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Multi-ancestry genome-wide association analyses of polycystic ovary syndrome

Nature Genetics volume 57, pages 2669–2681 (2025)Cite this article

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Abstract

Polycystic ovary syndrome (PCOS), the leading endocrine disorder in women of reproductive age, is highly heritable, yet its polygenic architecture remains poorly understood. Here we conducted a genome-wide association study on 12,419 Chinese women with PCOS and 34,235 controls, followed by a multi-ancestry meta-analysis with up to 13,773 European cases and 411,088 controls, identifying 94 independent loci, 73 of which were previously unreported. Despite different evolutionary pressures, Chinese and European ancestries showed substantial genetic overlap. Integrative functional analyses prioritized regulatory variants controlling gene activity in specific tissues, disease-causing genes including anti-Müllerian hormone (AMH), and biological pathways involving ligand-binding domain interactions and peroxisome proliferator-activated receptor gamma (PPARG) signaling. We identified granulosa cells as particularly important in PCOS development. Our genetics-driven drug discovery approach revealed multiple drug targets and repurposing opportunities, enabling personalized treatment strategies. These results enhance our understanding of the molecular basis of PCOS, paving the way for precision medicine.

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Fig. 1: Genome-wide association meta-analysis of PCOS across ancestries.
Fig. 2: Circos plot of PCOS-associated loci with prioritized genes and pathways.
Fig. 3: Tissues and cell types involved in PCOS.
Fig. 4: Cross-ancestry comparison of genetic effect sizes for PCOS risk loci.
Fig. 5: Fine-mapping analysis for 16q23.2 locus using SuSiEx.
Fig. 6: Evolutionary signatures of PCOS risk variants across ancestries.
Fig. 7: Performance of PRSs for PCOS.
Fig. 8: Gene–drug interaction network for PCOS-associated genes.

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Data availability

Summary statistics for the meta-analyses presented in this paper have been deposited in OMIX at the National Genomics Data Center (NGDC) (https://ngdc.cncb.ac.cn/omix; accession number OMIX010148, https://ngdc.cncb.ac.cn/omix/release/OMIX010148) and are freely and openly accessible for research activities. We used publicly available data from Apollo–University of Cambridge Repository (https://doi.org/10.17863/CAM.36024), the FinnGen Freeze 10 cohort (https://www.finngen.fi/en/access_results), the NHGRI-EBI GWAS Catalog (https://www.ebi.ac.uk/gwas/studies/GCST90044902), the IEU OpenGWAS project (https://gwas.mrcieu.ac.uk/) and the BioBank Japan PheWeb (https://pheweb.jp/). RNA-sequencing data of adult human ovarian tissue are derived from Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/) with accession id GSE118127. Source data are provided with this paper.

Code availability

The following computer code was used in this paper: RICOPILI (v.2019_Jun_25.001, https://github.com/Ripkelab/ricopili/wiki)56 for quality control, principal-component analysis, pre-phasing, imputation, association testing and meta-analysis, with several embedded tools including EIGENSOFT (v.6.1.4, https://github.com/DReichLab/EIG)96, Eagle (v.2.3.5, https://github.com/poruloh/Eagle)58, Minimac3 (v.2.0.1, https://github.com/Santy-8128/Minimac3)59, PLINK (v.1.90b6.10, https://www.cog-genomics.org/plink/1.9/)97 and METAL (v.2011-03-25, https://github.com/statgen/METAL)61; LDSC (v.1.0.1, https://github.com/bulik/ldsc)18 for heritability and genetic correlation estimations; Popcorn (v.0.9.9, https://github.com/brielin/Popcorn)47 for trans-ancestry genetic correlation analysis; MAGMA (v.1.10, https://cncr.nl/research/magma/)27 and VEGAS2 (v.2.01.17, https://github.com/raydai/VEGAS2)28 for pathway analyses; DEPICT (v.1rel173, https://github.com/perslab/depict)29 for post GWAS analyses. Seurat (v.4.3.0, https://github.com/satijalab/seurat)71 for single-cell analysis; and SuSiE (v.0.14.2, https://stephenslab.github.io/susieR)78, SuSiEx (v.1.1.2, https://github.com/getian107/SuSiEx)33 and echolocatoR (v.2.0.3, https://github.com/RajLabMSSM/echolocatoR)79 for fine-mapping.

We also used PWCoCo (v.1.1.1, https://github.com/jwr-git/pwcoco)73 for pairwise conditional and colocalization analysis, LocusCompareR (v.1.0.0, https://github.com/boxiangliu/locuscomparer)77 for visualization of colocalization events, Metascape (v.3.5, https://metascape.org/)83 for gene annotation and analysis, PRSice (v.2.3.5, https://choishingwan.github.io/PRSice/)85 for PRS analyses, TwoSampleMR (v.0.6.3, https://mrcieu.github.io/TwoSampleMR/)95 for Mendelian randomization analyses and GREP (v.1.0.0, https://github.com/saorisakaue/GREP)35 and DGIdb (v.5.0.7, https://github.com/dgidb/dgidb-v5)38 for drug analyses.

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Acknowledgements

We thank the reviewers for their insightful and constructive comments, which substantially improved the paper. We thank all participants and staff of the contributing studies. In particular, we acknowledge the participants and investigators of the FinnGen study. This study was supported by 973 Program (2015CB559100 to Y. Shi), National Key Research and Development Program of China (2021YFC2700400 to H.Z., 2024YFC2707300 to S.Z. and 2023YFA0913804 to H.Z.), National Natural Science Foundation of China (82421004 to H.Z., 32588201 to Z.C., 32370916 to S.Z., 82271540 to Z.L., 82101707 to X.G. and 82071606 to S.Z.), Shandong Provincial Key Research and Development Program (2024CXPT087 to H.Z., 2021ZDSYS06 to Z.L. and 2020ZLYS02 to Z.C.), Ningxia Hui Autonomous Region Key Research and Development Program (2024BEG02019 to H.Z.), Natural Science Foundation of Shandong Province (ZR2023YQ061 to S.Z. and YDZX2021009 to Z.L.), Shanghai Municipal Science and Technology Major Project (2019SHZDZX02 and 2017SHZDZX01 to Y. Shi), MOE Key Laboratory of Population Health Across Life Cycle (JK20232 to Y.C.), CAMS Innovation Fund for Medical Sciences (2021-I2M-5-001 to Z.C.), Taishan Scholars Program of Shandong Province (tstp20240526 to Z.L. and ts20190988 to H.Z.), Fundamental Research Funds for the Central Universities (YG2021ZD2020 to Y. Shi), Medicine Plus Interdisciplinary Cluster Joint Exploration Project of Qingdao Medical University (RZ2400001468) and Fundamental Research Funds of Shandong University (2023QNTD004 to S.Z.).

Author information

Author notes

  1. These authors contributed equally: Han Zhao, Yuping Xu, Baiqiang Xue, Shigang Zhao, Manfei Zhang.

Authors and Affiliations

  1. State Key Laboratory of Reproductive Medicine and Offspring Health, Center for Clinical Reproductive Medicine, the First Affiliated Hospital of Nanjing Medical University, Center for Reproductive Medicine, Institute of Women, Children and Reproductive Health, Shandong University, Jinan, China

    Han Zhao, Shigang Zhao, Shumin Li, Ziyi Yang, Zhao Wang, Changming Zhang, Xin Zhang & Zi-Jiang Chen

  2. Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), the Collaborative Innovation Center for Brain Science, Shanghai Jiao Tong University, Shanghai, China

    Han Zhao, Manfei Zhang, Yanqin Wen, Qing Zhang, Hui Sun, Ting Pan, Xuemin Jian, Disong Xia, Ziyun Wu, Qiangzhen Yang, Siyuan Du, Lin He, Zhiqiang Li & Yongyong Shi

  3. Key Laboratory of Reproductive Endocrinology (Shandong University), Ministry of Education, Jinan, China

    Han Zhao, Shigang Zhao, Shumin Li, Ziyi Yang, Zhao Wang, Changming Zhang, Xin Zhang & Zi-Jiang Chen

  4. Department of Obstetrics and Gynecology, NHC Key Laboratory of Study on Abnormal Gametes and Reproductive Tract, the First Affiliated Hospital of Anhui Medical University, Hefei, China

    Yuping Xu & Tianjuan Wang

  5. Engineering Research Center of Biopreservation and Artificial Organs, Ministry of Education, Hefei, China

    Yuping Xu, Tianjuan Wang & Yunxia Cao

  6. The Affiliated Hospital of Qingdao University and Institute of Genetics and Integrative Biology (Qingdao Branch of SJTU Bio-X Institutes), Qingdao University, Qingdao, China

    Baiqiang Xue, Yihong Lian, Chengwen Gao, Chuanhong Wu, Lixia Peng, Baokun Wang, Yuanchao Sun, Yonghe Ding & Zhiqiang Li

  7. School of Public Health, Qingdao University, Qingdao, China

    Baiqiang Xue & Zhiqiang Li

  8. Department of Obstetrics and Gynecology, First Affiliated Hospital, Zhejiang Chinese Medical University, Hangzhou, and First Affiliated Hospital, Heilongjiang University of Chinese Medicine, Harbin, China

    Xiao-Ke Wu

  9. Shanghai Key Laboratory for Assisted Reproduction and Reproductive Genetics, Shanghai, China

    Xueying Gao & Zi-Jiang Chen

  10. Department of Reproductive Medicine, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China

    Xueying Gao & Zi-Jiang Chen

  11. School of Pharmacy, Qingdao University, Qingdao, China

    Lixia Peng, Baokun Wang & Zhiqiang Li

  12. Qingdao Medical College, Qingdao University, Qingdao, China

    Lieqing Wei, Guofeng Xia, Yulong Jing, Weiye Shi, Junting Liu & Zhiqiang Li

  13. Institute of Neuroscience, State Key Laboratory of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology (CEBSIT), Chinese Academy of Sciences, Shanghai, China

    Yuyan He, Hairong Xu & Yongyong Shi

  14. University of Chinese Academy of Sciences, Beijing, China

    Yuyan He & Hairong Xu

  15. Shandong Technology Innovation Center for Reproductive Health, Jinan, China

    Zi-Jiang Chen

  16. Shandong Provincial Clinical Research Center for Reproductive Health, Jinan, China

    Zi-Jiang Chen

  17. Shandong Key Laboratory of Reproductive Medicine, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China

    Zi-Jiang Chen

  18. Research Unit of Gametogenesis and Health of ART-Offspring, Chinese Academy of Medical Sciences (No.2021RU001), Jinan, China

    Zi-Jiang Chen

  19. NHC Key Laboratory of Study on Abnormal Gametes and Reproductive Tract, Anhui Medical University, Hefei, China

    Yunxia Cao

  20. Key Laboratory of Population Health Across Life Cycle (Anhui Medical University), Ministry of Education, Hefei, China

    Yunxia Cao

  21. Anhui Province Key Laboratory of Reproductive Disorders and Obstetrics and Gynaecology Diseases, Hefei, China

    Yunxia Cao

  22. International Center for Primate Brain Research, Institute of Neuroscience, Center of Excellence in Brain Science and Intelligent Technology, Chinese Academy of Sciences, Shanghai, China

    Yongyong Shi

Authors
  1. Han Zhao
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  2. Yuping Xu
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  3. Baiqiang Xue
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  4. Shigang Zhao
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  5. Manfei Zhang
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  6. Xiao-Ke Wu
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  7. Tianjuan Wang
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  8. Yanqin Wen
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  9. Shumin Li
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  10. Qing Zhang
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  11. Ziyi Yang
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  12. Hui Sun
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  13. Ting Pan
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  14. Yihong Lian
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  15. Xueying Gao
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  16. Chengwen Gao
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  17. Zhao Wang
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  18. Chuanhong Wu
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  19. Changming Zhang
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  20. Xuemin Jian
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  21. Lixia Peng
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  22. Xin Zhang
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  23. Baokun Wang
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  24. Lieqing Wei
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  25. Yuyan He
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  26. Disong Xia
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  27. Ziyun Wu
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  28. Qiangzhen Yang
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  29. Yuanchao Sun
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  30. Yonghe Ding
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  31. Siyuan Du
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  32. Guofeng Xia
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  33. Yulong Jing
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  34. Hairong Xu
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  35. Weiye Shi
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  36. Junting Liu
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  37. Lin He
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  38. Zi-Jiang Chen
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  39. Yunxia Cao
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  40. Zhiqiang Li
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  41. Yongyong Shi
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Contributions

Y. Shi, Z.L., Y.C. and H.Z. jointly supervised the research. Z.L., Y. Shi, H.Z. and S.Z. designed and conceived the study. Z.L., B.X., S.Z., M.Z., C.G. and S.L. performed bioinformatics analyses. H.Z., S.Z., Y.C., Y.X., X.W., T.W., S.L., Z.Y., X.G., Z. Wang, C.Z., X.Z., T.P., C.G., C.W., L.P., Y. Sun, Y.D. and Z.C. contributed samples and performed phenotyping. Y.W., Q.Z., H.S., Y.L., X.J., B.W., L.W., Y.H., D.X., Z. Wu, Q.Y., S.D., G.X., Y.J., H.X., W.S., J.L. and L.H. performed genotyping. Z.L., B.X., S.Z., M.Z., H.Z., Y. Shi, S.L. and Y.X. wrote and edited the paper. All authors critically reviewed the paper and approved the final version.

Corresponding authors

Correspondence to Han Zhao, Yunxia Cao, Zhiqiang Li or Yongyong Shi.

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Nature Genetics thanks Elisabet Stener-Victorin and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Workflow for polycystic ovary syndrome genome-wide association study.

Schematic diagram showing the study design, sample collection, and analysis pipeline used for identifying genetic variants associated with polycystic ovary syndrome (PCOS).

Source data

Extended Data Fig. 2 Distribution of genome-wide significant loci across ancestry groups.

a, Discovery of genome-wide significant loci across analyses. Bar chart showing the number of genome-wide significant loci (P < 5 × 10−8) identified in each analysis, with numbers above bars indicating exact loci counts. b, Overlap of the susceptibility loci between ancestries. Venn diagram illustrating the overlap of significant loci across Chinese (CHN), European (EUR/EUR2), and trans-ancestry (MIX/MIX2) analyses. Analyses included: CHN, Chinese cohort GWAS (12,419 cases; 34,235 controls); EUR, European cohort meta-analysis (8,589 cases; 328,329 controls); EUR2, European cohort meta-analysis focusing on 10 K array variants (13,773 cases; 411,088 controls); MIX, trans-ancestry meta-analysis of Chinese and European cohorts (21,008 cases; 362,564 controls); MIX2, trans-ancestry meta-analysis focusing on 10 K array variants (26,192 cases; 445,323 controls).

Source data

Extended Data Fig. 3 Tissue-specific regulatory scores for polycystic ovary syndrome associated lead variants.

Heatmap displaying RegulomeDB tissue-specific regulatory scores for lead variants, where each row represents a lead variant and each column represents a specific tissue. Color intensity indicates regulatory score strength (see color key within figure). Tissues are grouped by biological systems with system-specific colors indicated in the top sidebar. Left sidebars show overall RegulomeDB scores (1a-7, where lower scores indicate stronger regulatory evidence) and variant probability values from Supplementary Table 4.

Source data

Extended Data Fig. 4 Enriched biological processes in polycystic ovary syndrome associated genes.

a, Biological process enrichment in the associated gene sets. Heatmap showing enrichment of biological processes across polycystic ovary syndrome (PCOS) associated gene sets from East Asian (EAS) and European (EUR) populations, where color intensity represents -log₁₀(P-value), with darker orange indicating stronger enrichment (see color key within figure). Each row represents a biological process term, and columns represent different gene sets. b, Hierarchical clustering of enriched functional terms. Hierarchical organization of enriched terms according to Gene Ontology (GO) parent-child relationships, with color coding following panel (a). Enrichment analysis employed a one-sided hypergeometric test to identify over-represented biological processes. Multiple testing correction was applied using the Benjamini-Hochberg method with FDR < 0.05 as the significance threshold. Heatmap colors represent −log₁₀(adjusted P-values).

Source data

Extended Data Fig. 5 Polygenic risk score model performance across P-value thresholds.

Bar graph showing the variance explained (Nagelkerke’s pseudo-R²) by polygenic risk scores (PRS) constructed using variants meeting different P-value thresholds (x-axis: 5e-08, 1e-05, 0.001, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 1). Y-axis shows R2 values (0 to maximum observed value). Bars are grouped by target dataset and colored by ancestry cohort (see color key within figure). Four independent Chinese cohorts include ASA (7,891 cases/12,001 controls), CHB (2,406 cases/4,863 controls), ASI (1,362 cases/10,501 controls), and SNP6 (760 cases/6,870 controls). PRS were constructed using summary statistics from an inverse-variance weighted (IVW) fixed-effects meta-analysis (CHN = Chinese-specific, EUR = European-specific, CHN + EUR = cross-ancestry mixed). All statistical tests were two-sided, with no multiple testing correction applied.

Source data

Supplementary information

Supplementary Information (download PDF )

Supplementary Figures 1–5, and Supplementary Note.

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Zhao, H., Xu, Y., Xue, B. et al. Multi-ancestry genome-wide association analyses of polycystic ovary syndrome. Nat Genet 57, 2669–2681 (2025). https://doi.org/10.1038/s41588-025-02393-x

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