Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2025 Apr 21;15(1):13743.
doi: 10.1038/s41598-025-97291-x.

Preeclampsia prediction with maternal and paternal polygenic risk scores: the TMM BirThree Cohort Study

Hisashi Ohseto  1 , Mami Ishikuro  2   3 , Taku Obara  1   4   5 , Akira Narita  1   4 , Ippei Takahashi  1 , Genki Shinoda  1   4 , Aoi Noda  1   4 , Keiko Murakami  4 , Masatsugu Orui  1   4 , Noriyuki Iwama  1   4   5 , Masahiro Kikuya  4   6 , Hirohito Metoki  4   7 , Junichi Sugawara  1   4   8 , Gen Tamiya  1   4   9 , Shinichi Kuriyama  1   4   10
Affiliations

Preeclampsia prediction with maternal and paternal polygenic risk scores: the TMM BirThree Cohort Study

Hisashi Ohseto et al. Sci Rep. .

Abstract

Genomic information from pregnant women and the paternal parent of their fetuses may provide effective biomarkers for preeclampsia (PE). This study investigated the association of parental polygenic risk scores (PRSs) for blood pressure (BP) and PE with PE onset and evaluated predictive performances of PRSs using clinical predictive variables. In the Tohoku Medical Megabank Project Birth and Three-Generation Cohort Study, 19,836 participants were genotyped using either Affymetrix Axiom Japonica Array v2 (further divided into two cohorts-the PRS training cohort and the internal-validation cohort-at a ratio of 1:2) or Japonica Array NEO (external-validation cohort). PRSs were calculated for systolic BP (SBP), diastolic BP (DBP), and PE and hyperparameters for PRS calculation were optimized in the training cohort. PE onset was associated with maternal SBP-, DBP-, and PE-PRSs and paternal SBP- and DBP-PRSs only in the external-validation cohort. Meta-analysis revealed overall associations with maternal PRSs but highlighted significant heterogeneity between cohorts. Maternal DBP-PRS calculated using "LDpred2" presented the most improvement in prediction models and provided additional predictive information on clinical predictive variables. Paternal DBP-PRS improved prediction models in the internal-validation cohort. In conclusion, Parental PRS, along with clinical predictive variables, is potentially useful for predicting PE.

Keywords: BirThree Cohort Study; Family history; Genome-wide association study; Hypertensive disorders of pregnancy; LDpred2.

PubMed Disclaimer

Conflict of interest statement

Declarations. Competing interests: K.M. is an employee of the Ministry of Education, Culture, Sports, Science and Technology, Japan. The other authors have no conflict of interests. Consent for publication: This manuscript has not been published in any journal or presented at any conference in part or in entirety and is not under consideration by another journal. It has been made publicly available as a preprint (DOI: https://doi.org/10.1101/2024.02.07.24302476 ).

Figures

Fig. 1
Fig. 1
TMM BirThree Cohort Study flow for three study cohorts. After excluding ineligible participants, the cohort was divided into three cohorts, namely PRS training, maternal internal validation, and maternal external validation. The cohorts with paternal genotypes in the maternal internal and external validation cohorts were the parental internal and external validation cohorts, respectively. The numbers of PE cases and total participants in each cohort are indicated in the format “PE cases, total participants”. TMM BirThree Cohort Study: The Tohoku Medical Megabank Project Birth and Three-Generation Cohort Study; JPA v2: Affymetrix Axiom Japonica Array v2; JPA NEO: Affymetrix Axiom Japonica Array NEO; PRS, polygenic risk score; and HDP, hypertensive disorders of pregnancy.
See this image and copyright information in PMC

Similar articles

  • Machine learning models for predicting blood pressure phenotypes by combining multiple polygenic risk scores.
    Hrytsenko Y, Shea B, Elgart M, Kurniansyah N, Lyons G, Morrison AC, Carson AP, Haring B, Mitchell BD, Psaty BM, Jaeger BC, Gu CC, Kooperberg C, Levy D, Lloyd-Jones D, Choi E, Brody JA, Smith JA, Rotter JI, Moll M, Fornage M, Simon N, Castaldi P, Casanova R, Chung RH, Kaplan R, Loos RJF, Kardia SLR, Rich SS, Redline S, Kelly T, O'Connor T, Zhao W, Kim W, Guo X, Ida Chen YD; Trans-Omics in Precision Medicine Consortium; Sofer T. Hrytsenko Y, et al. Sci Rep. 2024 May 30;14(1):12436. doi: 10.1038/s41598-024-62945-9. Sci Rep. 2024. PMID: 38816422 Free PMC article.
  • Evaluating the use of blood pressure polygenic risk scores across race/ethnic background groups.
    Kurniansyah N, Goodman MO, Khan AT, Wang J, Feofanova E, Bis JC, Wiggins KL, Huffman JE, Kelly T, Elfassy T, Guo X, Palmas W, Lin HJ, Hwang SJ, Gao Y, Young K, Kinney GL, Smith JA, Yu B, Liu S, Wassertheil-Smoller S, Manson JE, Zhu X, Chen YI, Lee IT, Gu CC, Lloyd-Jones DM, Zöllner S, Fornage M, Kooperberg C, Correa A, Psaty BM, Arnett DK, Isasi CR, Rich SS, Kaplan RC, Redline S, Mitchell BD, Franceschini N, Levy D, Rotter JI, Morrison AC, Sofer T. Kurniansyah N, et al. Nat Commun. 2023 Jun 2;14(1):3202. doi: 10.1038/s41467-023-38990-9. Nat Commun. 2023. PMID: 37268629 Free PMC article.
  • Increased Risk of Preeclampsia in Women With a Genetic Predisposition to Elevated Blood Pressure.
    Kivioja A, Toivonen E, Tyrmi J, Ruotsalainen S, Ripatti S, Huhtala H, Jääskeläinen T, Heinonen S, Kajantie E, Kere J, Kivinen K, Pouta A, Saarela T, Laivuori H. Kivioja A, et al. Hypertension. 2022 Sep;79(9):2008-2015. doi: 10.1161/HYPERTENSIONAHA.122.18996. Epub 2022 Jul 7. Hypertension. 2022. PMID: 35862124 Free PMC article.
  • Polygenic risk scores in atrial fibrillation: Associations and clinical utility in disease prediction.
    Gibson JT, Rudd JHF. Gibson JT, et al. Heart Rhythm. 2024 Jun;21(6):913-918. doi: 10.1016/j.hrthm.2024.02.006. Epub 2024 Feb 7. Heart Rhythm. 2024. PMID: 38336192 Review.
  • Polygenic risk scores in kidney transplantation.
    Jelencsics K, Oberbauer R. Jelencsics K, et al. Curr Opin Organ Transplant. 2025 Jun 1;30(3):208-214. doi: 10.1097/MOT.0000000000001212. Epub 2025 Apr 1. Curr Opin Organ Transplant. 2025. PMID: 40171629 Review.
See all similar articles

Cited by

References

    1. Ananth, C. V., Keyes, K. M. & Wapner, R. J. Pre-eclampsia rates in the united States, 1980–2010: Age-period-cohort analysis. BMJ347, f6564 (2013). - PMC - PubMed
    1. Shiozaki, A., Matsuda, Y., Satoh, S. & Saito, S. Comparison of risk factors for gestational hypertension and preeclampsia in Japanese Singleton pregnancies. J. Obstet. Gynaecol. Res.39, 492–499 (2013). - PubMed
    1. GBD 2015 Maternal Mortality Collaborators. Global, regional, and National levels of maternal mortality, 1990–2015: A systematic analysis for the global burden of disease study 2015. Lancet388, 1775–1812 (2016). - PMC - PubMed
    1. Davenport, M. H. et al. Prenatal exercise for the prevention of gestational diabetes mellitus and hypertensive disorders of pregnancy: A systematic review and meta-analysis. Br. J. Sports Med.52, 1367–1375 (2018). - PubMed
    1. Rolnik, D. L. et al. Aspirin versus placebo in pregnancies at high risk for preterm preeclampsia. N Engl. J. Med.377, 613–622 (2017). - PubMed

LinkOut - more resources