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Public Library of Science, PLoS ONE, 2(12), p. e0171324, 2017

DOI: 10.1371/journal.pone.0171324

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reGenotyper: Detecting mislabeled samples in genetic data

Journal article published in 2017 by Konrad Zych ORCID, Basten L. Snoek, Mark Elvin, Miriam Rodriguez, K. J. Van Der Velde ORCID, Danny Arends, Harm-Jan Westra ORCID, Morris A. Swertz, Gino Poulin ORCID, Jan E. Kammenga, Rainer Breitling ORCID, Ritsert C. Jansen ORCID, Yang Li, Suzannah Rutherford
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This paper is available in a repository.

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Abstract

In high-throughput molecular profiling studies, genotype labels can be wrongly assigned at various experimental steps; the resulting mislabeled samples seriously reduce the power to detect the genetic basis of phenotypic variation. We have developed an approach to detect potential mislabeling, recover the “ideal” genotype and identify “best-matched” labels for mislabeled samples. On average, we identified 4% of samples as mislabeled in eight published datasets, highlighting the necessity of applying a “data cleaning” step before standard data analysis.