Causally-anchored multi-omic deep learning recovers exercise-responsive and ageing-causal genes from human physical activity

Physical activity is among the most robust epidemiological correlates of reduced mortality and multi-morbidity, yet the molecular mechanisms through which exercise exerts these effects in humans remain incompletely resolved. This study combines causally-anchored multi-omic Mendelian Randomisation (MR) with graph-based deep learning for gene prioritisation in human exercise-ageing biology, using accelerometer-derived vigorous physical activity (VPA) in the UK Biobank as exposure. Combining multi-omic MR across five molecular layers, it asks whether causal inference and deep learning can recover exercise-responsive and potentially ageing-causal genes that were independently identified in prior studies. Enrichment for experimentally exercise-responsive genes was undetectable in the raw MR signal (p = 0.97) yet was recovered by the graph model (p = 0.007, reproducible across all initialisations); and the convergence between VPA MR-anchored and ageing-causal genes (significant on its own at 1.6-fold; p = 0.023) was likewise recovered by the graph model where p-value and effect-size ranking could not. The model further reproduced established acute exercise-responsive immune and lipid-metabolic programmes, supporting its recovery of genuine signal. Extending the prioritised genes to formal causal testing, systematic cis-MR with colocalisation across the eight convergent genes and four ageing outcomes identified cathepsin F (CTSF) as causally associated with exceptional longevity, with concordant positive estimates in the protein and LD-clumped expression arms and colocalisation support at the protein level. The contribution is therefore twofold: a model-free, like-for-like convergence between exercise-anchored and ageing-causal genes; and a graph-based method that recovers this convergence, together with exercise-responsive biology, beyond the reach of per-gene MR ranking.