Disease-guided functional gene mapping across species reveals translational correspondences beyond sequence orthology

Abstract

Selecting the correct mouse gene to model a human disease phenotype is critical for translational research, yet sequence-based orthology fails when genes have been lost, duplicated, or functionally rewired between species. Here we present BRIDGE (Biological Rank Integration for Disease Gene Equivalence), a framework that identifies functional mouse equivalents of human disease genes without sequence input. BRIDGE integrates 3.37 million disease-gene associations, biological pathways, and Gene Ontology annotations into a unified heterogeneous graph (94,897 nodes, [~]8.3 million edges), encoded by a heterogeneous graph transformer with fused Gromov-Wasserstein alignment and multi-strategy reciprocal rank fusion. On two sequence-independent benchmarks, BRIDGE achieves Recall@5 of 61.8-66.7%, compared with 0.0-20.1% for Ensembl Compara. We validate BRIDGE through case studies including neutrophil pathway rewiring (CXCL8[->]Cxcl1/2/5), acute-phase divergence (CRP[->]Apcs), and immune checkpoint substitution (LILRB2[->]Pirb), and demonstrate complementarity with sequence methods in drug-translation analysis. Prospective validation of 30 novel predictions against three independent data modalities (tissue expression, cell-type expression, and phenotype concordance) shows that BRIDGE picks are favoured in 64 of 65 orthogonal tests (sign test P = 3.6 x 10-{superscript 1}) and significantly outperform all tested baselines including Ensembl Compara, BLAST RBH, and ESM-2. BRIDGE provides a benchmarked framework for functional cross-species gene mapping in disease-model design.