期刊
BIOINFORMATICS
卷 27, 期 15, 页码 2104-2111出版社
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btr323
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资金
- Netherlands Genomics Initiative [93519031]
- Netherlands Organization for Scientific Research (NWO, ZonMW [916.10.135]
- European Community [FP7/2007-2013, 259867]
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.
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