Opportunities and challenges in leveraging multiomics and biobanks for vision science

Emerging biobank resources allow large-scale integration of eye-specific phenotypes with clinical, genomic, and multiomic data. This convergence enables unprecedented opportunities to systematically dissect the genetic architecture, epidemiology, and mechanistic pathways of both rare monogenic and common polygenic diseases. The review aims to critically examine how contemporary data extraction, multiomics, and analytic methodologies are reshaping disease classification, genetic discovery, and translational research in ophthalmology, while highlighting the associated challenges in leveraging these advanced tools.

Recent literature demonstrates the utility of genome-wide and phenome-wide association studies, transcriptomic analyses, and artificial intelligence in uncovering novel risk loci, endophenotypes, and biomarkers relevant to eye diseases. Furthermore, advances in multiancestry sampling show substantial population-specific genetic variation, enriching disease models for conditions such as glaucoma and age-related macular degeneration. Finally. integrative approaches, including Mendelian randomization and enrichment analyses, are helping elucidate shared genetic architecture between ocular and systemic diseases, informing therapeutic target identification, and refining risk prediction models.

The convergence of biobank-derived multimodal data and sophisticated analytic techniques is catalyzing a path to personalized medicine in ophthalmology. For these approaches to fully translate into clinical practice ensuring scientifically robust and equitable outcomes, future research must address cohort diversity, mechanistic validation, and practical cost-effectiveness.