The Wrong Animal: Genetic Mislabeling in Laboratory Mouse Research and the Promise of AI-Assisted Quality Control
DOI:
https://doi.org/10.63501/qzsx8v40Keywords:
mouse strain authentication, genetic mislabeling, preclinical reproducibility, genotype verification, laboratory mouse, artificial intelligence, research integrity, MiniMUGA, cell-line authentication,, NIH quality controlReferences
1. Pardo-Manuel de Villena F, Amos-Landgraf JM, Bell TA, et al. Improve genetic quality control to increase rigor and reproducibility of mouse research. Science. 2026;392:698–700. https://doi.org/10.1126/science.aec3177
2. Maxmen A. Genetic survey exposes flaws in widely used mouse models. Nature. 2026 May 15. https://www.nature.com/articles/d41586-026-01534-4
3. Rawle DJ, Le TT, Dumenil T, Bishop C, Yan K, Nakayama E, Bird PI, Suhrbier A. Widespread discrepancy in Nnt genotypes and genetic backgrounds complicates granzyme A and other knockout mouse studies. eLife. 2022;11:e70207. https://doi.org/10.7554/eLife.70207
4. Wilson JAC, Prow NA, Schroder WA, et al. RNA-Seq analysis of chikungunya virus infection and identification of granzyme A as a major promoter of arthritic inflammation. PLOS Pathog. 2017;13(2):e1006155. https://doi.org/10.1371/journal.ppat.1006155
5. Begley CG, Ellis LM. Drug development: Raise standards for preclinical cancer research. Nature. 2012;483:531–533. https://doi.org/10.1038/483531a
6. Prinz F, Schlange T, Asadullah K. Believe it or not: how much can we rely on published data on potential drug targets? Nat Rev Drug Discov. 2011;10:712. https://doi.org/10.1038/nrd3439-c1
7. International Cell Line Authentication Committee (ICLAC). Register of Misidentified Cell Lines. Version 12. 2023. https://iclac.org/databases/cross-contaminations/
8. NIH National Center for Advancing Translational Sciences. Cell Line Authentication. https://ncats.nih.gov/research/research-activities/cell-line-authentication
9. Blanchard MW, Bell TA, Buis JM, et al. The updated Mouse Universal Genotyping Array bioinformatic pipeline improves genetic QC in laboratory mice. G3 (Bethesda). 2023;13(4):jkad036. https://doi.org/10.1093/g3journal/jkad036
10. Pardo-Manuel de Villena F, Replication data for: Improve genetic quality control standards to increase rigor and reproducibility of mouse research. UNC Dataverse. 2025. https://doi.org/10.15139/S3/MLJJBK
11. VanDenBerg KR, Oravecz-Wilson K, Krolikowski L, Hill V, Reddy P, Freeman ZT. Impact of automated genotyping and increased breeding oversight on overall mouse breeding colony productivity. Front Physiol. 2022;13:925784. https://doi.org/10.3389/fphys.2022.925784
12. Hatherley R, Rankin N. Incidences of problematic cell lines are lower in papers that use RRIDs to identify cell lines. PLoS ONE. 2019;14(2):e0205418. https://doi.org/10.1371/journal.pone.0205418
Downloads
Published
Issue
Section
License
This article is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, sharing, adaptation, distribution, and reproduction in any medium or format, provided appropriate credit is given to the author(s) and the source. To view a copy of this license, visit: https://creativecommons.org/licenses/by/4.0