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A Polygenic Risk Analysis for Identifying Ulcerative Colitis Patients with European Ancestry.

Ling Liu1, Yiming Wu2, Yizhou Li3

  • 1College of Chemistry, Sichuan University, Chengdu 610065, China.

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Summary

Developing a polygenic risk score (PRS) for ulcerative colitis (UC) can identify high-risk individuals. Optimizing PRS models using specific genetic data improves accuracy for UC risk prediction.

Keywords:
polygenic risk scorepopulation stratificationsingle-nucleotide polymorphismsulcerative colitis

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Area of Science:

  • Genetics
  • Epidemiology
  • Bioinformatics

Background:

  • Ulcerative colitis (UC) incidence is rising globally.
  • Genetic predisposition plays a key role in UC development.
  • Polygenic risk scores (PRS) offer a method to quantify genetic susceptibility.

Purpose of the Study:

  • To develop and optimize a polygenic risk score (PRS) for ulcerative colitis (UC).
  • To identify factors influencing PRS accuracy and effectiveness.
  • To assess the utility of PRS in identifying individuals at high risk for UC.

Main Methods:

  • Utilized PRSice-2 to calculate PRS in a cohort of 2869 UC cases and 2900 controls.
  • Analyzed factors affecting PRS power, including GWAS summary statistics and population stratification.
  • Employed stepwise conditional analysis to establish the optimal PRS model.

Main Results:

  • Achieved an Area Under the Curve (AUC) of 0.713 with the optimized PRS model.
  • Individuals in the top 20% PRS had over tenfold increased UC risk (OR = 10.435).
  • PRS power improved with population-enriched, disease-associated SNPs and ethnically matched GWAS data.

Conclusions:

  • Optimized PRS models can effectively estimate UC genetic predisposition.
  • Using population-specific data enhances PRS accuracy for UC risk prediction.
  • A standardized, population-focused approach to PRS generation is cost-effective for practical risk assessment.