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Genome-Wide Association, Polygenic Risk Scores, and Machine Learning for Chronic Post-Surgical Pain Risk
Alexander Peres1, Ron Unger2, Eitan Mangoubi3
1The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat Gan, Israel; Department of Anesthesiology, Montefiore Medical Center, Albert Einstein College of Medicine, The Bronx, New York.
The Journal of Pain
|August 7, 2026
Summary
Chronic post-surgical pain has a modest genetic component. This study identified over 220 genetic variants and developed a polygenic risk score that, combined with clinical factors, improves prediction for post-surgical pain.
Area of Science:
- Genetics
- Pain Research
- Surgical Outcomes
Background:
- Chronic post-surgical pain is a common and challenging complication.
- Genetic factors influence pain but large-scale studies are limited.
- Understanding genetic contributions is crucial for risk prediction.
Purpose of the Study:
- Identify genetic variants associated with chronic post-surgical pain.
- Develop polygenic risk scores for pain prediction.
- Integrate genetic and clinical data for enhanced risk stratification.
Main Methods:
- Utilized UK Biobank data (47,836 participants).
- Conducted genome-wide association study on 19 million variants.
- Developed and validated polygenic risk scores using logistic regression.
Main Results:
- Identified 220 variants at suggestive significance for post-surgical pain.
- Polygenic risk scores were significantly higher in cases versus controls.
- A combined clinical-genomic model improved prediction accuracy (AUC 0.639).
Conclusions:
- Chronic post-surgical pain may have a modest genetic contribution.
- Polygenic risk scores can enhance surgical risk stratification.
- Integrating genetic and clinical data offers a promising approach for personalized pain management.
