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Polygenic prediction of preeclampsia and gestational hypertension

Nature Medicine volume 29, pages 1540–1549 (2023)Cite this article

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Abstract

Preeclampsia and gestational hypertension are common pregnancy complications associated with adverse maternal and child outcomes. Current tools for prediction, prevention and treatment are limited. Here we tested the association of maternal DNA sequence variants with preeclampsia in 20,064 cases and 703,117 control individuals and with gestational hypertension in 11,027 cases and 412,788 control individuals across discovery and follow-up cohorts using multi-ancestry meta-analysis. Altogether, we identified 18 independent loci associated with preeclampsia/eclampsia and/or gestational hypertension, 12 of which are new (for example, MTHFR–CLCN6, WNT3A, NPR3, PGR and RGL3), including two loci (PLCE1 and FURIN) identified in the multitrait analysis. Identified loci highlight the role of natriuretic peptide signaling, angiogenesis, renal glomerular function, trophoblast development and immune dysregulation. We derived genome-wide polygenic risk scores that predicted preeclampsia/eclampsia and gestational hypertension in external cohorts, independent of clinical risk factors, and reclassified eligibility for low-dose aspirin to prevent preeclampsia. Collectively, these findings provide mechanistic insights into the hypertensive disorders of pregnancy and have the potential to advance pregnancy risk stratification.

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Fig. 1: Manhattan plots of preeclampsia/eclampsia and gestational hypertension in combined discovery and follow-up meta-analysis.
Fig. 2: Polygenic prediction of preeclampsia/eclampsia and gestational hypertension in test cohorts.
Fig. 3: Sex-stratified phenome-wide association study of preeclampsia/eclampsia polygenic risk in the UK Biobank.

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Data availability

GWAS summary statistics for preeclampsia/eclampsia and gestational hypertension and genome-wide polygenic scores for preeclampsia/eclampsia, gestational hypertension and systolic blood pressure are available for download at https://doi.org/10.6084/m9.figshare.22680904.v1. Polygenic scores are also available in the PGS Catalog (https://www.pgscatalog.org/publication/PGP000462/). Summary statistics used in this meta-analysis are publicly available for FinnGen r6 (https://www.finngen.fi/en/access_results) and for BioBank Japan (https://pheweb.jp/pheno/PreEclampsia). Preeclampsia GWAS summary statistics from the InterPregGen consortium are available at https://ega-archive.org (dataset IDs EGAD00010001984 (European maternal meta-analysis), EGAD00010001985 (Central Asian maternal meta-analysis) and EGAD00010001983 (European and Central Asian fetal meta-analysis)). Placental transcriptome data are publicly available at https://www.obgyn.cam.ac.uk/placentome/.

Code availability

The code used to conduct these analyses is available at https://github.com/buutrg/HDP.

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Acknowledgements

This work was supported by grants from the US National Heart Lung and Blood Institute (K08HL166687 to M.C.H., K08HL146963 to K.J.G., R01 HL163234 to R.S. and K.J.G., R01HL139865 to R.D., R01HL155915 to R.D., DP2HL152423 to R.M.G., U01HL166060 to R.M.G., R03HL148483 to R.M.G., R01HL142711 to P.N., R01HL127564 to P.N., R01HL148050 to P.N., R01HL151283 to P.N., R01HL148565 to P.N., R01HL135242 to P.N. and R01HL151152 to P.N.); the American Heart Association (940166 to M.C.H. and 979465 to M.C.H.); the Korea Health Industry Development Institute (HI19C1330 to S.M.J.C.); Harvard Catalyst Medical Research Investigator Training Program (to A.P.P.); National Human Genome Research Institute (U01HG011719 to A.P.P. and P.N.); the Belgian American Educational Foundation (to A.S.); the US National Institute of General Medical Sciences (R35GM147197 to R.F.G. and R35GM124836 to R.D.); National Institute of Diabetes and Digestive and Kidney Diseases (R01DK125782 to P.N.); National Institute of Child Health and Human Development (R01HD101246 to D.M.H.); Preeclampsia Foundation (to K.J.G. and R.S.); Fondation Leducq (TNE-18CVD04 to P.N.) and the Massachusetts General Hospital Paul and Phyllis Fireman Endowed Chair in Vascular Medicine (to P.N.). We thank the participants and investigators from the InterPregGen consortium, FinnGen, Estonian Biobank, Genes & Health, Michigan Genomics Initiative, Mass General Brigham Biobank, BioBank Japan, BioMe, HUNT, PMBB, UK Biobank and nuMoM2b; additional acknowledgements appear in the Supplementary Note.

Author information

Author notes

  1. These authors contributed equally: Michael C. Honigberg, Buu Truong.

Authors and Affiliations

  1. Cardiology Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA

    Michael C. Honigberg, Aniruddh P. Patel, Sarah M. Urbut & Pradeep Natarajan

  2. Cardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA

    Michael C. Honigberg, Buu Truong, Art Schuermans, Margaret Sunitha Selvaraj, Aniruddh P. Patel, Satoshi Koyama, So Mi Jemma Cho, Sarah M. Urbut & Pradeep Natarajan

  3. Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA

    Michael C. Honigberg, Buu Truong, Art Schuermans, Margaret Sunitha Selvaraj, Aniruddh P. Patel, Satoshi Koyama, So Mi Jemma Cho, Shamsudheen Karuthedath Vellarikkal, Mark Trinder, Sarah M. Urbut, Richa Saxena, Rajat M. Gupta & Pradeep Natarajan

  4. Department of Computer Science, Columbia University, New York, NY, USA

    Raiyan R. Khan & Itsik Pe’er

  5. University of Pennsylvania, Philadelphia, PA, USA

    Brenda Xiao & Shefali Setia Verma

  6. K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway

    Laxmi Bhatta & Ben M. Brumpton

  7. HUNT Research Centre, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Levanger, Norway

    Laxmi Bhatta & Ben M. Brumpton

  8. The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Ha My T. Vy, Girish N. Nadkarni & Ron Do

  9. Department of Biological Sciences, North Carolina State University, Raleigh, NC, USA

    Rafael F. Guerrero

  10. Faculty of Medicine, KU Leuven, Leuven, Belgium

    Art Schuermans

  11. Integrative Research Center for Cerebrovascular and Cardiovascular Diseases, Seoul, Republic of Korea

    So Mi Jemma Cho

  12. Cardiovascular Division, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA

    Shamsudheen Karuthedath Vellarikkal & Rajat M. Gupta

  13. Centre for Heart Lung Innovation, University of British Columbia, Vancouver, British Columbia, Canada

    Mark Trinder

  14. Department of Obstetrics and Gynecology, Brigham and Women’s Hospital, Boston, MA, USA

    Kathryn J. Gray

  15. Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA

    Kathryn J. Gray, Richa Saxena & Pradeep Natarajan

  16. Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA

    Snehal Patil & Sebastian Zöllner

  17. Department of Integrative Biology, University of Texas at San Antonio, San Antonio, TX, USA

    Mariah C. Antopia

  18. Department of Obstetrics and Gynecology, Columbia University, New York, NY, USA

    Qi Yan

  19. Indiana University School of Medicine, Indianapolis, IN, USA

    David M. Haas

  20. Department of Human Genetics, Wellcome Sanger Institute, Cambridge, UK

    Hilary C. Martin

  21. Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK

    David A. van Heel

  22. Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia

    Triin Laisk

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  1. Michael C. Honigberg
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  2. Buu Truong
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Contributions

M.C.H., B.T. and P.N conceived these analyses. M.C.H., B.T., R.R.K., B.X., L.B., H.M.T.V., M.S.S., D.A.v.H. and T.L. performed formal analyses. M.C.H., B.T., A.P.P., R.F.G., S.M.J.C., S.M.U., K.J.G., B.M.B., S.P., S.Z., G.N.N., R.D., D.M.H., T.L. and P.N. provided resources. M.C.H., B.T., B.X., S.K., M.T., M.C.A., D.A.v.H. and T.L. performed data curation. M.C.H. and B.T. drafted the manuscript. M.C.H., B.T., R.R.K., B.X., L.B., A.S., S.K.V. and R.M.G. performed data visualization. K.J.G., R.S., G.N.N., R.D., Q.Y., I.P., S.S.V., H.C.M., D.A.v.H., T.L. and P.N. supervised the study. All authors contributed to the critical review and revision of the manuscript.

Corresponding authors

Correspondence to Michael C. Honigberg or Pradeep Natarajan.

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Competing interests

M.C.H. reports consulting fees from CRISPR Therapeutics, advisory board service for Miga Health, and grant support from Genentech, all unrelated to this work. K.J.G. has served as a consultant for BillionToOne, Aetion and Roche for projects unrelated to this work. R.S. is a cofounder of Magnet Biomedicine, unrelated to this work. R.D. reports receiving grants from AstraZeneca and grants and nonfinancial support from Goldfinch Bio, being a scientific cofounder, consultant and equity holder for Pensieve Health (pending) and being a consultant for Variant Bio, all unrelated to this work. P.N. reports grant support from Amgen, Apple, AstraZeneca, Boston Scientific and Novartis; spousal employment and equity at Vertex; consulting income from Apple, AstraZeneca, Novartis, Genentech/Roche, Blackstone Life Sciences, Foresite Labs and TenSixteen Bio and is a scientific advisor board member and shareholder of TenSixteen Bio and geneXwell, all unrelated to this work. All remaining authors report no competing interests.

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Nature Medicine thanks Lucy Chappell, Tu’uhevaha Kaituu-Lino and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling editor: Anna Maria Ranzoni, in collaboration with the Nature Medicine team.

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Extended data

Extended Data Fig. 1

Flow chart summarizing the study design and contributing cohorts.

Extended Data Fig. 2 Manhattan plots of preeclampsia/eclampsia and gestational hypertension in discovery cohorts.

Manhattan plots (chromosomal position on the X-axis and -log(10) of the P value on the Y-axis) are displayed for (a) preeclampsia/eclampsia in 17,150 cases and 451,241 controls and (b) gestational hypertension in 8,961 cases and 184,925 controls. Analyses included multi-ancestry meta-analysis of common variants (minor allele frequency ≥1%). Loci are labeled by the gene nearest to the lead variant. Two-sided P values (not adjusted for multiple comparisons) are from Z scores from fixed-effect inverse-variance weighted meta-analysis.

Extended Data Fig. 3 Results of multi-trait analysis of genome-wide summary statistics (MTAG) for preeclampsia/eclampsia.

Results are from joint analysis of summary statistics for preeclampsia/eclampsia and gestational hypertension in discovery cohorts. The plot displays chromosomal position on the X-axis and -log(10) of the P value on the Y-axis. Two-sided P values (not adjusted for multiple comparisons) are from Z scores from MTAG.

Extended Data Fig. 4 Relative expression of prioritized genes in human aortic cells with single-nuclei RNA sequencing.

We analyzed expression of genes prioritized by genome-wide meta-analysis of preeclampsia/eclampsia and gestational hypertension and secondary in silico analyses in a dataset of single-nuclei RNA sequencing from two normal human flash-frozen aortic specimens. Most prioritized genes were enriched in endothelial cell populations and/or macrophages.

Extended Data Fig. 5 Sex-stratified phenome-wide association study of gestational hypertension polygenic risk in the UK Biobank.

Gestational hypertension polygenic risk was associated with 1,445 phenotypes among (a) female and (b) male participants in the UK Biobank. Associations with phenotypes were tested using logistic regression with adjustment for age and the first five principal components of genetic ancestry. Two-sided P values (not adjusted for multiple comparisons) are from logistic regression models adjusted for age and the first five principal components of genetic ancestry.

Extended Data Table 1 Genetic correlation among preeclampsia, gestational hypertension, systolic blood pressure, and diastolic blood pressure
Full size table
Extended Data Table 2 Polygenic score performance for predicting preeclampsia/eclampsia in the HUNT study
Full size table

Supplementary information

Supplementary Information

Supplementary Fig. 1 and Note.

Reporting Summary

Supplementary Tables

Supplementary Tables 1–19.

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Honigberg, M.C., Truong, B., Khan, R.R. et al. Polygenic prediction of preeclampsia and gestational hypertension. Nat Med 29, 1540–1549 (2023). https://doi.org/10.1038/s41591-023-02374-9

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