The incidence of ulcerative colitis (UC) has increased globally. As a complex disease, the genetic predisposition for UC could be estimated by the polygenic risk score (PRS), which aggregates the effects of a large number of genetic variants in a single quantity and shows promise in identifying individuals at higher lifetime risk of UC. Here, based on a cohort of 2869 UC cases and 2900 controls with genotype array datasets, we used PRSice-2 to calculate PRS, and systematically analyzed factors that could affect the power of PRS, including GWAS summary statistics, population stratification, and impact of variants. After leveraging a stepwise condition analysis, we eventually established the best PRS model, achieving an AUC of 0.713. Meanwhile, samples in the top 20% of the PRS distribution had a risk of UC more than ten times higher than samples in the lowest 20% (OR = 10.435, 95% CI 8.571–12.703). Our analyses demonstrated that including population-enriched, more disease-associated SNPs and using GWAS summary statistics from similar ethnic background can improve the power of PRS. Strictly following the principle of focusing on one population in all aspects of generating PRS can be a cost-effective way to apply genotype-array-derived PRS to practical risk estimation.

Background: Ulcerative colitis (UC) is a major form of inflammatory bowel disease affecting the gastrointestinal tract. Increasing evidence suggests UC is predisposed to co-occurring with other autoimmune diseases, yet its pathogenesis remains insufficiently understood. Large-scale biobank-based cross-trait genetic analyses may provide insights into the origins of UC and its comorbidities. Methods: Using the UK Biobank and Mount Sinai BioMe Biobank, we conducted genome-wide association studies (GWASs) in individuals of European ancestry. High-impact rare variants were aggregated for collapsing analysis. Genome-wide significant variants were tested in a phenome-wide association study (PheWAS) to explore UC comorbidities. Polygenic risk scores (PRSs) were derived from large-scale GWASs under different thresholds and functionalities, and the best-performing PRS was further applied in a PRS-based PheWAS. Genetic correlation between UC and highly associated traits was evaluated. Results: GWASs identified four genome-wide significant loci, including two novel variants (rs2314757, p = 4.82 × 10⁻¹¹, OR = 0.81; rs6869382, p = 2.48 × 10⁻⁸, OR = 0.83) and two previously reported UC-associated sites (rs4654925, p = 1.85 × 10⁻⁸, OR = 0.84; rs2836882, p = 1.23 × 10⁻¹¹, OR = 0.78) outside the HLA region. The optimal PRS, constructed with SNPs at p < 0.05, conferred an odds ratio of 5.86 (95% CI: 5.05–6.86) for UC in individuals with the highest versus lowest quintile. Both variant- and PRS-based PheWASs consistently highlighted type 1 diabetes (T1D) as the most significant comorbidity, confirmed by genetic correlation analysis. Conclusions: This study reveals novel loci contributing to UC and highlights comorbidities with shared genetic bases. UC PRSs demonstrated strong utility beyond risk prediction, effectively identifying UC-associated traits. A robust genetic correlation was established between UC and T1D.