Empirical Bayes analysis of single nucleotide polymorphisms

Background: An important goal of whole-genome studies concerned with single nucleotide polymorphisms (SNPs) is the identification of SNPs associated with a covariate of interest such as the case-control status or the type of cancer. Since these studies often comprise the genotypes of hundreds of thousands of SNPs, methods are required that can cope with the corresponding multiple testing problem. For the analysis of gene expression data, approaches such as the empirical Bayes analysis of microarrays have been developed particularly for the detection of genes associated with the response. However, the empirical Bayes analysis of microarrays has only been suggested for binary responses when considering expression values, i.e. continuous predictors. Results: In this paper, we propose a modification of this empirical Bayes analysis that can be used to analyze high-dimensional categorical SNP data. This approach along with a generalized version

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Holger Schwender, Katja Ickstadt
• Keywords: BMCBI 2008, Empirical Bayes Analysis, Single Nucleotide Polymorphisms, SNPs