TY - JOUR
T1 - The predictive capacity of polygenic risk scores for disease risk is only moderately influenced by imputation panels tailored to the target population
AU - Levi, Hagai
AU - Elkon, Ran
AU - Shamir, Ron
N1 - Publisher Copyright:
© 2024 Oxford University Press. All rights reserved.
PY - 2024/2/1
Y1 - 2024/2/1
N2 - Motivation: Polygenic risk scores (PRSs) predict individuals’ genetic risk of developing complex diseases. They summarize the effect of many variants discovered in genome-wide association studies (GWASs). However, to date, large GWASs exist primarily for the European population and the quality of PRS prediction declines when applied to other ethnicities. Genetic profiling of individuals in the discovery set (on which the GWAS was performed) and target set (on which the PRS is applied) is typically done by SNP arrays that genotype a fraction of common SNPs. Therefore, a key step in GWAS analysis and PRS calculation is imputing untyped SNPs using a panel of fully sequenced individuals. The imputation results depend on the ethnic composition of the imputation panel. Imputing genotypes with a panel of individuals of the same ethnicity as the genotyped individuals typically improves imputation accuracy. However, there has been no systematic investigation into the influence of the ethnic composition of imputation panels on the accuracy of PRS predictions when applied to ethnic groups that differ from the population used in the GWAS. Results: We estimated the effect of imputation of the target set on prediction accuracy of PRS when the discovery and the target sets come from different ethnic groups. We analyzed binary phenotypes on ethnically distinct sets from the UK Biobank and other resources. We generated ethnically homogenous panels, imputed the target sets, and generated PRSs. Then, we assessed the prediction accuracy obtained from each imputation panel. Our analysis indicates that using an imputation panel matched to the ethnicity of the target population yields only a marginal improvement and only under specific conditions. Availability and implementation: The source code used for executing the analyses is this paper is available at https://github.com/Shamir-Lab/ PRS-imputation-panels.
AB - Motivation: Polygenic risk scores (PRSs) predict individuals’ genetic risk of developing complex diseases. They summarize the effect of many variants discovered in genome-wide association studies (GWASs). However, to date, large GWASs exist primarily for the European population and the quality of PRS prediction declines when applied to other ethnicities. Genetic profiling of individuals in the discovery set (on which the GWAS was performed) and target set (on which the PRS is applied) is typically done by SNP arrays that genotype a fraction of common SNPs. Therefore, a key step in GWAS analysis and PRS calculation is imputing untyped SNPs using a panel of fully sequenced individuals. The imputation results depend on the ethnic composition of the imputation panel. Imputing genotypes with a panel of individuals of the same ethnicity as the genotyped individuals typically improves imputation accuracy. However, there has been no systematic investigation into the influence of the ethnic composition of imputation panels on the accuracy of PRS predictions when applied to ethnic groups that differ from the population used in the GWAS. Results: We estimated the effect of imputation of the target set on prediction accuracy of PRS when the discovery and the target sets come from different ethnic groups. We analyzed binary phenotypes on ethnically distinct sets from the UK Biobank and other resources. We generated ethnically homogenous panels, imputed the target sets, and generated PRSs. Then, we assessed the prediction accuracy obtained from each imputation panel. Our analysis indicates that using an imputation panel matched to the ethnicity of the target population yields only a marginal improvement and only under specific conditions. Availability and implementation: The source code used for executing the analyses is this paper is available at https://github.com/Shamir-Lab/ PRS-imputation-panels.
UR - http://www.scopus.com/inward/record.url?scp=85184665993&partnerID=8YFLogxK
U2 - 10.1093/bioinformatics/btae036
DO - 10.1093/bioinformatics/btae036
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C2 - 38265251
AN - SCOPUS:85184665993
SN - 1367-4803
VL - 40
JO - Bioinformatics
JF - Bioinformatics
IS - 2
M1 - btae036
ER -