  In a mouse intercross with more than 500 animals and genome-wide gene
expression data on six tissues, we identified a high proportion (18%) of sample
mix-ups in the genotype data. Local expression quantitative trait loci (eQTL;
genetic loci influencing gene expression) with extremely large effect were used
to form a classifier to predict an individual's eQTL genotype based on
expression data alone. By considering multiple eQTL and their related
transcripts, we identified numerous individuals whose predicted eQTL genotypes
(based on their expression data) did not match their observed genotypes, and
then went on to identify other individuals whose genotypes did match the
predicted eQTL genotypes. The concordance of predictions across six tissues
indicated that the problem was due to mix-ups in the genotypes (though we
further identified a small number of sample mix-ups in each of the six panels
of gene expression microarrays). Consideration of the plate positions of the
DNA samples indicated a number of off-by-one and off-by-two errors, likely the
result of pipetting errors. Such sample mix-ups can be a problem in any genetic
study, but eQTL data allow us to identify, and even correct, such problems. Our
methods have been implemented in an R package, R/lineup.
