The landscape of polygenic score prediction accuracy and transportability across the phenome in ancestrally diverse populations
- 1University of Colorado Anschutz
Abstract
Polygenic scores (PGS) are increasingly common in human genetics research, yet their performance and portability vary substantially across traits and populations. We systematically evaluate prediction accuracy and cross-ancestry portability for 3,994 published PGSs across the phenome in the ancestrally diverse CCPM biobank. We identify widespread trait-specific and cross-trait associations, demonstrating both the utility and breadth of existing polygenic predictors. However, predictive effects frequently differ across ancestry groups, highlighting persistent challenges for portability that threaten equity in downstream applications. We further find that accuracy and portability depend strongly on whether the PGS is continuous or thresholded. Finally, through hierarchical mixed modeling, we identify score-development, genome features, and cohort characteristics associated with improved PGS accuracy and portability. These results highlight key factors supporting future development of robust and portable scores for research and clinical translation.
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BibTeX
@misc{lin2026landscape,
title = {{The landscape of polygenic score prediction accuracy and transportability across the phenome in ancestrally diverse populations}},
author = {M. Lin and C. H. Arehart and S. N. K. Walker and N. Rafaels and K. R. Crooks and M. Fisher and S. Chavan and H. Smith and N. Pozdeyev and L. A. Lange and M. Pividori and J. A. Shortt and R. M. Layer and Colorado Center for Personalized Medicine and A. E. Hendricks and S. Raghavan and C. R. Gignoux},
howpublished = {medRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.08.26365087},
url = {https://www.medrxiv.org/content/10.64898/2026.10.08.26365087v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - The landscape of polygenic score prediction accuracy and transportability across the phenome in ancestrally diverse populations
AU - M. Lin
AU - C. H. Arehart
AU - S. N. K. Walker
AU - N. Rafaels
AU - K. R. Crooks
AU - M. Fisher
AU - S. Chavan
AU - H. Smith
AU - N. Pozdeyev
AU - L. A. Lange
AU - M. Pividori
AU - J. A. Shortt
AU - R. M. Layer
AU - Colorado Center for Personalized Medicine
AU - A. E. Hendricks
AU - S. Raghavan
AU - C. R. Gignoux
PY - 2026
DA - 2026/10/08
PB - medRxiv
DO - 10.64898/2026.10.08.26365087
UR - https://www.medrxiv.org/content/10.64898/2026.10.08.26365087v1
AB - Polygenic scores (PGS) are increasingly common in human genetics research, yet their performance and portability vary substantially across traits and populations. We systematically evaluate prediction accuracy and cross-ancestry portability for 3,994 published PGSs across the phenome in the ancestrally diverse CCPM biobank. We identify widespread trait-specific and cross-trait associations, demonstrating both the utility and breadth of existing polygenic predictors. However, predictive effects frequently differ across ancestry groups, highlighting persistent challenges for portability that threaten equity in downstream applications. We further find that accuracy and portability depend strongly on whether the PGS is continuous or thresholded. Finally, through hierarchical mixed modeling, we identify score-development, genome features, and cohort characteristics associated with improved PGS accuracy and portability. These results highlight key factors supporting future development of robust and portable scores for research and clinical translation.
ER -