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Bridging the diversity gap: Analytical and study design considerations for improving the accuracy of trans-ancestry genetic prediction.

HGG Advances 4:100214 (2023)
DOI PMC
Creative Commons Lizenzvertrag
Open Access Gold as soon as Publ. Version/Full Text is submitted to ZB.
Genetic prediction of common complex disease risk is an essential component of precision medicine. Currently, genome-wide association studies (GWASs) are mostly composed of European-ancestry samples and resulting polygenic scores (PGSs) have been shown to poorly transfer to other ancestries partly due to heterogeneity of allelic effects between populations. Fixed-effects (FETA) and random-effects (RETA) trans-ancestry meta-analyses do not model such ancestry-related heterogeneity, while ancestry-specific (AS) scores may suffer from low power due to low sample sizes. In contrast, trans-ancestry meta-regression (TAMR) builds ancestry-aware PGS that account for more complex trans-ancestry architectures. Here, we examine the predictive performance of these four PGSs under multiple genetic architectures and ancestry configurations. We show that the predictive performance of FETA and RETA is strongly affected by cross-ancestry genetic heterogeneity, while AS PGS performance decreases in under-represented target populations. TAMR PGS is also impacted by heterogeneity but maintains good prediction performance in most situations, especially in ancestry-diverse scenarios. In simulations of human complex traits, TAMR scores currently explain 25% more phenotypic variance than AS in triglyceride levels and 33% more phenotypic variance than FETA in type 2 diabetes in most non-European populations. Importantly, a high proportion of non-European-ancestry individuals is needed to reach prediction levels that are comparable in those populations to the one observed in European-ancestry studies. Our results highlight the need to rebalance the ancestral composition of GWAS to enable accurate prediction in non-European-ancestry groups, and demonstrate the relevance of meta-regression approaches for compensating some of the current population biases in GWAS.
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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Gwas ; Complex Traits ; Polygenic Scores ; Trans-ancestry; Polygenic Risk Scores; Metaanalysis; Association; Discovery; Insights; History
ISSN (print) / ISBN 2666-2477
e-ISSN 2666-2477
Quellenangaben Volume: 4, Issue: 3, Pages: , Article Number: 100214 Supplement: ,
Publisher Cell Press
Publishing Place Radarweg 29, 1043 Nx Amsterdam, Netherlands
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of Translational Genomics (ITG)
Grants European Union