CoChat
Sign Up
Cited by 66Genetic Associations and EpidemiologyRace, Genetics, and SocietyGenetic Mapping and Diversity in Plants and Animals

The DOI matches its Crossref record; its reference list is not available.

Transferability of genetic loci and polygenic scores for cardiometabolic traits in British Pakistani and Bangladeshi individuals

Open access
Huang, Qin Qin, Sallah, Neneh, Dunca, Diana, Trivedi, Bhavi, Hunt, Karen A., Hodgson, Sam, Lambert, Samuel A., Arciero, Elena, Wright, John, Griffiths, Chris, Trembath, Richard C., Hemingway, Harry, Inouye, Michael, Finer, Sarah, van Heel, David A., Lumbers, R. Thomas, Martin, Hilary C., Kuchenbaecker, Karoline·2022·Nature Communications
DOI 10.1038/s41467-022-32095-5PMC PMC9363492

Published version of a preprint

Open this paper in CoChat — free accountOpen PDF

Abstract

Individuals with South Asian ancestry have a higher risk of heart disease than other groups but have been largely excluded from genetic research. Using data from 22,000 British Pakistani and Bangladeshi individuals with linked electronic health records from the Genes & Health cohort, we conducted genome-wide association studies of coronary artery disease and its key risk factors. Using power-adjusted transferability ratios, we found evidence for transferability for the majority of cardiometabolic loci powered to replicate. The performance of polygenic scores was high for lipids and blood pressure, but lower for BMI and coronary artery disease. Adding a polygenic score for coronary artery disease to clinical risk factors showed significant improvement in reclassification. In Mendelian randomisation using transferable loci as instruments, our findings were consistent with results in European-ancestry individuals. Taken together, trait-specific transferability of trait loci between populations is an important consideration with implications for risk prediction and causal inference.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/s41467-022-32095-5

Cites

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • The PAT ratio for EduYears for EUR to EAS was 0.62 (95/153).

    “Based on the credible sets, the PAT ratio for EduYears for EUR to EAS was 0.62 (number of observed transferable loci divided by number of expected transferable loci in the EAS population = 95/153).”

    From Shared genetic architectures of educational attainment in East Asian and European populations (2024) · cites this paper as [32]

    The Results section (‘High transferability of cardiometabolic loci’; page unavailable) defines the PAT ratio using credible sets, but reports 0.62 for coronary artery disease in British Pakistani and Bangladeshi individuals. Searches for EduYears, EUR-to-EAS transferability, and 95/153 found no passage addressing the claimed result. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

No citation lists for this paper

No citation lists for this paper yet — checked again daily.

References 71 · Cited by 66

Research works printed but not linked: 42

  1. Barnett, A. H. et al. Type 2 diabetes and cardiovascular risk in the UK south Asian community. Diabetologia 49, 2234–2246 (2006). · DOI
  2. Gurdasani, D., Barroso, I., Zeggini, E. & Sandhu, M. S. Genomics of disease risk in globally diverse populations. Nat. Rev. Genet. 20, 520–535 (2019). · DOI
  3. Majara, L. et al. Low generalizability of polygenic scores in African populations due to genetic and environmental diversity. Cold Spring Harbor Lab. https://doi.org/10.1101/2021.01.12.426453 (2021). · DOI
  4. Wang, Y. et al. Theoretical and empirical quantification of the accuracy of polygenic scores in ancestry divergent populations. Nat. Commun. 11, 3865 (2020). · DOI · Paper page
  5. Riveros-Mckay, F. et al. An integrated polygenic tool substantially enhances coronary artery disease prediction. Circ. Genom. Precis. Med. https://doi.org/10.1161/CIRCGEN.120.003304 (2021). · DOI
  6. Sun, L. et al. Polygenic risk scores in cardiovascular risk prediction: A cohort study and modelling analyses. PLoS Med. 18, e1003498 (2021). · DOI
  7. Weale, M. E. et al. Validation of an integrated risk tool, including polygenic risk score, for atherosclerotic cardiovascular disease in multiple ethnicities and ancestries. Am. J. Cardiol. https://doi.org/10.1016/j.amjcard.2021.02.032 (2021). · DOI
  8. Mostafavi, H. et al. Variable prediction accuracy of polygenic scores within an ancestry group. Elife 9, e48376 (2020). · DOI · Paper page
  9. Carter, A. R. et al. Educational attainment as a modifier for the effect of polygenic scores for cardiovascular risk factors: cross-sectional and prospective analysis of UK Biobank. Int. J. Epidemiol. 51, 885–897 (2022). · DOI
  10. Finer, S. et al. Cohort Profile: East London Genes & Health (ELGH), a community-based population genomics and health study in British Bangladeshi and British Pakistani people. Int. J. Epidemiol. 49, 20–21i (2020). · DOI
  11. Gelernter, J. et al. Genome-wide Association Study of maximum habitual alcohol intake in >140,000 U.S. European and African American veterans yields novel risk loci. Biol. Psychiatry 86, 365–376 (2019). · DOI
  12. Wang, M. et al. Validation of a genome-wide polygenic score for coronary artery disease in South Asians. J. Am. Coll. Cardiol. 76, 703–714 (2020). · DOI
  13. Inouye, M. et al. Genomic risk prediction of coronary artery disease in 480,000 adults: implications for primary prevention. J. Am. Coll. Cardiol. 72, 1883–1893 (2018). · DOI
  14. Homer, K. et al. Statin prescribing for primary prevention of cardiovascular disease: a cross-sectional, observational study. Br. J. Gen. Pract. 65, e538–e544 (2015). · DOI
  15. Márquez-Luna, C. & Loh, P.-R. Multiethnic polygenic risk scores improve risk prediction in diverse populations. Genet. Epidemiol. 41, 811–823 (2017). · DOI
  16. Braun, T. R. et al. A replication study of GWAS-derived lipid genes in Asian Indians: the chromosomal region 11q23.3 harbors loci contributing to triglycerides. PLoS ONE 7, e37056 (2012). · DOI
  17. Shahid, S. U., Shabana, N. A., Rehman, A. & Humphries, S. GWAS implicated risk variants in different genes contribute additively to increase the risk of coronary artery disease (CAD) in the Pakistani subjects. Lipids Health Dis. 17, 89 (2018). · DOI
  18. Chambers, J. C. et al. Common genetic variation near MC4R is associated with waist circumference and insulin resistance. Nat. Genet. 40, 716–718 (2008). · DOI
  19. Harshfield, E. L. et al. Genome-wide analysis of blood lipid metabolites in over 5000 South Asians reveals biological insights at cardiometabolic disease loci. BMC Med. 19, 232 (2021). · DOI
  20. Selvaraj, M. S. et al. Whole genome sequence analysis of blood lipid levels in >66,000 individuals. Preprint at bioRxiv https://doi.org/10.1101/2021.10.11.463514 (2021). · DOI
  21. Rashid, S. et al. Elevated cholesteryl ester transfer protein (CETP) activity, a major determinant of the atherogenic dyslipidemia, and atherosclerotic cardiovascular disease in South Asians. Eur. J. Prev. Cardiol. 22, 468–477 (2015). · DOI
  22. Sniderman, A. D., Bhopal, R., Prabhakaran, D., Sarrafzadegan, N. & Tchernof, A. Why might South Asians be so susceptible to central obesity and its atherogenic consequences? The adipose tissue overflow hypothesis. Int. J. Epidemiol. 36, 220–225 (2007). · DOI · Paper page
  23. Shah, A. D. et al. Less favorable body composition and adipokines in South Asians compared with other US ethnic groups: results from the MASALA and MESA studies. Int. J. Obes. 40, 639–645 (2016). · DOI
  24. Kuchenbaecker, K. et al. The transferability of lipid loci across African, Asian and European cohorts. Nat. Commun. 10, 4330 (2019). · DOI
  25. Gola, D. et al. Population bias in polygenic risk prediction models for coronary artery disease. Circ. Genom. Precis. Med. 13, e002932 (2020). · DOI
  26. Weissbrod, O. et al. Leveraging fine-mapping and multipopulation training data to improve cross-population polygenic risk scores. Nat. Genet. 54, 450–458 (2022). · DOI
  27. Zaitlen, N., Paşaniuc, B., Gur, T., Ziv, E. & Halperin, E. Leveraging genetic variability across populations for the identification of causal variants. Am. J. Hum. Genet. 86, 23–33 (2010). · DOI
  28. Elliott, J. et al. Predictive accuracy of a polygenic risk score–enhanced prediction model vs. a clinical risk score for coronary artery disease. JAMA 323, 636–645 (2020). · DOI
  29. Hippisley-Cox, J., Coupland, C., & Brindle, P. Development and validation of QRISK3 risk prediction algorithms to estimate future risk of cardiovascular disease: prospective cohort study. BMJ 357, j2099 (2017). · DOI · Paper page
  30. Das, S. et al. Next-generation genotype imputation service and methods. Nat. Genet. 48, 1284–1287 (2016). · DOI · Paper page
  31. GenomeAsia100K Consortium. The GenomeAsia 100K Project enables genetic discoveries across Asia. Nature 576, 106–111 (2019). · DOI
  32. Winkler, T. W. et al. Quality control and conduct of genome-wide association meta-analyses. Nat. Protoc. 9, 1192–1212 (2014). · DOI
  33. Stanaway, I. B. et al. The eMERGE genotype set of 83,717 subjects imputed to ~40 million variants genome wide and association with the herpes zoster medical record phenotype. Genet. Epidemiol. 43, 63–81 (2019).
  34. Li, Y., Sperrin, M. & van Staa, T. R package ‘QRISK3’: an unofficial research purposed implementation of ClinRisk’s QRISK3 algorithm into R. F1000Research 8, 2139 (2019). · DOI
  35. Zhou, W. et al. Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies. Nat. Genet. 50, 1335–1341 (2018). · DOI · Paper page
  36. Yang, J., Lee, S. H., Goddard, M. E. & Visscher, P. M. GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88, 76–82 (2011). · DOI · Paper page
  37. Virani, S. S. et al. Heart Disease and Stroke Statistics—2020 update: a report from the American Heart Association. Circulation 141, e139–e596 (2020).
  38. Brown, B. C. Asian Genetic Epidemiology Network Type 2 Diabetes Consortium, Ye, C. J., Price, A. L. & Zaitlen, N. Transethnic genetic-correlation estimates from summary statistics. Am. J. Hum. Genet. 99, 76–88 (2016). · DOI · Paper page
  39. Lambert, S. A. et al. The Polygenic Score Catalog as an open database for reproducibility and systematic evaluation. Nat. Genet. https://doi.org/10.1038/s41588-021-00783-5 (2021). · DOI
  40. Hemani, G. et al. The MR-Base platform supports systematic causal inference across the human phenome. eLife 7, e34408 (2018). · DOI · Paper page
  41. Burgess, S., Bowden, J., Fall, T., Ingelsson, E. & Thompson, S. G. Sensitivity analyses for robust causal inference from Mendelian randomization analyses with multiple genetic variants. Epidemiology 28, 30–42 (2017). · DOI · Paper page
  42. Burgess, S. & Thompson, S. G. Multivariable Mendelian randomization: the use of pleiotropic genetic variants to estimate causal effects. Am. J. Epidemiol. 181, 251–260 (2015). · DOI · Paper page

Metadata

Authors
Huang, Q. Q., Sallah, N., Dunca, D., Trivedi, B., Hunt, K. A., Hodgson, S., Lambert, S. A., Arciero, E., Wright, J., Griffiths, C., Trembath, R. C., Hemingway, H., Inouye, M., Finer, S., van Heel, D. A., Lumbers, R. T., Martin, H. C., & Kuchenbaecker, K.
Affiliations
  • Wellcome Sanger Institute (GB)
  • University College London (GB)
  • Queen Mary University of London (GB)
  • University of Southampton (GB)
  • University of Cambridge (GB)
  • National Health Service (GB)
  • University of London (GB)
  • King's College London (GB)
  • UCL Biomedical Research Centre (GB)
  • Baker Heart and Diabetes Institute (AU)
Corresponding author
Kuchenbaecker, Karoline
ORCID
Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2022-08-09
Volume
13
Issue
1
Pages
4664-4664
ISSN
2041-1723 (ISSN-L)
DOI
10.1038/s41467-022-32095-5
PMC
PMC9363492
OpenAlex
W4291019306
Cited by
66
References
71
Citations by year
2026: 7 · 2025: 11 · 2024: 21 · 2023: 17 · 2022: 10
FWCI
8.90
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Communications (publishedVersion, cc-by)
  • medRxiv (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • UCL Discovery (University College London) (submittedVersion)
  • Minerva Access (University of Melbourne) (submittedVersion, cc-by)
  • Research Portal (King's College London) (submittedVersion, other-oa)
  • Queen Mary Research Online (Queen Mary University of London) (submittedVersion, cc-by)
  • Apollo (University of Cambridge) (submittedVersion, cc-by)
  • Apollo
  • Apollo (cc-by)
Funders
  • Wellcome Trust (WT210561, 206194)
  • Higher Education Funding Council for England (845/1796)
  • EC | Horizon 2020 Framework Programme (948561)
  • British Heart Foundation (RG/13/13/30194, RG/18/13/33946)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
  • Social Sciences › Anthropology › Race, Genetics, and Society
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
Keywords
polygenic risk score, cardiometabolic traits, coronary artery disease, South Asian ancestry, British Pakistanis, Bangladeshis, serum lipids, blood pressure, body mass index, risk reclassification, Mendelian randomization, population stratification
MeSH terms
Coronary Artery Disease, Humans, Pakistan, Polymorphism, Single Nucleotide, Asian People, Genome-Wide Association Study, Genetic Loci

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review