MixupMapper: correcting sample mix-ups in genome-wide datasets increases power to detect small genetic effects

Harm-Jan Westra, Ritsert C Jansen, Rudolf S N Fehrmann, Gerard J te Meerman, David van Heel, Cisca Wijmenga, Lude Franke*, Gerhardus te Meerman

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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MOTIVATION: Sample mix-ups can arise during sample collection, handling, genotyping or data management. It is unclear how often sample mix-ups occur in genome-wide studies, as there currently are no post hoc methods that can identify these mix-ups in unrelated samples. We have therefore developed an algorithm (MixupMapper) that can both detect and correct sample mix-ups in genome-wide studies that study gene expression levels.

RESULTS: We applied MixupMapper to five publicly available human genetical genomics datasets. On average, 3% of all analyzed samples had been assigned incorrect expression phenotypes: in one of the datasets 23% of the samples had incorrect expression phenotypes. The consequences of sample mix-ups are substantial: when we corrected these sample mix-ups, we identified on average 15% more significant cis-expression quantitative trait loci (cis-eQTLs). In one dataset, we identified three times as many significant cis-eQTLs after correction. Furthermore, we show through simulations that sample mix-ups can lead to an underestimation of the explained heritability of complex traits in genome-wide association datasets.

AVAILABILITY AND IMPLEMENTATION: MixupMapper is freely available at http://www.genenetwork.nl/mixupmapper/

Original languageEnglish
Pages (from-to)2104-2111
Number of pages8
Issue number15
Publication statusPublished - 1-Aug-2011


  • Algorithms
  • Gene Expression Profiling
  • Genome-Wide Association Study
  • Genomics
  • Genotype
  • Humans
  • Phenotype
  • Polymorphism, Single Nucleotide
  • Quantitative Trait Loci
  • Sensitivity and Specificity
  • Specimen Handling

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