Multiple SNP Set Analysis for Genome-Wide Association Studies Through Bayesian Latent Variable Selection

被引:18
|
作者
Lu, Zhao-Hua [1 ]
Zhu, Hongtu [1 ,2 ]
Knickmeyer, Rebecca C. [3 ]
Sullivan, Patrick F. [4 ]
Williams, Stephanie N. [4 ]
Zou, Fei [1 ]
机构
[1] Univ N Carolina, Dept Biostat, Chapel Hill, NC 27599 USA
[2] Univ N Carolina, Biomed Res Imaging Ctr, Chapel Hill, NC 27599 USA
[3] Univ N Carolina, Dept Psychiat, Chapel Hill, NC 27599 USA
[4] Univ N Carolina, Dept Genet, Chapel Hill, NC 27599 USA
基金
美国国家卫生研究院;
关键词
Bayesian variable selection; GWAS; linkage disequilibrium blocks; imaging phenotypes; ALZHEIMERS-DISEASE; GENETIC ASSOCIATION; APOLIPOPROTEIN-E; CANDIDATE GENE; MODEL; SIMILARITY; TESTS; RISK; SCHIZOPHRENIA; REGRESSION;
D O I
10.1002/gepi.21932
中图分类号
Q3 [遗传学];
学科分类号
071007 ; 090102 ;
摘要
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. (C) 2015 Wiley Periodicals, Inc.
引用
收藏
页码:664 / 677
页数:14
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