Meta-Analysis

. 2023 Jun;29(6):1540-1549.

doi: 10.1038/s41591-023-02374-9. Epub 2023 May 29.

Polygenic prediction of preeclampsia and gestational hypertension

Michael C Honigberg^#^{1

2

3}, Buu Truong^#^{4

5}, Raiyan R Khan⁶, Brenda Xiao⁷, Laxmi Bhatta^{8

9}, Ha My T Vy¹⁰, Rafael F Guerrero¹¹, Art Schuermans^{4

5

12}, Margaret Sunitha Selvaraj^{4

5}, Aniruddh P Patel^{13

4

5}, Satoshi Koyama^{4

5}, So Mi Jemma Cho^{4

5

14}, Shamsudheen Karuthedath Vellarikkal^{5

15}, Mark Trinder^{5

16}, Sarah M Urbut^{13

4

5}, Kathryn J Gray^{17

18}, Ben M Brumpton^{8

9}, Snehal Patil¹⁹, Sebastian Zöllner¹⁹, Mariah C Antopia²⁰, Richa Saxena^{5

18}, Girish N Nadkarni¹⁰, Ron Do¹⁰, Qi Yan²¹, Itsik Pe'er⁶, Shefali Setia Verma⁷, Rajat M Gupta^{5

15}, David M Haas²², Hilary C Martin²³, David A van Heel²⁴, Triin Laisk²⁵, Pradeep Natarajan^{26

27

28

29}

Affiliations

¹ Cardiology Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA. mhonigberg@mgh.harvard.edu.
² Cardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. mhonigberg@mgh.harvard.edu.
³ Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA. mhonigberg@mgh.harvard.edu.
⁴ Cardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
⁵ Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
⁶ Department of Computer Science, Columbia University, New York, NY, USA.
⁷ University of Pennsylvania, Philadelphia, PA, USA.
⁸ K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway.
⁹ HUNT Research Centre, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Levanger, Norway.
¹⁰ The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
¹¹ Department of Biological Sciences, North Carolina State University, Raleigh, NC, USA.
¹² Faculty of Medicine, KU Leuven, Leuven, Belgium.
¹³ Cardiology Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA.
¹⁴ Integrative Research Center for Cerebrovascular and Cardiovascular Diseases, Seoul, Republic of Korea.
¹⁵ Cardiovascular Division, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
¹⁶ Centre for Heart Lung Innovation, University of British Columbia, Vancouver, British Columbia, Canada.
¹⁷ Department of Obstetrics and Gynecology, Brigham and Women's Hospital, Boston, MA, USA.
¹⁸ Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
¹⁹ Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA.
²⁰ Department of Integrative Biology, University of Texas at San Antonio, San Antonio, TX, USA.
²¹ Department of Obstetrics and Gynecology, Columbia University, New York, NY, USA.
²² Indiana University School of Medicine, Indianapolis, IN, USA.
²³ Department of Human Genetics, Wellcome Sanger Institute, Cambridge, UK.
²⁴ Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK.
²⁵ Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia.
²⁶ Cardiology Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA. pnatarajan@mgh.harvard.edu.
²⁷ Cardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. pnatarajan@mgh.harvard.edu.
²⁸ Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA. pnatarajan@mgh.harvard.edu.
²⁹ Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA. pnatarajan@mgh.harvard.edu.

^# Contributed equally.

PMID: 37248299
PMCID: PMC10330886
DOI: 10.1038/s41591-023-02374-9

Meta-Analysis

Polygenic prediction of preeclampsia and gestational hypertension

Michael C Honigberg et al. Nat Med. 2023 Jun.

. 2023 Jun;29(6):1540-1549.

doi: 10.1038/s41591-023-02374-9. Epub 2023 May 29.

Authors

Affiliations

¹ Cardiology Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA. mhonigberg@mgh.harvard.edu.
² Cardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. mhonigberg@mgh.harvard.edu.
³ Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA. mhonigberg@mgh.harvard.edu.
⁴ Cardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
⁵ Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
⁶ Department of Computer Science, Columbia University, New York, NY, USA.
⁷ University of Pennsylvania, Philadelphia, PA, USA.
⁸ K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway.
⁹ HUNT Research Centre, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Levanger, Norway.
¹⁰ The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
¹¹ Department of Biological Sciences, North Carolina State University, Raleigh, NC, USA.
¹² Faculty of Medicine, KU Leuven, Leuven, Belgium.
¹³ Cardiology Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA.
¹⁴ Integrative Research Center for Cerebrovascular and Cardiovascular Diseases, Seoul, Republic of Korea.
¹⁵ Cardiovascular Division, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
¹⁶ Centre for Heart Lung Innovation, University of British Columbia, Vancouver, British Columbia, Canada.
¹⁷ Department of Obstetrics and Gynecology, Brigham and Women's Hospital, Boston, MA, USA.
¹⁸ Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
¹⁹ Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA.
²⁰ Department of Integrative Biology, University of Texas at San Antonio, San Antonio, TX, USA.
²¹ Department of Obstetrics and Gynecology, Columbia University, New York, NY, USA.
²² Indiana University School of Medicine, Indianapolis, IN, USA.
²³ Department of Human Genetics, Wellcome Sanger Institute, Cambridge, UK.
²⁴ Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK.
²⁵ Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia.
²⁶ Cardiology Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA. pnatarajan@mgh.harvard.edu.
²⁷ Cardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. pnatarajan@mgh.harvard.edu.
²⁸ Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA. pnatarajan@mgh.harvard.edu.
²⁹ Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA. pnatarajan@mgh.harvard.edu.

^# Contributed equally.

PMID: 37248299
PMCID: PMC10330886
DOI: 10.1038/s41591-023-02374-9

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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Conflict of interest statement

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.

Figures

**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.

**Fig. 1 |. Manhattan plots of preeclampsia/eclampsia and gestational hypertension in combined discovery and follow-up meta-analysis.**
a,b, Manhattan plots (chromosomal position on the x axis and −log₁₀ of the P value on the y axis) are displayed for (a) preeclampsia/eclampsia in 20,064 cases and 703,117 controls and (b) gestational hypertension in 11,027 cases and 412,788 controls. Analyses included a 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 a fixed-effect inverse-variance-weighted meta-analysis.

**Fig. 2 |. Polygenic prediction of preeclampsia/eclampsia and gestational hypertension in test cohorts.**
PRS for preeclampsia/eclampsia and gestational hypertension were derived from our discovery genome-wide meta-analyses, tuned in the UK Biobank, and carried forward for testing in independent cohorts (HUNT and nuMoM2b). Prevalence of preeclampsia/eclampsia versus percentile of PRS_{preeclampsia+SBP} in (a) HUNT and (b) nuMoM2b. c, Prevalence of gestational hypertension vs. percentile of PRS_GH+SBP in nuMoM2b. Distribution of PRS_{preeclampsia+SBP} percentile by preeclampsia/eclampsia status in (d) HUNT (n = 25,582; 1,569 with preeclampsia/eclampsia and 24,013 control participants) and (e) nuMoM2b (n = 6,225; 481 with preeclampsia/eclampsia and 5,744 control participants). f, Distribution of PRS_GH+SBP percentile by gestational hypertension status in nuMoM2b (n = 7,063; 1,319 with gestational hypertension and 5,744 control participants). Within each boxplot, horizontal lines reflect the median, top and bottom of the box reflect the interquartile range and whiskers reflect the maximum and minimum PRS percentile within each grouping.

**Fig. 3 |. Sex-stratified phenome-wide association study of preeclampsia/eclampsia polygenic risk in the UK Biobank.**
Preeclampsia/eclampsia polygenic risk was associated with 1,445 phenotypes among (a) female and (b) male participants in the UK Biobank. 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.

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References

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Polygenic prediction of preeclampsia and gestational hypertension

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Polygenic prediction of preeclampsia and gestational hypertension

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Conflict of interest statement

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