title: A FAST ALGORITHM FOR DETECTING GENE-GENE INTERACTIONS IN GENOME-WIDE ASSOCIATION STUDIES.
abstract: With the recent advent of high-throughput genotyping techniques, genetic data for genome-wide association studies (GWAS) have become increasingly available, which entails the development of efficient and effective statistical approaches. Although many such approaches have been developed and used to identify single-nucleotide polymorphisms (SNPs) that are associated with complex traits or diseases, few are able to detect gene-gene interactions among different SNPs. Genetic interactions, also known as epistasis, have been recognized to play a pivotal role in contributing to the genetic variation of phenotypic traits. However, because of an extremely large number of SNP-SNP combinations in GWAS, the model dimensionality can quickly become so overwhelming that no prevailing variable selection methods are capable of handling this problem. In this paper, we present a statistical framework for characterizing main genetic effects and epistatic interactions in a GWAS study. Specifically, we first propose a two-stage sure independence screening (TS-SIS) procedure and generate a pool of candidate SNPs and interactions, which serve as predictors to explain and predict the phenotypes of a complex trait. We also propose a rates adjusted thresholding estimation (RATE) approach to determine the size of the reduced model selected by an independence screening. Regularization regression methods, such as LASSO or SCAD, are then applied to further identify important genetic effects. Simulation studies show that the TS-SIS procedure is computationally efficient and has an outstanding finite sample performance in selecting potential SNPs as well as gene-gene interactions. We apply the proposed framework to analyze an ultrahigh-dimensional GWAS data set from the Framingham Heart Study, and select 23 active SNPs and 24 active epistatic interactions for the body mass index variation. It shows the capability of our procedure to resolve the complexity of genetic control.
authors.affiliation: Department of Applied and Computational Mathematics and Statistics University of Notre Dame Notre Dame, Indiana 46556 USA jli7@nd.edu.
authors.forename: Jiahan
authors.initials: J
authors.lastname: Li
authors.affiliation: Institute for Studies in Economics Department of Statistics School of Economics Fujian Key Laboratory of Statistical Science Xiamen University Xiamen, Fujian 361005 China.
authors.forename: Wei
authors.initials: W
authors.lastname: Zhong
authors.affiliation: The Methodology Center Department of Statistics Pennsylvania State University University Park, Pennsylvania 16802 USA rli@stat.psu.edu.
authors.forename: Runze
authors.initials: R
authors.lastname: Li
authors.affiliation: Center for Statistical Genetics Pennsylvania State University Hershey, Pennsylvania 17033 USA rwu@hes.hmc.psu.edu.
authors.forename: Rongling
authors.initials: R
authors.lastname: Wu
keywords: GWAS; Gene–gene interaction; high-dimensional data; sure independence screening; variable selection