Genome-Wide Cox Regression Analysis Identifies 134 Novel Risk Loci for Disability Development: The Canadian Longitudinal Study on Aging and UK Biobank
Bibliographic record
Abstract
BACKGROUND: Disability significantly affects the well-being of older adults and imposes substantial personal and social burdens. Although genetic effects play a role in disability, large-scale genome-wide association studies (GWAS) of disability development remain scarce. METHODS: We performed the first Cox proportional hazards GWAS on disability development on 8421 individuals aged more than 65 years from the Canadian Longitudinal Study on Aging (CLSA). Disability was defined as the inability to perform daily activities, as measured by the activities of daily living (ADL) scale. A polygenic hazard score (PHS) was developed and incorporated into the predictive model, along with demographic and environmental factors. RESULTS: The study observed a 16.28% incidence of disability over a mean follow-up duration of 4.64 years (SD = 1.95). The Cox-GWAS identified six genome-wide significant variants (P < 5E-08) and 134 independent single nucleotide polymorphisms (SNPs) with suggestive significance level (P < 1E - 05). Replication in the UK Biobank (UKB) confirmed that rs589819, rs56294014, and rs143714258 remained nominally significant and exhibited consistent effect directions. Post-GWAS analyses, including transcriptome-wide association studies TWAS, gene set, and tissue--enrichment analyses, revealed genetic pathways related to inflammation regulation, neurogenesis, and metabolic processes. Incorporating PHS with demographic and environmental factors improves the prediction performance in both CLSA and UKB. CONCLUSION: This study is among the first genome-wide Cox regression analyses to uncover novel genetic loci and biological pathways involved in disability development in older adults. These findings provide a foundation for predictive modeling and targeted prevention strategies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".