Application of Bayesian classification with singular value decomposition method in genome-wide association studies.
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.
Kwon
Soonil
S
Medical Genetics Institute, Cedars-Sinai Medical Center, 8635 West Third Street, Los Angeles, CA 90048, USA. Soonil.Kwon@cshs.org.
Cui
Jinrui
J
Rhodes
Shannon L
SL
Tsiang
Donald
D
Rotter
Jerome I
JI
Guo
Xiuqing
X