Analysis of polygenic risk score usage and performance in diverse human populations

By L. Duncan, Hanyang Shen, B. Gelaye, J. Meijsen, K. Ressler, Michal Feldman, Roseann E. Peterson, B. Domingue. 2019

885 citations recorded by the source corpus.

A historical tendency to use European ancestry samples hinders medical genetics research, including the use of polygenic scores, which are individual-level metrics of genetic risk. We analyze the first decade of polygenic scoring studies (2008–2017, inclusive), and find that 67% of studies included exclusively European ancestry participants and another 19% included only East Asian ancestry participants. Only 3.8% of studies were among cohorts of African, Hispanic, or Indigenous peoples. We find that predictive performance of European ancestry-derived polygenic scores is lower in non-European ancestry samples (e.g. African ancestry samples: t = −5.97, df = 24, p = 3.7 × 10−6), and we demonstrate the effects of methodological choices in polygenic score distributions for worldwide populations. These findings highlight the need for improved treatment of linkage disequilibrium and variant frequencies when applying polygenic scoring to cohorts of non-European ancestry, and bolster the rationale for large-scale GWAS in diverse human populations. Predominant participation of European-ancestry individuals in genetic studies has hindered the better understanding of genetic risk in non-European ancestry individuals. Here, Duncan et al. quantify polygenic risk score use and performance in worldwide populations.

Subfields: Experimental and Cognitive Psychology, Genetics, Computational Biology & Bioinformatics

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Citations in the corpus

Underspecification Presents Challenges for Credibility in Modern Machine Learning (2020, background): “, 2018), but face difficulties when applied to new patient populations (Martin et al., 2017; Duncan et al., 2019; Berg et al., 2019).”
Algorithm fairness in artificial intelligence for medicine and healthcare (2023, background): “Additionally, cross-ancestry association studies that include populations from divergent ancestries have uncovered new diseased loci 174 .”
Genome-wide association studies in ancestrally diverse populations: opportunities, methods, pitfalls, and recommendations. (2019, positive): “Genetic risk prediction attenuates with increasing divergence between the discovery and target populations, indicating that polygenic risk scores (PRSs) based on Eurocentric GWASs are not equally predictive when applied to non-European populations (Duncan et al., 2019; Martin et al., 2019).”