title: Penalized multivariate linear mixed model for longitudinal genome-wide association studies.
abstract: We consider analysis of Genetic Analysis Workshop 18 data, which involves multiple longitudinal traits and dense genome-wide single-nucleotide polymorphism (SNP) markers. We use a multivariate linear mixed model to account for the covariance of random effects and multivariate residuals. We divide the SNPs into groups according to the genes they belong to and score them using weighted sum statistics. We propose a penalized approach for genetic variant selection at the gene level. The overall modeling and penalized selection method is referred to as the penalized multivariate linear mixed model. Cross-validation is used for tuning parameter selection. A resampling approach is adopted to evaluate the relative stability of the identified genes. Application to the Genetic Analysis Workshop 18 data shows that the proposed approach can effectively select markers associated with phenotypes at gene level.
authors.affiliation: School of Public Health, University of Illinois at Chicago, 1601 W. Taylor Street, Chicago, IL 60612, USA.
authors.forename: Jin
authors.initials: J
authors.lastname: Liu
authors.affiliation: Department of Statistics & Actuarial Science, Department of Biostatistics, University of Iowa, 241 Schaeffer Hall, Iowa City, IA 52242, USA.
authors.forename: Jian
authors.initials: J
authors.lastname: Huang
authors.affiliation: School of Public Health, Yale University, 60 College Street, New Haven, CT 06520, USA.|VA Cooperative Studies Program Coordinating Center, West Haven, CT 06516, USA.
authors.forename: Shuangge
authors.initials: S
authors.lastname: Ma