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This article is part of the supplement: Genetic Analysis Workshop 17: Unraveling Human Exome Data

Open Access Proceedings

Application of Bayesian regression with singular value decomposition method in association studies for sequence data

Soonil Kwon12, Xiaofei Yan12, Jinrui Cui12, Jie Yao12, Kai Yang1, Donald Tsiang1, Xiaohui Li12, Jerome I Rotter13 and Xiuqing Guo123*

Author Affiliations

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

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BMC Proceedings 2011, 5(Suppl 9):S57  doi:10.1186/1753-6561-5-S9-S57

Published: 29 November 2011

Abstract

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.