title: Multiple SNP Set Analysis for Genome-Wide Association Studies Through Bayesian Latent Variable Selection.
abstract: The power of genome-wide association studies (GWAS) for mapping complex traits with single-SNP analysis (where SNP is single-nucleotide polymorphism) may be undermined by modest SNP effect sizes, unobserved causal SNPs, correlation among adjacent SNPs, and SNP-SNP interactions. Alternative approaches for testing the association between a single SNP set and individual phenotypes have been shown to be promising for improving the power of GWAS. We propose a Bayesian latent variable selection (BLVS) method to simultaneously model the joint association mapping between a large number of SNP sets and complex traits. Compared with single SNP set analysis, such joint association mapping not only accounts for the correlation among SNP sets but also is capable of detecting causal SNP sets that are marginally uncorrelated with traits. The spike-and-slab prior assigned to the effects of SNP sets can greatly reduce the dimension of effective SNP sets, while speeding up computation. An efficient Markov chain Monte Carlo algorithm is developed. Simulations demonstrate that BLVS outperforms several competing variable selection methods in some important scenarios.
authors.affiliation: Department of Biostatistics, University of North Carolina at Chapel Hill, North Carolina, United States of America.
authors.forename: Zhao-Hua
authors.initials: ZH
authors.lastname: Lu
authors.affiliation: Department of Biostatistics, University of North Carolina at Chapel Hill, North Carolina, United States of America.|Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, North Carolina, United States of America.
authors.forename: Hongtu
authors.initials: H
authors.lastname: Zhu
authors.affiliation: Department of Psychiatry, University of North Carolina at Chapel Hill, North Carolina, United States of America.
authors.forename: Rebecca C
authors.initials: RC
authors.lastname: Knickmeyer
authors.affiliation: Department of Genetics, University of North Carolina at Chapel Hill, North Carolina, United States of America.
authors.forename: Patrick F
authors.initials: PF
authors.lastname: Sullivan
authors.affiliation: Department of Genetics, University of North Carolina at Chapel Hill, North Carolina, United States of America.
authors.forename: Stephanie N
authors.initials: SN
authors.lastname: Williams
authors.affiliation: Department of Biostatistics, University of North Carolina at Chapel Hill, North Carolina, United States of America.
authors.forename: Fei
authors.initials: F
authors.lastname: Zou
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D005787:Gene Frequency / Q000235:genetics*; D055106:Genome-Wide Association Study / Q000379:methods*; D006801:Humans; D015810:Linkage Disequilibrium / Q000235:genetics; D008390:Markov Chains; D008957:Models, Genetic; D009010:Monte Carlo Method; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*; D019655:Quantitative Trait, Heritable; D012559:Schizophrenia / Q000453:epidemiology / Q000235:genetics*; D013548:Sweden / Q000453:epidemiology
keywords: Bayesian variable selection; GWAS; imaging phenotypes; linkage disequilibrium blocks