ArticleNature communications2023
Optimal strategies for learning multi-ancestry polygenic scores vary across traits.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A multiancestry polygenic risk score improves stratification in patients with hypertrophic cardiomyopathy.Nature cardiovascular research · 2026Pooled it
- All of Us diversity and scale yield context-dependent improvements in polygenic prediction.Nature genetics · 2026Article
- Evidence standards for multi-ancestry polygenic prediction.Genome biology · 2026Review
- Explicitly modeling genetic ancestry to improve polygenic prediction accuracy for height in a large, admixed cohort of US Latinos: Findings from HCHS/SOL.HGG advances · 2026Article
- S4-multi: Enhancing polygenic score prediction in ancestrally diverse populations.HGG advances · 2026Article
- Methodological opportunities in genomic data analysis to advance health equity.Nature reviews. Genetics · 2025Review
- Clinical use of polygenic scores in type 2 diabetes: challenges and possibilities.Diabetologia · 2025Review
- Polygenic Score Complements Family History and Lynch Syndrome Genes for Predicting Colorectal Cancer Risk.JCO precision oncology · 2025Article
- All of Us diversity and scale improve polygenic prediction contextually with greatest improvements for under-represented populations.bioRxiv : the preprint server for biology · 2025Article
- Spotlight on amino acid changing mutations in the JAK-STAT pathway: from disease-specific mutation to general mutation databases.Scientific reports · 2025Article
- Using pre-training and interaction modeling for ancestry-specific disease prediction using multiomics data from the UK Biobank.PloS one · 2025Article
- Benchmarking multi-ancestry prostate cancer polygenic risk scores in a real-world cohort.PLoS computational biology · 2024Article
- Polygenic prediction across populations is influenced by ancestry, genetic architecture, and methodology.Cell genomics · 2023Article
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Authors and funding
4 authors.
Funding
Abstract
Polygenic scores (PGSs) are individual-level measures that aggregate the genome-wide genetic predisposition to a given trait. As PGS have predominantly been developed using European-ancestry samples, trait prediction using such European ancestry-derived PGS is less accurate in non-European ancestry individuals. Although there has been recent progress in combining multiple PGS trained on distinct populations, the problem of how to maximize performance given a multiple-ancestry cohort is largely unexplored. Here, we investigate the effect of sample size and ancestry composition on PGS performance for fifteen traits in UK Biobank. For some traits, PGS estimated using a relatively small African-ancestry training set outperformed, on an African-ancestry test set, PGS estimated using a much larger European-ancestry only training set. We observe similar, but not identical, results when considering other minority-ancestry groups within UK Biobank. Our results emphasise the importance of targeted data collection from underrepresented groups in order to address existing disparities in PGS performance.
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