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Polygenic Scores for Height in Admixed Populations
Bárbara D Bitarello1, Iain Mathieson1
1Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104 barbara.bitarello@pennmedicine.upenn.edu mathi@pennmedicine.upenn.edu.
G3 (Bethesda, Md.)
|September 4, 2020
Summary
Polygenic risk scores (PRS) show lower predictive accuracy in non-European ancestry groups. Improving PRS for diverse populations requires larger, ancestry-specific genetic studies.
Area of Science:
- Genetics
- Personalized Medicine
- Population Genetics
Background:
- Polygenic risk scores (PRS) are valuable tools for predicting disease risk and quantitative traits.
- A significant limitation of current PRS is their reduced predictive power in non-European ancestry populations due to a lack of diverse GWAS data.
- The precise reasons for this performance disparity remain incompletely understood.
Purpose of the Study:
- To investigate the performance of height PRS in admixed African and European ancestry cohorts.
- To evaluate ancestry-related differences in PRS predictive accuracy while controlling for environmental and cohort effects.
- To identify factors contributing to the reduced predictive power of PRS in non-European ancestries.
Main Methods:
- Utilized height PRS in admixed African and European ancestry cohorts.
- Assessed the linear relationship between European ancestry proportion and PRS predictive accuracy.
- Analyzed the contributions of recombination rate, allele frequency differences, and effect size variations to PRS performance disparities.
Main Results:
- Height PRS predictive accuracy demonstrated a linear increase with European ancestry proportion.
- European ancestry segments within admixed genomes partially explained the observed PRS accuracy.
- Recombination rate, allele frequency differences, and varying marginal effect sizes collectively impacted PRS predictive power, but no single factor fully accounted for the decrease.
- A combined PRS approach using ancestry-specific effect sizes showed potential for improved prediction in admixed individuals.
Conclusions:
- Ancestry composition significantly influences PRS predictive accuracy, with higher European ancestry correlating with better performance.
- Multiple genetic factors contribute to the underperformance of PRS in non-European ancestries.
- Developing effective PRS for diverse populations necessitates larger, ancestry-specific discovery cohorts and refined prediction models.
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