Genetic Profiling of Autoimmune Diseases and Exploring Clusters Through Polygenic Risk Score Analysis Using Cohort Data from the UK Biobank

Autoimmune diseases result from immune responses against self-antigens but exhibit marked phenotypic diversity shaped by genetic and environmental factors. Genome-wide association studies (GWAS) have identified susceptibility loci that inform polygenic scores (PGS) for risk prediction. This study integrates phenotypic and genetic data from the UK Biobank(UKB) to characterize disease overlap, genetic heterogeneity, and shared biological mechanisms across autoimmune conditions. Comorbidity patterns were further assessed using patient records from UKB and the TriNetX(TNX). Phenotypic data from 502,371 UKB participants were used to evaluate diagnostic overlap, with a subset of 104,544 individuals analyzed for PGS distributions. Significant variants were identified using genome-wide thresholds, allele frequency, and predicted impact, and shared genes were subsequently mapped to pathways using Hallmark gene sets. Comorbidity across rare and common autoimmune diseases was assessed in the UKB and TNX using ICD-10 codes, focusing on White individuals (71,069,654 in TNX; 502,371 in UKB). Odds ratios for 15 diseases were estimated, and cross-cohort comparisons evaluated reproducibility and cohort-specific differences. PGS analyses revealed both shared and distinct genetic architectures, indicating partial genetic overlap and supporting poly-autoimmunity. Integration of common, rare and impactful variants identified both known and novel gene associations, while pathway analysis highlighted systemic and tissue-specific immune dysregulation. Cross-dataset comparisons confirmed consistent comorbidity patterns but underscored the impact of dataset-specific factors, emphasizing the need for standardized approaches in autoimmune disease research.