title: A note on statistical method for genotype calling of high-throughput SNP arrays.
abstract: We study the genotype calling algorithms for the high-throughput single-nucleotide polymorphism (SNP) arrays. Building upon the novel SNP-RMA preprocessing approach and the state-of-the-art CRLMM approach for genotype calling, we propose a simple modification to better model and combine the information across multiple SNPs with empirical Bayes modeling, which could often significantly improve the genotype calling of CRLMM. Through applications to the HapMap Trio data set and a non-HapMap test set of high quality SNP chips, we illustrate the competitive performance of the proposed method.
authors.affiliation: Division of Biostatistics, School of Public Health, University of Minnesota.
authors.forename: Jiaqi
authors.initials: J
authors.lastname: Yang
authors.forename: Wei
authors.initials: W
authors.lastname: Zhang
authors.forename: Baolin
authors.initials: B
authors.lastname: Wu
keywords: SNP arrays; empirical Bayes; genotype calling algorithm; mixture model