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Calibrated prediction intervals for polygenic scores across diverse contexts
Kangcheng Hou1, Ziqi Xu2, Yi Ding1
1Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, USA.
Medrxiv : the Preprint Server for Health Sciences
|August 7, 2023
Summary
Polygenic scores (PGS) show variable accuracy across different contexts like age and sex. Adjusting prediction intervals for these contexts ensures reliable genomic predictions for everyone.
Area of Science:
- Genomics
- Biostatistics
- Personalized Medicine
Background:
- Polygenic scores (PGS) are widely used for genomic prediction in diverse fields.
- Existing methods often overlook how demographic and environmental contexts affect PGS accuracy.
Approach:
- Analyzed large biobank data (All of Us, UK Biobank) to assess PGS performance variability.
- Developed a joint framework to model the impact of multiple contexts on PGS accuracy.
- Introduced context-specific trait prediction intervals to improve calibration.
Key Points:
- PGS accuracy varies significantly across contexts such as age, sex, income, and genetic ancestry.
- Context-specific adjustments are crucial for accurate and well-calibrated genomic predictions.
- Prediction interval adjustments range from 10% (diastolic blood pressure) to 80% (waist circumference).
Conclusions:
- A comprehensive approach accounting for context is essential for equitable utilization of PGS.
- Future study designs and data collection should prioritize detailed contextual information.
- This work enables more reliable PGS-based trait predictions across diverse populations.
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