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Trans-ancestry polygenic models for the prediction of LDL blood levels: an analysis of the United Kingdom Biobank and Taiwan Biobank.
Hassanin, Emadeldin; Lee, Ko-Han; Hsieh, Tzung-Chien; Aldisi, Rana; Lee, Yi-Lun; Bobbili, Dheeraj; Krawitz, Peter; May, Patrick; Chen, Chien-Yu; Maj, Carlo.
Afiliação
  • Hassanin E; Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Sur-Alzette, Luxembourg.
  • Lee KH; Institute for Genomic Statistics and Bioinformatics, University of Bonn, Bonn, Germany.
  • Hsieh TC; Taiwan AI Labs and Foundation, Taipei, Taiwan.
  • Aldisi R; Institute for Genomic Statistics and Bioinformatics, University of Bonn, Bonn, Germany.
  • Lee YL; Institute for Genomic Statistics and Bioinformatics, University of Bonn, Bonn, Germany.
  • Bobbili D; Taiwan AI Labs and Foundation, Taipei, Taiwan.
  • Krawitz P; Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Sur-Alzette, Luxembourg.
  • May P; Institute for Genomic Statistics and Bioinformatics, University of Bonn, Bonn, Germany.
  • Chen CY; Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Sur-Alzette, Luxembourg.
  • Maj C; Taiwan AI Labs and Foundation, Taipei, Taiwan.
Front Genet ; 14: 1286561, 2023.
Article em En | MEDLINE | ID: mdl-38075701
Polygenic risk score (PRS) predictions often show bias toward the population of available genome-wide association studies (GWASs), which is typically of European ancestry. This study aimed to assess the performance differences of ancestry-specific PRS and test the implementation of multi-ancestry PRS to enhance the generalizability of low-density lipoprotein (LDL) cholesterol predictions in the East Asian (EAS) population. In this study, we computed ancestry-specific and multi-ancestry PRSs for LDL using data obtained from the Global Lipid Genetics Consortium, while accounting for population-specific linkage disequilibrium patterns using the PRS-CSx method in the United Kingdom Biobank dataset (UKB, n = 423,596) and Taiwan Biobank dataset (TWB, n = 68,978). Population-specific PRSs were able to predict LDL levels better within the target population, whereas multi-ancestry PRSs were more generalizable. In the TWB dataset, covariate-adjusted R 2 values were 9.3% for ancestry-specific PRS, 6.7% for multi-ancestry PRS, and 4.5% for European-specific PRS. Similar trends (8.6%, 7.8%, and 6.2%) were observed in the smaller EAS population of the UKB (n = 1,480). Consistent with R 2 values, PRS stratification in EAS regions (TWB) effectively captured a heterogenous variability in LDL blood cholesterol levels across PRS strata. The mean difference in LDL levels between the lowest and highest EAS-specific PRS (EAS_PRS) deciles was 0.82, compared to 0.59 for European-specific PRS (EUR_PRS) and 0.76 for multi-ancestry PRS. Notably, the mean LDL values in the top decile of multi-ancestry PRS were comparable to those of EAS_PRS (3.543 vs. 3.541, p = 0.86). Our analysis of the PRS prediction model for LDL cholesterol further supports the issue of PRS generalizability across populations. Our targeted analysis of the EAS population revealed that integrating non-European genotyping data with a powerful European-based GWAS can enhance the generalizability of LDL PRS.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Front Genet Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Luxemburgo

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Front Genet Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Luxemburgo