Aim: Age-related macular degeneration (AMD) is a leading cause of vision loss in older adults, with genetic factors strongly influencing onset and progression. This study aimed to analyse GWAS data to identify AMD-associated genetic variants and explore their biological and regulatory significance.
Methodology: Publicly available GWAS datasets were analysed using Gene Ontology (GO), KEGG, and Reactome databases to identify functional pathways. MicroRNA enrichment analysis assessed regulatory control, whilst MetaboAnalyst linked genetic variation to metabolic alterations. Unsupervised machine learning approaches, including principal component analysis (PCA), K-means, and hierarchical clustering, identified biological patterns among single nucleotide polymorphisms (SNPs).
Results: Strong associations were observed at known susceptibility loci, particularly CFH, with additional genes including INHBB, GLI2, and TYR implicated in immune regulation and retinal development. Enrichment analyses highlighted complement activation, lipid metabolism, and oxidative stress as key biological processes. MicroRNA analysis revealed regulators influencing inflammatory and immune pathways, whilst clustering identified three distinct SNP groups supported by hierarchical patterns.
Interpretation: By integrating genetic, functional and regulatory data, this study advances molecular understanding of AMD, identifying complement dysregulation, immune dysfunction, and metabolic imbalance as central contributors, alongside novel candidate biomarkers and therapeutic targets with potential implications for precision medicine in AMD management.
Key words: Age-related macular degeneration, Functional enrichment, Genome wide association study, Genetic variants, Molecular pathways
Loya Geeta Anusha, N. Uday Kumar, E. V. Ravikanth et al.· Journal of environmental bio...· 0 citations
