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Published on: July 27, 2021
Non-Parametric Ancestry Adjustment for Polygenic Scores
Daniel Mas Montserrat1, Miriam Barrabes1, Carlos D Bustamante1
1Galatea Bio, Miami, FL.
Medrxiv : the Preprint Server for Health Sciences
|June 29, 2026
Summary
Polygenic risk scores (PRS) show ancestry-related biases, causing inaccurate health predictions for non-European individuals. New methods using neighborhood techniques improve PRS accuracy across diverse ancestries.
Area of Science:
- Genetics
- Bioinformatics
- Population Genetics
Background:
- Polygenic risk scores (PRS) trained on European data exhibit ancestry-related biases.
- These biases lead to inaccurate predictions for non-European and admixed populations.
- Existing ancestry adjustment methods are suboptimal for diverse genetic backgrounds.
Purpose of the Study:
- To theoretically characterize ancestry-dependent biases in PRS.
- To develop novel, non-parametric methods for accurate PRS adjustments.
- To improve PRS performance in underrepresented populations.
Main Methods:
- Theoretical analysis of PRS ancestry biases.
- Development of non-parametric neighborhood-based adjustment techniques.
- Empirical validation using UK Biobank data.
Main Results:
- Demonstrated effectiveness of proposed methods in reducing ancestry-related PRS shifts.
- Achieved more accurate PRS predictions for diverse populations.
- Provided statistical consistency guarantees for the novel methods.
Conclusions:
- Novel non-parametric methods significantly improve PRS accuracy across ancestries.
- These advancements address critical limitations in current PRS applications.
- The proposed techniques offer a path towards equitable genomic medicine.
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