Variable prediction accuracy of polygenic scores within an ancestry group

Hakhamanesh Mostafavi, Arbel Harpak, Ipsita Agarwal, Dalton Conley, Jonathan K. Pritchard, Molly Przeworski

Research output: Contribution to journalArticle

5 Scopus citations

Abstract

Fields as diverse as human genetics and sociology are increasingly using polygenic scores based on genome-wide association studies (GWAS) for phenotypic prediction. However, recent work has shown that polygenic scores have limited portability across groups of different genetic ancestries, restricting the contexts in which they can be used reliably and potentially creating serious inequities in future clinical applications. Using the UK Biobank data, we demonstrate that even within a single ancestry group (i.e., when there are negligible differences in linkage disequilibrium or in causal alleles frequencies), the prediction accuracy of polygenic scores can depend on characteristics such as the socio-economic status, age or sex of the individuals in which the GWAS and the prediction were conducted, as well as on the GWAS design. Our findings highlight both the complexities of interpreting polygenic scores and underappreciated obstacles to their broad use.

Original languageEnglish (US)
Article numbere48376
JournaleLife
Volume9
DOIs
StatePublished - Jan 2020

All Science Journal Classification (ASJC) codes

  • Neuroscience(all)
  • Immunology and Microbiology(all)
  • Biochemistry, Genetics and Molecular Biology(all)

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    Mostafavi, H., Harpak, A., Agarwal, I., Conley, D., Pritchard, J. K., & Przeworski, M. (2020). Variable prediction accuracy of polygenic scores within an ancestry group. eLife, 9, [e48376]. https://doi.org/10.7554/eLife.48376