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Statistical uncertainty explains the poor agreement in polygenic scoring for type 2 diabetes
Ravi Mandla1,2, Xinzhe Li1,2, Zhuozheng Shi1,2
1Graduate Program in Genomics and Computational Biology, University of Pennsylvania.
Medrxiv : the Preprint Server for Health Sciences
|March 11, 2026
Summary
Polygenic scores (PGS) for type 2 diabetes (T2D) show poor agreement due to statistical uncertainty. Incorporating uncertainty estimates improves risk prediction accuracy and clinical utility for identifying high-risk individuals.
Area of Science:
- Genetics
- Medical Genetics
- Computational Biology
Background:
- Polygenic scores (PGS) are crucial for predicting genetic risk of diseases.
- Disagreement among different PGS for the same individual hinders clinical application.
- Type 2 diabetes (T2D) is a major focus for PGS development.
Purpose of the Study:
- To investigate the reasons behind poor agreement across T2D PGS.
- To develop an approach for selecting high-risk individuals that accounts for PGS uncertainty.
- To enhance the interpretability and clinical utility of PGS.
Main Methods:
- Statistical analysis of existing T2D PGS data.
- Development of individual-level uncertainty estimates for PGS.
- Comparison of risk prediction using point estimates versus uncertainty-aware methods.
Main Results:
- Statistical uncertainty fully explains the variability and poor agreement across T2D PGS.
- Individual-level uncertainty estimates from a single PGS account for cross-score discrepancies.
- An uncertainty-informed approach identifies high-risk individuals with greater confidence and higher T2D incidence.
Conclusions:
- The apparent disagreement in PGS is an artifact of statistical uncertainty, not inherent score differences.
- Incorporating uncertainty measures is essential for accurate and reliable PGS-based risk prediction.
- Addressing PGS uncertainty is key to overcoming implementation barriers in clinical genetics.
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