Data-driven prioritization of mouse strains for improved preclinical modeling of rare and common disease

Abstract

Choosing an appropriate mouse genetic background is a persistent challenge for successful translation of preclinical disease modeling. We present Strain Recommender, a genomic framework that prioritizes inbred mouse strains as relatively vulnerable or resilient to a disease state using disease-associated gene signatures and strain-specific transcriptome predictions. The method represents disease states as weighted gene scores, ranks 657 strains based on resemblance to the disease state, and estimates uncertainty via a permutation-derived false positive rate (FPR). In a prospective validation of connective tissue disorder predictions, vulnerable and resilient Collaborative Cross strains showed significantly different cardiovascular abnormalities. In a global retrospective validation predicting previously reported strain background effects, Strain Recommender achieved [≥] 90% sensitivity for 86.6% of diseases with 94.4% mean sensitivity (95% CI: 94.0-94.8%) across 5,890 diseases, including 92.3% (95% CI: 91.6-93.0%) for 2,598 rare diseases, demonstrating its potential to improve the validity of mouse models of human disease.