Identification and Correction of Sample Mix-Ups in Expression Genetic Data: A Case Study.
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 (although 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.
Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, Wisconsin 53706 kbroman@biostat.wisc.edu.
Karl W
http://orcid.org/0000-0002-4914-6671
KW
Broman
Department of Biochemistry, University of Wisconsin, Madison, Wisconsin 53706.
Mark P
MP
Keller
Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, Wisconsin 53706.
Aimee Teo
http://orcid.org/0000-0003-3783-2807
AT
Broman
Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, Wisconsin 53706.
Christina
C
Kendziorski
Department of Statistics, University of Wisconsin, Madison, Wisconsin 53706 Department of Horticulture, University of Wisconsin, Madison, Wisconsin 53706.
Brian S
http://orcid.org/0000-0002-8774-9377
BS
Yandell
Department of Epidemiology and Biostatistics, University of California, San Francisco, California 94107.
Śaunak
http://orcid.org/0000-0003-4519-6361
Ś
Sen
Department of Biochemistry, University of Wisconsin, Madison, Wisconsin 53706.
Alan D
AD
Attie
D000818:Animals; D002874:Chromosome Mapping; D019295:Computational Biology / Q000379:methods; D015870:Gene Expression; D020869:Gene Expression Profiling; D055106:Genome-Wide Association Study; D023281:Genomics / Q000379:methods; D005838:Genotype; D008126:Lod Score; D051379:Mice; D010641:Phenotype; D040641:Quantitative Trait Loci; D059467:Transcriptome
eQTL; genetical genomics; microarrays; mislabeling errors; quality control