Variable prediction accuracy of polygenic scores within an ancestry group
Mostafavi, Hakhamanesh et al. (2020), Variable prediction accuracy of polygenic scores within an ancestry group, Dryad, Dataset, https://doi.org/10.5061/dryad.66t1g1jxs
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.
This repository contains summary statistics for all association tests (including GWAS and effect re-estimations for sets of pre-ascertained SNPs) that were performed in this study.
The directory "gwas_by_sample_characteristics" stores data corresponding to Figures 1, 2, and Appendix-figures 1-5,13-15, and Appendix-table 2.
The directory "standard_vs_sibling_gwas" stores data corresponding to Figure 3, and Appendix-figures 11, 12, 16.
Additional README files can be found within each directory.
National Institute of General Medical Sciences, Award: GM121372
National Human Genome Research Institute, Award: HG008140
Robert Wood Johnson Foundation, Award: 84337817
Simons Foundation, Award: 633313