title: BAYESIAN GROUP LASSO FOR NONPARAMETRIC VARYING-COEFFICIENT MODELS WITH APPLICATION TO FUNCTIONAL GENOME-WIDE ASSOCIATION STUDIES.
abstract: Although genome-wide association studies (GWAS) have proven powerful for comprehending the genetic architecture of complex traits, they are challenged by a high dimension of single-nucleotide polymorphisms (SNPs) as predictors, the presence of complex environmental factors, and longitudinal or functional natures of many complex traits or diseases. To address these challenges, we propose a high-dimensional varying-coefficient model for incorporating functional aspects of phenotypic traits into GWAS to formulate a so-called functional GWAS orfGWAS. Bayesian group lasso and the associated MCMC algorithms are developed to identify significant SNPs and estimate how they affect longitudinal traits through time-varying genetic actions. The model is generalized to analyze the genetic control of complex traits using subject-specific sparse longitudinal data. The statistical properties of the new model are investigated through simulation studies. We use the new model to analyze a real GWAS data set from the Framingham Heart Study, leading to the identification of several significant SNPs associated with age-specific changes of body mass index. ThefGWAS model, equipped with Bayesian group lassso, will provide a useful tool for genetic and developmental analysis of complex traits or diseases.
authors.affiliation: Department of Applied and Computational, Mathematics and Statistics, The University of Notre Dame, Notre Dame, IN 46556. jli7@nd.edu.
authors.forename: Jiahan
authors.initials: J
authors.lastname: Li
authors.affiliation: Center for Computational Biology, Beijing Forestry University, Beijing, China 100083. zhongwang@bjfu.edu.cn.
authors.forename: Zhong
authors.initials: Z
authors.lastname: Wang
authors.affiliation: Department of Statistics, The Methodology Center, The Pennsylvania State University, University Park, PA 16802. rli@stat.psu.edu.
authors.forename: Runze
authors.initials: R
authors.lastname: Li
authors.affiliation: Center for Statistical Genetics, The Pennsylvania State University, Hershey, PA 17033. rwu@hes.hmc.psu.edu.
authors.forename: Rongling
authors.initials: R
authors.lastname: Wu
keywords: Bayesian approach; GWAS; Group variable selection; Longitudinal data