Trans-ancestry polygenic models for the prediction of LDL blood levels: an analysis of the United Kingdom Biobank and Taiwan Biobank

Emadeldin Hassanin; Ko-Han Lee; Tzung-Chien Hsieh; Rana Aldisi; Yi-Lun Lee; Dheeraj Bobbili; Peter Krawitz; Patrick May; Chien-Yu Chen; Carlo Maj

doi:10.3389/fgene.2023.1286561

Trans-ancestry polygenic models for the prediction of LDL blood levels: an analysis of the United Kingdom Biobank and Taiwan Biobank

Front Genet. 2023 Nov 23:14:1286561. doi: 10.3389/fgene.2023.1286561. eCollection 2023.

Authors

Emadeldin Hassanin^#^{1

2}, Ko-Han Lee^#³, Tzung-Chien Hsieh², Rana Aldisi², Yi-Lun Lee³, Dheeraj Bobbili¹, Peter Krawitz², Patrick May¹, Chien-Yu Chen^#^{3

4

5

6}, Carlo Maj^#⁷

Affiliations

¹ Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Sur-Alzette, Luxembourg.
² Institute for Genomic Statistics and Bioinformatics, University of Bonn, Bonn, Germany.
³ Taiwan AI Labs and Foundation, Taipei, Taiwan.
⁴ Center for Computational and Systems Biology, National Taiwan University, Taipei, Taiwan.
⁵ Department of Biomechatronics Engineering, National Taiwan University, Taipei, Taiwan.
⁶ Center for Advanced Computing and Imaging in Biomedicine, Natinal Taiwan University, Taipei, Taiwan.
⁷ Centre for Human Genetics, University of Marburg, Marburg, Germany.

^# Contributed equally.

Abstract

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.

Keywords: East Asia; LDL cholesterol; Taiwan Biobank; United Kingdom Biobank; polygenic risk score.

Grants and funding

The authors declare that financial support was received for the research, authorship, and/or publication of this article. This work was financially supported by the “Center for Advanced Computing and Imaging in Biomedicine (NTU-112L900701)” from the Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan. PM received funding from the Luxembourg National Research Fund (FNR) INTER grant “ProtectMove” (INTER/DFG/19/14429377). EH, DB, and PM were supported by FNR INTER INTER/DFG/21/16394868. This work was supported by the National Science and Technology Council (NSTC), Taiwan (MOST 109-2221-E-002-162-MY3, MOST 111-2221-E-002-66-MY3, NSTC 112-2221-E -002-184-MY3).