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

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  • Jiaqi Yang
  • Wei Zhang
  • Baolin Wu

Abstract

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.

Suggested Citation

  • Jiaqi Yang & Wei Zhang & Baolin Wu, 2013. "A note on statistical method for genotype calling of high-throughput single-nucleotide polymorphism arrays," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(6), pages 1372-1381, June.
    • Handle: RePEc:taf:japsta:v:40:y:2013:i:6:p:1372-1381
    DOI: 10.1080/02664763.2013.785499
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    1. Quinn McNemar, 1947. "Note on the sampling error of the difference between correlated proportions or percentages," Psychometrika, Springer;The Psychometric Society, vol. 12(2), pages 153-157, June.
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