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

摘要

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.