Cataract GWAS meta-analysis and multi-omics Mendelian randomisation inform disease mechanisms and candidate target...

Cataract is a highly prevalent cause of visual impairment and blindness worldwide. Although genome-wide association studies (GWAS) have mapped multiple cataract risk loci, further work is required towards translating these signals and integrating multi-omics evidence for preventing or delaying age-related cataract progression.

A senile cataract GWAS was first conducted in All of Us, followed by ancestry-specific GWAS meta-analyses in European-ancestry (161,812 cases and 1,099,738 controls) and East Asian ancestry (66,679 cases and 521,995 controls). To prioritise candidate targets, we performed Mendelian randomisation (MR) and colocalisation using cis-expression quantitative trait loci (cis-eQTL) instruments from eQTLGen. We then conducted a plasma proteome-wide cis-pQTL MR scan with colocalisation using UKB-PPP and deCODE for European analyses, and cis-pQTL data from the China Kadoorie Biobank for East Asian analyses.

We identified 78 and 4 independent loci for cataract in Europeans and East Asians, respectively, including 8 and 3 putatively unreported loci. By integrating the meta-GWAS with cis-eQTL MR, we prioritised 16 genes, including MTMR3, NT5DC2, and EFNA1, with eQTL-GWAS colocalisation further supporting MTMR3 and NT5DC2. Proteome-wide cis-pQTL MR prioritised 32 proteins for senile cataract in Europeans, and colocalisation refined these to EFNA1 and RALY. In East Asians, MR highlighted APOE and ALDH2 as candidate targets. We also identified colocalised pleiotropic signals shared between cataract and related traits, including myopia, hypertension, and type 2 diabetes, highlighting candidate genes such as TCF7L2, BMP4, CDKAL1, PAX4, and ALDH2.

These findings advance cross-ancestry understanding of cataract genetic architecture and prioritise genetically supported candidates.

This work was supported by the Joint Funds for the Innovation of Science and Technology, Fujian Joint Innovation Fund in Science and Technology Project (Grant No. 2025Y9437).