title: Analysis of genome-wide association studies with multiple outcomes using penalization.
abstract: Genome-wide association studies have been extensively conducted, searching for markers for biologically meaningful outcomes and phenotypes. Penalization methods have been adopted in the analysis of the joint effects of a large number of SNPs (single nucleotide polymorphisms) and marker identification. This study is partly motivated by the analysis of heterogeneous stock mice dataset, in which multiple correlated phenotypes and a large number of SNPs are available. Existing penalization methods designed to analyze a single response variable cannot accommodate the correlation among multiple response variables. With multiple response variables sharing the same set of markers, joint modeling is first employed to accommodate the correlation. The group Lasso approach is adopted to select markers associated with all the outcome variables. An efficient computational algorithm is developed. Simulation study and analysis of the heterogeneous stock mice dataset show that the proposed method can outperform existing penalization methods.
authors.affiliation: Department of Biostatistics, School of Public Health, Yale University, New Haven, Connecticut, United States of America. jin.liu.jl2329@yale.edu
authors.forename: Jin
authors.initials: J
authors.lastname: Liu
authors.forename: Jian
authors.initials: J
authors.lastname: Huang
authors.forename: Shuangge
authors.initials: S
authors.lastname: Ma
mesh_terms: D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D030541:Databases, Genetic; D005819:Genetic Markers; D020022:Genetic Predisposition to Disease; D005821:Genetic Techniques; D055106:Genome-Wide Association Study; D006801:Humans; D016014:Linear Models; D051379:Mice; D008957:Models, Genetic; D015233:Models, Statistical; D015999:Multivariate Analysis; D010641:Phenotype; D012044:Regression Analysis; D012307:Risk Factors
chemical_list: D005819:Genetic Markers