This article is part of the supplement: Genetic Analysis Workshop 17: Unraveling Human Exome Data
Application of Bayesian regression with singular value decomposition method in association studies for sequence data
1 Medical Genetics Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
2 Center for Biostatistics and Bioinformatics, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
3 Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
BMC Proceedings 2011, 5(Suppl 9):S57 doi:10.1186/1753-6561-5-S9-S57Published: 29 November 2011
Genetic association studies usually involve a large number of single-nucleotide polymorphisms (SNPs) (k) and a relative small sample size (n), which produces the situation that k is much greater than n. Because conventional statistical approaches are unable to deal with multiple SNPs simultaneously when k is much greater than n, single-SNP association studies have been used to identify genes involved in a disease’s pathophysiology, which causes a multiple testing problem. To evaluate the contribution of multiple SNPs simultaneously to disease traits when k is much greater than n, we developed the Bayesian regression with singular value decomposition (BRSVD) method. The method reduces the dimension of the design matrix from k to n by applying singular value decomposition to the design matrix. We evaluated the model using a Markov chain Monte Carlo simulation with Gibbs sampler constructed from the posterior densities driven by conjugate prior densities. Permutation was incorporated to generate empirical p-values. We applied the BRSVD method to the sequence data provided by Genetic Analysis Workshop 17 and found that the BRSVD method is a practical method that can be used to analyze sequence data in comparison to the single-SNP association test and the penalized regression method.