ArticleG3 (Bethesda, Md.)2015
Identification and Correction of Sample Mix-Ups in Expression Genetic Data: A Case Study.
Article in G3 (Bethesda, Md.), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
12 citing papers in PubMed, 28 citations in OpenAlex.
- Genetic pathways regulating the longitudinal acquisition of cocaine self-administration in a panel of inbred and recombinant inbred mice.Cell reports · 2023Article
- The effects of sequencing depth on the assembly of coding and noncoding transcripts in the human genome.BMC genomics · 2022Article
- Integration of single-cell transcriptomes and chromatin landscapes reveals regulatory programs driving pharyngeal organ development.Nature communications · 2022Article
- DRAMS: A tool to detect and re-align mixed-up samples for integrative studies of multi-omics data.PLoS computational biology · 2020Article
- Assessment of kinship detection using RNA-seq data.Nucleic acids research · 2019Article
- Cleaning Genotype Data from Diversity Outbred Mice.G3 (Bethesda, Md.) · 2019Article
- Article
- NGSCheckMate: software for validating sample identity in next-generation sequencing studies within and across data types.Nucleic acids research · 2017Article
- Linkage disequilibrium matches forensic genetic records to disjoint genomic marker sets.Proceedings of the National Academy of Sciences of the United States of America · 2017Article
- reGenotyper: Detecting mislabeled samples in genetic data.PloS one · 2017Article
- Identification of sample annotation errors in gene expression datasets.Archives of toxicology · 2015Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.