GENOMIC INSIGHTS INTO CHRONIC DISEASE SUSCEPTIBILITY: IMPLICATIONS FOR PERSONALISED MEDICINE

Abstract

The complex interplay of genetic, metabolic and immune-related processes influences chronic diseases. Even though genomic research has found susceptibility loci to specific conditions, there have been fewer studies that combine variant level, pathway-level, and cumulative risk using a single framework to examine chronic disease susceptibility. This study aimed to investigate genomic variation associated with susceptibility patterns and examine its implications for personalised medicine using type 2 diabetes mellitus as a model chronic disease. A secondary analysis was conducted using SNP genotype data from the GEO dataset GSE226084. Following quality control, principal component analysis was applied for dimensionality reduction, and K-means clustering was used to derive genomic susceptibility groups. Logistic regression identified associated SNPs, which were subsequently annotated, aggregated at the gene level, and evaluated through pathway enrichment analysis. A weighted genetic risk score was calculated to assess cumulative genetic burden across clusters. Two distinct genomic clusters were identified, comprising 63 and 243 individuals. Among 3,733 tested SNPs, 871 remained significant after false discovery rate correction. Key loci included HLA-DPA1, ETV6, TRIM15, TRIM26, and TCF7L2, with notable signal concentration on chromosome 6. Enrichment analysis revealed pathways related to immune regulation, inflammatory response, and cellular signaling. Genetic risk scores differed markedly between clusters, with one group exhibiting consistently higher cumulative genetic burden. These findings demonstrate that genomic susceptibility is organised into biologically distinct profiles defined by coordinated variant, gene, pathway, and cumulative risk signals. Integrating these layers provides a practical basis for risk stratification and supports the application of genomics in personalised medicine.