reGenotyper: Detecting mislabeled samples in genetic data

• DOI: 10.1371/journal.pone.0171324
• Publication Date: 2017-02-13T19:44:20Z
• Authors: Konrad Zych; Basten L. Snoek; Mark Elvin; Miriam Rodriguez; K. Joeri Van der ...; Danny Arends; Harm-Jan Westra; Morris A. Swertz; Gino Poulin; Jan E. Kammenga; Rainer Breitling; Ritsert C. Jansen; Yang Li
• Keywords: C. ELEGANS; 0301 basic medicine; Nematoda; QTL; Gene Expression; DISEASE; NATURAL VARIATION DATA; Q; R; Animal Models; Genomics; Phenotypes; Phenotype; Experimental Organism Systems; Caenorhabditis Elegans; Physical Sciences; POPULATIONS; Medicine; Algorithms; Research Article; EXPRESSION; 570; Genotype; Permutation; Science; Quantitative Trait Loci; Polymorphism, Single Nucleotide; MIX-UPS; 03 medical and health sciences; Model Organisms; Genome-Wide Association Studies; Genetics; Life Science; Animals; Humans; Computer Simulation; GENOME-WIDE ASSOCIATION; Evolutionary Biology; Population Biology; IDENTIFICATION; PERTURBATION; Discrete Mathematics; Gene Expression Profiling; Organisms; Biology and Life Sciences; Computational Biology; Reproducibility of Results; Human Genetics; Genome Analysis; Invertebrates; Genetic Loci; Combinatorics; Genetic Polymorphism; Caenorhabditis; Population Genetics; Mathematics

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.