Integrative GWAS and co-localisation analysis suggests novel genes associated with age-related multimorbidity

**Background:** Age-related multimorbidity, the co-occurrence of multiple chronic conditions in older adults, is a growing public health challenge. Traditional disease-specific approaches may not adequately address the shared underlying biological mechanisms, such as the Hallmarks of Ageing (e.g., inflammation, cellular senescence). Genetic studies have shown that age-related diseases are genetically correlated, suggesting common pathways. This study aimed to systematically identify genetic signals shared across multiple age-related traits and map them to potential drug targets.

**Methods:** The authors manually curated a list of 34 chronic age-related diseases and biomarkers (e.g., grip strength, gait speed) based on a review by Melzer et al. They retrieved 1,394 GWAS studies from Open Targets Genetics (version 210608) for these traits, requiring a sample size of at least 2,000. Lead variants with genome-wide significant associations (p < 5 × 10^-8) were expanded to tag variants using linkage disequilibrium (LD) expansion (r² ≥ 0.7) or fine-mapping (posterior probability > 0.1%). Overlapping genetic signals between traits were identified using co-localisation analysis (posterior probability of co-localisation > 0.8) when summary statistics were available, or by shared tag variants when not. These associations were clustered into independent genetic signals using graph-based community detection (Louvain method). Candidate causal genes were assigned using the Open Targets Genetics locus-to-gene (L2G) machine learning model (score ≥ 0.5). The resulting 995 protein-coding genes (TargetAge set) were compared to known ageing-related gene sets (GenAge, CellAge) and Gene Ontology terms representing the Hallmarks of Ageing. Tractability as drug targets was assessed using Open Targets Platform data, including clinical precedence, small molecule/antibody/PROTAC tractability, and chemical probe availability.

**Key Results:** The analysis identified 1,006 clusters of genetic associations involving more than one age-related trait. Of these, 796 (79%) contained at least one predicted causal gene (L2G ≥ 0.5), with 178 supported by co-localisation with pQTL or eQTL. The TargetAge set of 995 protein-coding genes included 290 (29%) annotated to at least one Hallmark of Ageing (p = 1.40 × 10^-6). The most highly represented hallmark was the immune inflammatory response (11% of genes). The TargetAge set significantly overlapped with GenAge (34 genes, p = 1.4 × 10^-5) and CellAge (34 genes, p = 2.0 × 10^-6). Eight genes were linked to three clusters (MPPED2, APOB, LDLR, TBX3, ZFHX3, BCL2L11, SMAD3, IRS1), and two genes (PPARG, LRMDA) to four clusters. Tractability assessment showed that 128 (13%) TargetAge genes are targets of approved or investigational drugs, 202 have experimental evidence of ligand binding (Discovery Precedence), and 324 are predicted tractable by antibody therapeutics (high confidence). High-quality chemical probes are available for 38 targets, 11 of which have no existing drugs in clinical development. The remaining 341 (34%) have no existing tractability evidence.

**Clinical Implications:** This study provides a systematic, genetics-driven approach to identify potential drug targets for age-related multimorbidity. The TargetAge gene set and associated genetic clusters are publicly available via an interactive web application (TargetAge). The findings highlight both drug repurposing opportunities (e.g., EDNRA for cardiovascular diseases) and novel targets (e.g., SEC16B for obesity and type 2 diabetes, FILIP1 for lean body mass and osteoarthritis). The enrichment of immune-inflammatory pathways supports the Geroscience hypothesis that targeting shared ageing mechanisms could benefit multiple conditions. However, the authors note limitations, including potential bias toward European populations and the need for further causal validation and clinical trials.