Statistical Methods for Polygenic Risk Scores
A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Technologies and Resources for Genetics".
Deadline for manuscript submissions: 25 June 2025 | Viewed by 124
Special Issue Editor
Interests: bioinformatics; optimization; statistical genomics; survival analysis
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Polygenic risk scores (PRSs) are a powerful tool in genetic research, providing a quantitative measure of an individual's genetic predisposition to certain diseases or traits by aggregating the effects of multiple genetic variants. Despite their potential, PRSs face several challenges. Most PRS models are developed using datasets from individuals of European ancestry, which limits their predictive power when applied to underrepresented populations due to genetic and environmental heterogeneity. This disparity exacerbates health inequities, as minority groups may not benefit equally from advancements in genetic risk prediction. Additionally, constructing PRS for these populations is hindered by small sample sizes and high-dimensionality issues, making statistical analysis and model validation more complex. To address these challenges, innovative approaches are needed to transfer knowledge from well-studied populations to underrepresented groups while accounting for heterogeneity to ensure equitable and accurate risk predictions across diverse populations. This Special Issue will highlight cutting-edge statistical methods for PRS and explore potential future directions for improvement.
Dr. Kevin He
Guest Editor
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Keywords
- bioinformatics
- computational biology
- data integration
- polygenic risk scores
- precision medicine
- risk prediction
- statistical genomics
- transfer learning
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