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, the prediction accuracy of polygenic scores depends on characteristics such as the age or sex composition of the individuals in which the GWAS and the prediction were conducted, and on the GWAS study design. Our findings highlight both the complexities of interpreting polygenic scores and underappreciated obstacles to their broad use.
%0 Journal Article
%1 mostafavi2019variable
%A Mostafavi, Hakhamanesh
%A Harpak, Arbel
%A Conley, Dalton
%A Pritchard, Jonathan K
%A Przeworski, Molly
%D 2019
%I Cold Spring Harbor Laboratory
%J bioRxiv
%K GWAS UK_biobank human_genetics polygenic_scores polygenic_traits
%R 10.1101/629949
%T Variable prediction accuracy of polygenic scores within an ancestry group
%U https://www.biorxiv.org/content/early/2019/05/07/629949
%X 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, the prediction accuracy of polygenic scores depends on characteristics such as the age or sex composition of the individuals in which the GWAS and the prediction were conducted, and on the GWAS study design. Our findings highlight both the complexities of interpreting polygenic scores and underappreciated obstacles to their broad use.
@article{mostafavi2019variable,
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, the prediction accuracy of polygenic scores depends on characteristics such as the age or sex composition of the individuals in which the GWAS and the prediction were conducted, and on the GWAS study design. Our findings highlight both the complexities of interpreting polygenic scores and underappreciated obstacles to their broad use.},
added-at = {2019-05-17T22:54:57.000+0200},
author = {Mostafavi, Hakhamanesh and Harpak, Arbel and Conley, Dalton and Pritchard, Jonathan K and Przeworski, Molly},
biburl = {https://www.bibsonomy.org/bibtex/29fd749daf1752d7a32a332ca0f156557/peter.ralph},
doi = {10.1101/629949},
elocation-id = {629949},
eprint = {https://www.biorxiv.org/content/early/2019/05/07/629949.full.pdf},
interhash = {85e495c0bcfa84ddf233d233bcc43090},
intrahash = {9fd749daf1752d7a32a332ca0f156557},
journal = {bioRxiv},
keywords = {GWAS UK_biobank human_genetics polygenic_scores polygenic_traits},
publisher = {Cold Spring Harbor Laboratory},
timestamp = {2019-05-17T22:54:57.000+0200},
title = {Variable prediction accuracy of polygenic scores within an ancestry group},
url = {https://www.biorxiv.org/content/early/2019/05/07/629949},
year = 2019
}