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• W2067286145 abstract "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/ Contact: lude@ludesign.nl Supplementary Information: Supplementary data are available at Bioinformatics online." @default.
• W2067286145 created "2016-06-24" @default.
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• W2067286145 date "2011-06-07" @default.
• W2067286145 modified "2023-10-16" @default.
• W2067286145 title "MixupMapper: correcting sample mix-ups in genome-wide datasets increases power to detect small genetic effects" @default.
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• W2067286145 doi "https://doi.org/10.1093/bioinformatics/btr323" @default.
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