title: A Fast Multiple-Kernel Method With Applications to Detect Gene-Environment Interaction.
abstract: 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.
authors.affiliation: Department of Statistics, North Carolina State University, Raleigh, North Carolina, United States of America.
authors.forename: Rachel
authors.initials: R
authors.lastname: Marceau
authors.affiliation: Department of Statistics, North Carolina State University, Raleigh, North Carolina, United States of America.
authors.forename: Wenbin
authors.initials: W
authors.lastname: Lu
authors.affiliation: Department of Statistics, North Carolina State University, Raleigh, North Carolina, United States of America.
authors.forename: Shannon
authors.initials: S
authors.lastname: Holloway
authors.affiliation: Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, United States of America.|Department of Medicine, University of Virginia, Charlottesville, Virginia, United States of America.|Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, Virginia, United States of America.
authors.forename: Michèle M
authors.initials: MM
authors.lastname: Sale
authors.affiliation: Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, United States of America.|Department of Neurology, University of Virginia, Charlottesville, Virginia, United States of America.
authors.forename: Bradford B
authors.initials: BB
authors.lastname: Worrall
authors.affiliation: Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, United States of America.|Cardiovascular Research Center, University of Virginia, Charlottesville, Virginia, United States of America.
authors.forename: Stephen R
authors.initials: SR
authors.lastname: Williams
authors.affiliation: Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America.
authors.forename: Fang-Chi
authors.initials: FC
authors.lastname: Hsu
authors.affiliation: Department of Statistics, North Carolina State University, Raleigh, North Carolina, United States of America.|Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina, United States of America.|Department of Statistics, National Cheng-Kung University, Tainan, Taiwan.
authors.forename: Jung-Ying
authors.initials: JY
authors.lastname: Tzeng
mesh_terms: D000465:Algorithms; D059647:Gene-Environment Interaction; D014644:Genetic Variation; D006710:Homocysteine / Q000378:metabolism; D006801:Humans; D008957:Models, Genetic; D040641:Quantitative Trait Loci; D012044:Regression Analysis; D012307:Risk Factors; D012984:Software; D020521:Stroke / Q000209:etiology; D014815:Vitamins / Q000378:metabolism
chemical_list: D014815:Vitamins; D006710:Homocysteine
keywords: exon level association test; gene-environment interaction; gene-gene interactions; kernel machine regression; multiple-kernel analysis