Lu Zhao-Hua, Zhu Hongtu, Knickmeyer Rebecca C, Sullivan Patrick F, Williams Stephanie N, Zou Fei
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.
Genet Epidemiol. 2015 Dec;39(8):664-77. doi: 10.1002/gepi.21932. Epub 2015 Oct 30.
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.
全基因组关联研究（GWAS）通过单核苷酸多态性（SNP）分析来定位复杂性状的能力，可能会受到SNP效应大小适中、未观察到的因果SNP、相邻SNP之间的相关性以及SNP-SNP相互作用的影响。已证明，用于测试单个SNP集与个体表型之间关联的替代方法有望提高GWAS的效能。我们提出了一种贝叶斯潜在变量选择（BLVS）方法，以同时对大量SNP集与复杂性状之间的联合关联定位进行建模。与单SNP集分析相比，这种联合关联定位不仅考虑了SNP集之间的相关性，而且能够检测与性状边缘不相关的因果SNP集。分配给SNP集效应的尖峰和平板先验可以大大减少有效SNP集的维度，同时加快计算速度。开发了一种有效的马尔可夫链蒙特卡罗算法。模拟表明，在某些重要场景中，BLVS优于几种竞争的变量选择方法。
