ArticleBioinformatics (Oxford, England)2024
The predictive capacity of polygenic risk scores for disease risk is only moderately influenced by imputation panels tailored to the target population.
Article in Bioinformatics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- PRANA: A Deep Learning Method for Adapting Polygenic Risk Scores to Diverse Ethnic Groups.medRxiv : the preprint server for health sciences · 2026Article
- Empirical evaluation of analytic validity of polygenic scores.Genome medicine · 2026Article
- Establishing Best Practices for Clinical GWAS: Tackling Imputation and Data Quality Challenges.International journal of molecular sciences · 2025Review
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3 authors.
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Abstract
motivationPolygenic 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.
resultsWe 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.
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