A Self-Adapting Polygenic Risk Score Model Improves Risk Prediction of Venous Thromboembolism in Han Chinese Cohorts.

Area of Science:

• Genetics
• Genomics
• Epidemiology

Background:

• Genome-wide association studies (GWAS) for Venous Thromboembolism (VTE) predominantly use European data, limiting the application of existing polygenic risk scores (PRS) in other populations.
• Genetic factors influencing VTE risk in Chinese populations remain underexplored, hindering targeted prevention efforts.

Purpose of the Study:

• To develop and validate an improved PRS model for VTE risk stratification in Chinese populations.
• To identify key single-nucleotide polymorphisms (SNPs) for accurate VTE risk prediction.

Main Methods:

• Comprehensive analysis of VTE-associated SNPs in a Chinese cohort.
• Development of a self-adapting PRS model using various algorithms and risk evaluation criteria.
• Independent testing of the PRS model on an independent Chinese population cohort.

Main Results:

• The self-adaptive PRS model, particularly one based on 44 SNPs, demonstrated strong performance (AUC 0.709), outperforming conventional PRS models (AUC 0.620-0.718).
• The developed PRS model proved to be an independent predictor of VTE risk, beyond clinical factors like age and smoking.
• The 44 SNPs-derived PRS model effectively discriminates high-risk subpopulations for VTE.

Conclusions:

• A 44-SNP-based self-adaptive PRS model is effective for VTE risk stratification in Chinese populations.
• This model holds promise for clinical utility in VTE screening and precision prevention programs.
• Further development could integrate this model into practical VTE screening initiatives for enhanced public health outcomes.