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This article is part of the supplement: Genetic Analysis Workshop 16

Open Access Proceedings

Application of Bayesian classification with singular value decomposition method in genome-wide association studies

Soonil Kwon, Jinrui Cui, Shannon L Rhodes, Donald Tsiang, Jerome I Rotter and Xiuqing Guo*

Author Affiliations

Medical Genetics Institute, Cedars-Sinai Medical Center, 8635 West Third Street, Los Angeles, CA 90048, USA

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BMC Proceedings 2009, 3(Suppl 7):S9  doi:

Published: 15 December 2009


To analyze multiple single-nucleotide polymorphisms simultaneously when the number of markers is much larger than the number of studied individuals, as is the situation we have in genome-wide association studies (GWAS), we developed the iterative Bayesian variable selection method and successfully applied it to the simulated rheumatoid arthritis data provided by the Genetic Analysis Workshop 15 (GAW15). One drawback for applying our iterative Bayesian variable selection method is the relatively long running time required for evaluation of GWAS data. To improve computing speed, we recently developed a Bayesian classification with singular value decomposition (BCSVD) method. We have applied the BCSVD method here to the rheumatoid arthritis data distributed by GAW16 Problem 1 and demonstrated that the BCSVD method works well for analyzing GWAS data.