贝叶斯分类与奇异值分解方法在全基因组关联研究中的应用。
Application of Bayesian classification with singular value decomposition method in genome-wide association studies.
作者信息
Kwon Soonil, Cui Jinrui, Rhodes Shannon L, Tsiang Donald, Rotter Jerome I, Guo Xiuqing
机构信息
Medical Genetics Institute, Cedars-Sinai Medical Center, 8635 West Third Street, Los Angeles, CA 90048, USA.
出版信息
BMC Proc. 2009 Dec 15;3 Suppl 7(Suppl 7):S9. doi: 10.1186/1753-6561-3-S7-S9.
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.
在标记数量远多于研究个体数量的情况下，如我们在全基因组关联研究（GWAS）中所面临的情形，为了同时分析多个单核苷酸多态性，我们开发了迭代贝叶斯变量选择方法，并成功将其应用于遗传分析研讨会15（GAW15）提供的模拟类风湿性关节炎数据。应用我们的迭代贝叶斯变量选择方法的一个缺点是评估GWAS数据需要相对较长的运行时间。为了提高计算速度，我们最近开发了一种带奇异值分解的贝叶斯分类（BCSVD）方法。我们在此将BCSVD方法应用于GAW16问题1所分发的类风湿性关节炎数据，并证明BCSVD方法在分析GWAS数据方面效果良好。
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