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Bioinformatics. 2011 Aug 1;27(15):2104-11. doi: 10.1093/bioinformatics/btr323. Epub 2011 Jun 7.

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

Author information

1
Department of Genetics, University Medical Center Groningen, Groningen, The Netherlands.

Abstract

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/

PMID:
21653519
DOI:
10.1093/bioinformatics/btr323
[Indexed for MEDLINE]

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