Cross-Ancestry Polygenic Risk Scores Enhance Alzheimer’s Disease Risk Prediction in Multiethnic Cohorts
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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
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Single-ancestry PRS is only predictive in participants of European populations.
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Cross-ancestry PRS improves risk predictions in non-European participants.
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Cross-ancestry PRS is associated with abnormal Aβ and tau pathology and cognitive decline
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Cross-ancestry PRS is associated with the AD latent variable in a multi-ancestral cohort
