation studies, we show that our algorithm has similar performance to an EM-based KM approach for quantitative traits while running much faster. We also apply our method to the Vitamin Intervention for Stroke Prevention (VISP) clinical trial, examining gene-by-vitamin effects on recurrent stroke risk and gene-by-age effects on change in homocysteine level.

Alternate JournalGenet Epidemiol
Original PublicationA fast multiple-kernel method with applications to detect gene-environment interaction.
PubMed ID26139508
PubMed Central IDPMC4544636
Grant ListU01 HG005160 / HG / NHGRI NIH HHS / United States
R01 MH084022 / MH / NIMH NIH HHS / United States
R01 NS34447 / NS / NINDS NIH HHS / United States
R01 CA140632 / CA / NCI NIH HHS / United States
T32 GM081057 / GM / NIGMS NIH HHS / United States
R01 NS034447 / NS / NINDS NIH HHS / United States
5T32GM081057 / GM / NIGMS NIH HHS / United States
P01CA142538 / CA / NCI NIH HHS / United States
P01 CA142538 / CA / NCI NIH HHS / United States