Phenotype-stratified computational convergence of osteoarthritis loci across human knee cell programs.

Genome-wide association studies of osteoarthritis use related but non-identical endpoints, including knee osteoarthritis, broader hip-and-knee osteoarthritis, and total knee replacement. Whether these endpoint-specific genetic signals map to distinct human joint cell programs remains unclear. We developed and applied a phenotype-stratified computational convergence framework that integrates osteoarthritis genetic loci with a unified human knee single-cell/single-nucleus atlas. Three predefined locus groups were analyzed: knee osteoarthritis-enriched, total knee replacement-enriched, and shared broader-osteoarthritis loci. Candidate effectors were assigned using a refined locus-to-gene evidence layer and scored against knee cell programs using gene-wise program z-scores. Convergence was evaluated using one-sided upper-tail gene-set permutation testing, with Benjamini-Hochberg correction across the complete family of three phenotype groups × eight cell programs. Knee osteoarthritis-enriched loci showed their strongest discovery-atlas alignment with a fibro-inflammatory synovial program (mean program z = 1.107; nominal permutation p = 0.008; BH q = 0.192), whereas total knee replacement-enriched loci aligned most strongly with a cartilage ossification-like structural program (mean program z = 0.597; nominal permutation p = 0.018; BH q = 0.216). No primary convergence test remained significant after correction across the 24-test family. In donor-aware cartilage analysis, the cartilage ossification-like program ranked first in 28 of 31 cartilage sample units. External public-data stress testing identified transferability boundaries: synovial marker-level signals were partly directionally consistent, whereas small candidate-effector modules were not consistently reproduced across external bulk or single-cell datasets. These results provide phenotype-linked computational prioritization of human knee cell programs while defining clear statistical and biological limits on their interpretation.