TY - JOUR
T1 - Variable prediction accuracy of polygenic scores within an ancestry group
AU - Mostafavi, Hakhamanesh
AU - Harpak, Arbel
AU - Agarwal, Ipsita
AU - Conley, Dalton
AU - Pritchard, Jonathan K.
AU - Przeworski, Molly
N1 - Publisher Copyright:
© 2020, eLife Sciences Publications Ltd. All rights reserved.
PY - 2020/1
Y1 - 2020/1
N2 - 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.
AB - 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.
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U2 - 10.7554/eLife.48376
DO - 10.7554/eLife.48376
M3 - Article
C2 - 31999256
AN - SCOPUS:85079481021
SN - 2050-084X
VL - 9
JO - eLife
JF - eLife
M1 - e48376
ER -