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A note on statistical method for genotype calling of high-throughput single-nucleotide polymorphism arrays

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We study the genotype calling algorithms for the high-throughput single-nucleotide polymorphism (SNP) arrays. Building upon the novel SNP-robust multi-chip average preprocessing approach and the state-of-the-art corrected robust linear model with Mahalanobis distance (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.
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Keywords: SNP arrays; empirical Bayes; genotype calling algorithm; mixture model

Document Type: Research Article

Affiliations: 1: Division of Biostatistics, School of Public Health, University of Minnesota, Minnesota, MN, 55455, USA 2: Department of Computer Science and Engineering, University of Minnesota, Minnesota, MN, 55455, USA

Publication date: June 1, 2013

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