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Atlas of genetic and phenotypic associations across 42 female reproductive health diagnoses

Nature Medicine volume 31, pages 1626–1634 (2025)Cite this article

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Abstract

The genetic background of many female reproductive health diagnoses remains uncharacterized, compromising our understanding of the underlying biology. Here, we map the genetic architecture across 42 female-specific health conditions using data from up to 293,618 women from two large population-based cohorts, the Estonian Biobank and the FinnGen study. Our study illustrates the utility of genetic analyses in understanding women’s health better. As specific examples, we describe genetic risk factors for ovarian cysts that elucidate the genetic determinants of folliculogenesis and, by leveraging population-specific variants, uncover new candidate genes for uterine fibroids. We find that most female reproductive health diagnoses have a heritable component, with varying degrees of polygenicity and discoverability. Finally, we identify pleiotropic loci and genes that function in genital tract development (WNT4, PAX8, WT1, SALL1), hormonal regulation (FSHB, GREB1, BMPR1B, SYNE1/ESR1) and folliculogenesis (CHEK2), underlining their integral roles in female reproductive health.

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Fig. 1: Study design and effective sample size (neff) for 42 analyzed female health diagnoses.
Fig. 2: SNP-based heritability, genetic correlations and phenotypic overlap, and discoverability and polygenicity of the studied traits.
Fig. 3: PRS for ICP.
Fig. 4: Overview of loci associated with the studied female reproductive health traits.

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Data availability

Summary statistics from the meta-analyses described in this paper can be accessed from the GWAS Catalog (https://www.ebi.ac.uk/gwas/) (accession codes GCST90454202–GCST90454243). FinnGen summary statistics can be accessed as described at https://www.finngen.fi/en/access_results. The procedure to access EstBB individual-level data has been described at https://genomics.ut.ee/en/content/estonian-biobank#dataaccess.

The Trøndelag Health Study (HUNT) may be accessed by application to the HUNT Research Centre at https://www.ntnu.edu/hunt/data. The Trøndelag Health Study (HUNT) has invited persons aged 13–100 years to four surveys between 1984 and 2019. Comprehensive data from more than 140,000 persons who have participated at least once and biological material from 78,000 persons are collected. Data are stored in the HUNT databank, and biological material is stored in the HUNT biobank. The HUNT Research Centre has permission from the Norwegian Data Inspectorate to store and handle these data. The key identification in the database is the personal identification number given to all Norwegians at birth or immigration, whereas deidentified data are sent to researchers upon approval of a research protocol by the Regional Ethical Committee and the HUNT Research Centre. To protect participants’ privacy, the HUNT Research Centre aims to limit the storage of data outside the HUNT databank and cannot deposit data in open repositories. The HUNT databank has precise information on all data exported to different projects and is able to reproduce these on request. There are no restrictions regarding data export given approval of applications to the HUNT Research Centre. For more information, see http://www.ntnu.edu/hunt/data.

The following databases used in the analyses are freely available online:

1. To identify population-specific variants, we performed allele frequency look-up using the Open Targets Genetics v8 portal (https://genetics.opentargets.org/) and the gnomAD v2.1 database (https://gnomad.broadinstitute.org/).

2. To establish the potential biological role of candidate genes for the ovarian cysts phenotype, we queried the Mouse Genome Informatics database (https://www.informatics.jax.org/).

Other datasets (for example, the GRCh37 reference genome) used in the analyses are integrated components of the publicly available platforms and tools used for the data analysis.

Code availability

All software programs used for the analyses described in this paper are freely available online: GWAMA v2.1 (https://genomics.ut.ee/en/tools); REGENIE v2.0.2 and v2.2.4 (https://github.com/rgcgithub/regenie); UCSC liftOver command line tool version downloaded from the UCSC Genome Browser toolkit (http://genome.ucsc.edu/); FUMA v1.4.0 (https://fuma.ctglab.nl/); MAGMA v1.08 integrated in FUMA v1.4.0; LDSC v1.0.1 (https://github.com/bulik/ldsc); MiXeR v1.3 (https://github.com/precimed/mixer); Fuji plot (https://github.com/mkanai/fujiplot); PLINK2 (www.cog-genomics.org/plink/2.0/); PRS-CS (last version updated January 4, 2021, https://github.com/getian107/PRScs); PGS Catalog Calculator v2.0.0-beta.3 (https://github.com/PGScatalog/pgsc_calc); R package PheWAS v1.0. For FinnGen, code to perform GWAS analyses is available at the FinnGen GitHub (https://github.com/FINNGEN/). Individual plots were created using R v3.6.3, v4.2.2 and v4.3.2, including the R packages corrplot v0.92, ggplot2 v3.5.1 and tidyverse v2.0.0. The final figures were edited with Inkscape.

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Acknowledgements

This study was funded by the Ministry of Education and Research Centres of Excellence grant TK214 Centre of Excellence for Personalised Medicine and by the Estonian Research Council grants PSG776, PRG1911 and PRG1076; and the European Union’s Horizon 2020 research and innovation program under the MATER Marie Skłodowska-Curie grant agreement no. 813707. The analyses were performed in the High-Performance Computing Center of the University of Tartu. We thank the participants of the FinnGen and EstBB studies. This work was partially written at writing retreats organized by the Institute of Genomics, University of Tartu.

The Trøndelag Health Study (HUNT) is a collaboration between the HUNT Research Centre (Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, NTNU), Trøndelag County Council, Central Norway Regional Health Authority and the Norwegian Institute of Public Health. The genotyping in HUNT was financed by the National Institutes of Health; the University of Michigan; the Research Council of Norway; the Liaison Committee for Education, Research and Innovation in central Norway; and the Joint Research Committee between St. Olavs Hospital and the Faculty of Medicine and Health Sciences, NTNU. Members of the HUNT All-In Research Team (in alphabetical order by surname): B. O. Åsvold, B. Brumpton, M. E. Gabrielsen, K. Hveem, I. Surakka, L. Thomas and W. Zhou.

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Authors and Affiliations

  1. Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia

    Natàlia Pujol Gualdo, Jelisaveta Džigurski, Valentina Rukins, Fanny-Dhelia Pajuste, Mariann Võsa, Mia Golob, Lisette Haug, Maris Alver, Kristi Läll, Andres Metspalu, Lili Milani, Tõnu Esko, Mari Nelis, Georgi Hudjashov, Priit Palta, Reedik Mägi & Triin Laisk

  2. HUNT Center for Molecular and Clinical Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway

    Brooke N. Wolford & Ben M. Brumpton

  3. Department of Obstetrics and Gynecology, Institute of Clinical Medicine, University of Tartu, Tartu, Estonia

    Maire Peters

  4. Celvia CC AS, Tartu, Estonia

    Maire Peters

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Estonian Biobank Research Team

  • Andres Metspalu
  • , Lili Milani
  • , Tõnu Esko
  • , Mari Nelis
  •  & Georgi Hudjashov

Contributions

N.P.G., R.M., P.P. and T.L. designed the study. The EstBB Research Team collected and provided the genotype and phenotype data used in the EstBB cohort-level analyses. B.M.B. provided the genotype and phenotype data used in HUNT analyses. N.P.G., J.D., V.R., F.-D.P., B.N.W., K.L., M.G., L.H. and T.L. performed and interpreted the analyses. N.P.G., J.D., V.R. and T.L. wrote the paper. M.V. performed final edits of the figures. All authors discussed the results and commented on the paper.

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Correspondence to Triin Laisk.

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Nature Medicine thanks Peter Dixon and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editors: Sonia Muliyil and Liam Messin, in collaboration with the Nature Medicine team.

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Pujol Gualdo, N., Džigurski, J., Rukins, V. et al. Atlas of genetic and phenotypic associations across 42 female reproductive health diagnoses. Nat Med 31, 1626–1634 (2025). https://doi.org/10.1038/s41591-025-03543-8

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