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Peer-reviewedCC BY-NC-NDAlzheimer s & Dementia

Cross‐ancestry polygenic risk scores enhance Alzheimer's disease risk prediction in multiethnic cohorts

  • Michael C Okorie1,2,3
  • Caroline Jonson4,5,6,2,3
  • Alexis P. Oddi6,2,3
  • Patricia A. Castruita6,2,3
  • Brian Fulton‐Howard7,8,9
  • Kristine S. Yaffe10,1,2,11,12,13
  • Jennifer S. Yokoyama14,6,2,3
  • Chinedu Theresa Udeh‐Momoh15,16,17
  • Shea J. Andrews1,2,13
  • for the Alzheimer's Disease Sequencing Project and the Healthy Aging Brain Study—Health Disparities
17 affiliations
  1. 1Department of Psychiatry and Behavioral Sciences University of California San Francisco California USA
  2. 2Fein Memory and Aging Center, University of California San Francisco California USA
  3. 3University of California, San Francisco
  4. 4Center for Alzheimer's and Related Dementias National Institutes of Health Bethesda Maryland USA
  5. 5DataTecnica LLC Washington DC District of Columbia USA
  6. 6Department of Radiology and Biomedical Imaging University of California San Francisco California USA
  7. 7Department of Neuroscience Icahn School of Medicine at Mount Sinai New York New York USA
  8. 8Ronald M Loeb Center for Alzheimer's disease Icahn School of Medicine at Mount Sinai New York New York USA
  9. 9Icahn School of Medicine at Mount Sinai
  10. 10Department of Epidemiology and Biostatistics University of California San Francisco California USA
  11. 11San Francisco Veterans Affairs Health Care System San Francisco California USA
  12. 12The Center for Population Brain Health University of California San Francisco California USA
  13. 13University of California San Francisco
  14. 14Department of Neurology and Weill Institute for Neurosciences University of California San Francisco California USA
  15. 15Brain and Mind Institute Aga Khan University Nairobi Kenya
  16. 16Department of Epidemiology and Prevention Wake Forest University School of Medicine Winston‐Salem North Carolina USA
  17. 17Wake Forest University School of Medicine
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Abstract

INTRODUCTION Genome-wide association studies (GWAS) have identified 80+ genetic loci associated with Alzheimer’s disease (AD), enabling the development of polygenic risk scores (PRS). However, the predictive accuracy of PRS in diverse populations remains low. Here, we evaluated the predictive accuracy of single-, multi-, and cross-ancestry AD-PRS models across multi-ancestral populations. METHODS We used AD GWAS summary statistics from European, African, Amerindian, and East Asian populations to construct AD-PRS for each target population. Model performance was assessed by estimating odds ratios, R 2 , and AUC. RESULTS The cross-ancestry Bayesian PRS model demonstrated the highest predictive performance in non-European populations. It was significantly associated with poorer cognitive function, lower Aβ 42 CSF levels, and the most severe category of Aβ and tau neuropathological burden, as well as a clinical AD latent variable in a multi-ancestral validation cohort. DISCUSSION Inclusive genetic datasets and cross-ancestry PRS models are needed to enhance the transportability of AD-PRS across multi-ancestral populations. Research in context: Systematic review: Using diverse GWAS datasets to construct AD-PRS is a promising yet underexplored approach to improve risk prediction accuracy across different populations. Integrating diverse base GWAS datasets and evaluating various PRS models can enhance model performance, but such approaches have not yet been widely applied to multi-ancestral cohorts to measure AD risks or abnormalities in biomarkers. Interpretation: Incorporation of ancestrally diverse base GWAS datasets enhanced the association between PRS and AD risk across multiple populations. Leveraging both these diverse discovery datasets and a Bayesian framework markedly improved model performance, extending its potential clinical applicability beyond AD case-control classification to the prediction of biomarker abnormalities. Future directions: Future research should prioritize the validation of cross-ancestry PRS models in larger and more heterogeneous populations, alongside systematic benchmarking against an expanding repertoire of PRS methodologies. Clinical implementation of AD-PRS will require rigorous validation in large, diverse as well as community-based populations to ensure reproducibility and generalizability, thereby enhancing its translational relevance. Highlights Single-ancestry PRS is only predictive in participants of European populations. Cross-ancestry PRS improves risk predictions in non-European participants. Cross-ancestry PRS is associated with abnormal Aβ and tau pathology and cognitive decline Cross-ancestry PRS is associated with the AD latent variable in a multi-ancestral cohort

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Cite

BibTeXRIS
BibTeX
@article{okorie2026crossancestry,
  title = {{Cross‐ancestry polygenic risk scores enhance Alzheimer's disease risk prediction in multiethnic cohorts}},
  author = {Michael C Okorie and Caroline Jonson and Alexis P. Oddi and Patricia A. Castruita and Brian Fulton‐Howard and Kristine S. Yaffe and Jennifer S. Yokoyama and Chinedu Theresa Udeh‐Momoh and Shea J. Andrews and for the Alzheimer's Disease Sequencing Project and the Healthy Aging Brain Study—Health Disparities},
  journal = {Alzheimer s \& Dementia},
  year = {2026},
  month = jul,
  doi = {10.1002/alz.71529},
  url = {https://doi.org/10.1002/alz.71529},
  note = {CC BY-NC-ND},
}
RIS
TY  - JOUR
TI  - Cross‐ancestry polygenic risk scores enhance Alzheimer's disease risk prediction in multiethnic cohorts
AU  - Michael C Okorie
AU  - Caroline Jonson
AU  - Alexis P. Oddi
AU  - Patricia A. Castruita
AU  - Brian Fulton‐Howard
AU  - Kristine S. Yaffe
AU  - Jennifer S. Yokoyama
AU  - Chinedu Theresa Udeh‐Momoh
AU  - Shea J. Andrews
AU  - for the Alzheimer's Disease Sequencing Project and the Healthy Aging Brain Study—Health Disparities
PY  - 2026
DA  - 2026/07/28
JO  - Alzheimer s & Dementia
DO  - 10.1002/alz.71529
UR  - https://doi.org/10.1002/alz.71529
AB  - INTRODUCTION Genome-wide association studies (GWAS) have identified 80+ genetic loci associated with Alzheimer’s disease (AD), enabling the development of polygenic risk scores (PRS). However, the predictive accuracy of PRS in diverse populations remains low. Here, we evaluated the predictive accuracy of single-, multi-, and cross-ancestry AD-PRS models across multi-ancestral populations. METHODS We used AD GWAS summary statistics from European, African, Amerindian, and East Asian populations to construct AD-PRS for each target population. Model performance was assessed by estimating odds ratios, R 2 , and AUC. RESULTS The cross-ancestry Bayesian PRS model demonstrated the highest predictive performance in non-European populations. It was significantly associated with poorer cognitive function, lower Aβ 42 CSF levels, and the most severe category of Aβ and tau neuropathological burden, as well as a clinical AD latent variable in a multi-ancestral validation cohort. DISCUSSION Inclusive genetic datasets and cross-ancestry PRS models are needed to enhance the transportability of AD-PRS across multi-ancestral populations. Research in context: Systematic review: Using diverse GWAS datasets to construct AD-PRS is a promising yet underexplored approach to improve risk prediction accuracy across different populations. Integrating diverse base GWAS datasets and evaluating various PRS models can enhance model performance, but such approaches have not yet been widely applied to multi-ancestral cohorts to measure AD risks or abnormalities in biomarkers. Interpretation: Incorporation of ancestrally diverse base GWAS datasets enhanced the association between PRS and AD risk across multiple populations. Leveraging both these diverse discovery datasets and a Bayesian framework markedly improved model performance, extending its potential clinical applicability beyond AD case-control classification to the prediction of biomarker abnormalities. Future directions: Future research should prioritize the validation of cross-ancestry PRS models in larger and more heterogeneous populations, alongside systematic benchmarking against an expanding repertoire of PRS methodologies. Clinical implementation of AD-PRS will require rigorous validation in large, diverse as well as community-based populations to ensure reproducibility and generalizability, thereby enhancing its translational relevance. Highlights Single-ancestry PRS is only predictive in participants of European populations. Cross-ancestry PRS improves risk predictions in non-European participants. Cross-ancestry PRS is associated with abnormal Aβ and tau pathology and cognitive decline Cross-ancestry PRS is associated with the AD latent variable in a multi-ancestral cohort
SN  - 1552-5260
SN  - 1552-5279
ER  -