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Cross-ancestry genetic architecture and prediction for cholesterol traits

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Abstract

While cholesterol is essential, a high level of cholesterol is associated with the risk of cardiovascular diseases. Genome-wide association studies (GWASs) have proven successful in identifying genetic variants that are linked to cholesterol levels, predominantly in white European populations. However, the extent to which genetic effects on cholesterol vary across different ancestries remains largely unexplored. Here, we estimate cross-ancestry genetic correlation to address questions on how genetic effects are shared across ancestries. We find significant genetic heterogeneity between ancestries for cholesterol traits. Furthermore, we demonstrate that single nucleotide polymorphisms (SNPs) with concordant effects across ancestries for cholesterol are more frequently found in regulatory regions compared to other genomic regions. Indeed, the positive genetic covariance between ancestries is mostly driven by the effects of the concordant SNPs, whereas the genetic heterogeneity is attributed to the discordant SNPs. We also show that the predictive ability of the concordant SNPs is significantly higher than the discordant SNPs in the cross-ancestry polygenic prediction. The list of concordant SNPs for cholesterol is available in GWAS Catalog. These findings have relevance for the understanding of shared genetic architecture across ancestries, contributing to the development of clinical strategies for polygenic prediction of cholesterol in cross-ancestral settings.

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Data availability

The genotype and phenotype data of the UK Biobank can be accessed through procedures described on its webpage (https://www.ukbiobank.ac.uk/) and summary statistics of cholesterol traits can be obtained from Biobank Japan (BBJ) website (http://jenger.riken.jp/en/).

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Acknowledgements

We thank the staff and participants of the UK Biobank and Biobank Japan for their important contributions. Our reference number approved by UK Biobank is 14575. The analyses were performed using computational resources provided by the Australian Government through Gadi under the National Computational Merit Allocation Scheme (NCMAS), and HPCs (Statgen server) managed by UniSA IT.

Funding

This research is supported by the Australian Research Council (DP190100766).

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Authors and Affiliations

  1. Australian Centre for Precision Health, University of South Australia, Adelaide, SA, 5000, Australia

    Md. Moksedul Momin, Xuan Zhou, Elina Hyppönen, Beben Benyamin & S. Hong Lee

  2. UniSA Allied Health and Human Performance, University of South Australia, Adelaide, SA, 5000, Australia

    Md. Moksedul Momin, Xuan Zhou, Beben Benyamin & S. Hong Lee

  3. Department of Genetics and Animal Breeding, Faculty of Veterinary Medicine, Chattogram Veterinary and Animal Sciences University (CVASU), Khulshi, Chattogram, 4225, Bangladesh

    Md. Moksedul Momin

  4. South Australian Health and Medical Research Institute (SAHMRI), University of South Australia, Adelaide, SA, 5000, Australia

    Md. Moksedul Momin, Xuan Zhou, Elina Hyppönen, Beben Benyamin & S. Hong Lee

  5. UniSA Clinical and Health Sciences, University of South Australia, Adelaide, SA, Australia

    Elina Hyppönen

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Contributions

S.H.L. and M.M.M. conceived the idea. S.H.L. supervised the study. M.M.M performed quality control of the data and formal analysis. S.H.L and M.M.M wrote the first draft of the manuscript. X.Z., E.H and B.B. provided critical feedback, and key elements in interpreting the result. All authors provided critical feedback and suggestions. All the authors contributed to editing and approval of the final manuscript.

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Correspondence to Md. Moksedul Momin or S. Hong Lee.

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Momin, M.M., Zhou, X., Hyppönen, E. et al. Cross-ancestry genetic architecture and prediction for cholesterol traits. Hum. Genet. (2024). https://doi.org/10.1007/s00439-024-02660-7

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