Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Sex-specific genetic architecture of blood pressure

Nature Medicine volume 30, pages 818–828 (2024)Cite this article

Subjects

Abstract

The genetic and genomic basis of sex differences in blood pressure (BP) traits remain unstudied at scale. Here, we conducted sex-stratified and combined-sex genome-wide association studies of BP traits using the UK Biobank resource, identifying 1,346 previously reported and 29 new BP trait-associated loci. Among associated loci, 412 were female-specific (Pfemale ≤ 5 × 10−8; Pmale > 5 × 10−8) and 142 were male-specific (Pmale ≤ 5 × 10−8; Pfemale > 5 × 10−8); these sex-specific loci were enriched for hormone-related transcription factors, in particular, estrogen receptor 1. Analyses of gene-by-sex interactions and sexually dimorphic effects identified four genomic regions, showing female-specific associations with diastolic BP or pulse pressure, including the chromosome 13q34-COL4A1/COL4A2 locus. Notably, female-specific pulse pressure-associated loci exhibited enriched acetylated histone H3 Lys27 modifications in arterial tissues and a female-specific association with fibromuscular dysplasia, a female-biased vascular disease; colocalization signals included Chr13q34: COL4A1/COL4A2, Chr9p21: CDKN2B-AS1 and Chr4q32.1: MAP9 regions. Sex-specific and sex-biased polygenic associations of BP traits were associated with multiple cardiovascular traits. These findings suggest potentially clinically significant and BP sex-specific pleiotropic effects on cardiovascular diseases.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Study overview.
Fig. 2: Sex differences in the complex genetic architectures of BP traits and effects on gene regulation.
Fig. 3: Statistical analysis of sex interactions by evaluating SDEs and gene-by-sex interactions.
Fig. 4: Associations of sex-stratified BP PGSs with cardiovascular traits and diseases defined sex-dimorphic or sex-specific associations.
Fig. 5: Fine-mapping of the chromosome 13q34-COL4A1/COL4A2 region overlapping DBP and PP signals.

Similar content being viewed by others

Data availability

The results of the sex-stratified GWAS of BP traits in the UKB are available through the GWAS Catalog (temporary accession code GCP000792). The female-only GWAS of PE in UKB and MGI HTN GWAS data are also deposited in the GWAS Catalog (temporary accession code GCP000792). Other datasets were previously published and available as described in Methods. Data for eQTL and sex-biased genes were retrieved from version 8 of the GTEx database (https://gtexportal.org/home/datasets). H3K27ac ChIP–seq datasets were retrieved from ENCODE (https://www.encodeproject.org/). The LOLA TFBS database was used in the Unibind enrichment analysis (https://zenodo.org/records/4161028). Representative GWAS summary statistics for each CVD trait data were collected from the GWAS Catalog under accession GCST90026612 (ref. 23) for FMD, GCST90245878 (ref. 26) for SCAD, GCST90018906 (ref. 22) for PE, GCST011365 (ref. 98) for MI, GCST90162626 (ref. 99) for HF, GCST90204201 (ref. 100) for atrial fibrillation, GCST90027266 (ref. 101) for TAA, GCST90018783 (ref. 22) for AA, GCST90094400 (ref. 56) and GCST90094401 (ref. 56) for thoracic aorta dimensions; and GCST90104539 (ref. 102), GCST90104540 (ref. 102), GCST90104542 (ref. 102), GCST90104541 (ref. 102) and GCST90104543 (ref. 102) for stroke. GWAS summary statistics of intracranial aneurysms were collected from the ISGC group103 or at https://cd.hugeamp.org/. CAD summary statistics were downloaded from CVDKP (https://personal.broadinstitute.org/ryank/Aragam_2022_CARDIoGRAM_CAD_GWAS.zip)104.

Other summary statistics collected as additional references are: GWAS catalog GCST90043949 (ref. 105 (essential hypertension), GCST90044351 (ref. 105; hypertension), GCST90044479 (ref. 105; PE), GCST90018877 (ref. 22; MI), GCST90043954 (ref. 105; MI), GCST90043957 (ref. 105; CAD), GCST90014122 (ref. 106; stroke), GCST90044350 (ref. 105; stroke), GCST90043745 (ref. 105; migraine), GCST90043743 (ref. 105; migraine), GCST90018815 (ref. 22; cerebral aneurysm), GCST90018816 (ref. 22; cerebral aneurysm), GCST90044003 (ref. 105; cerebral aneurysm), GCST009541 (ref. 107; HF), GCST90018806 (ref. 22; HF), GCST90043986 (ref. 105; HF), GCST004296 (ref. 108; atrial fibrillation), GCST006414 (ref. 109; atrial fibrillation), GCST006061 (ref. 110; atrial fibrillation), GCST90043977 (ref. 105; atrial fibrillation), GCST90044010 (ref. 105; AA), GCST90044011 (ref. 105; AA), GCST90044012 (ref. 105; AA), GCST90044009 (ref. 105; abdominal AA) and GCST90000582 (ref. 52; SCAD). Other MI or CAD: CARDIoGRAMplusC4D Consortium (http://www.cardiogramplusc4d.org/data-downloads/)50. CAD: CVDKP (https://cvd.hugeamp.org/dinspector.html?dataset=GWAS_CADMETA_eu)111.

Stroke: https://www.megastroke.org/ (ref. 112).

Myocardial interstitial fibrosis: CVDKP (https://personal.broadinstitute.org/ryank/Nauffal_2023_myocardial_t1_time.zip)113. Phenotype summary was based on the UKB (https://www.ukbiobank.ac.uk/). Other sex-stratified CVD data referred to http://www.nealelab.is/uk-biobank/ (ref. 114). Full lists of the datasets used in this study, along with the corresponding accession numbers, are available in Supplementary Tables 11 and 18. The variants and weights used for each specific PGS are included in Supplementary Table 25. Source data are provided with this paper.

Code availability

Data summaries were based on a custom R script developed for this study. Pipeline scripts used for identifying region with sex-specific pleiotropy from the cross-trait colocalization analyses are deposited in our repository (https://github.com/momo121099/Sex-Specific-BP-Pleitropy_CVD). Publicly available software and packages were utilized for this study, following the instructions provided by their respective developers, including R v.4.3.0, PLINK v.1.9, PLINK v.2.0, Vcftool 0.1.14, BOLT-LMM v2.4.1, HOMER, Unibind, GREGOR v.1.4.0, METAL, LocusZoom v.0.12.0, FUMA/MAGMA, GEM, REGENIE, LDSC v1.0.1, Coloc, GTX tool package, QTLtools v1.3.1, mtCOJO from GCTA v.1.94.1, SusieR, MendelianRandomization, LDlink, ggplot2, gplots, pROC and Michigan Imputation Server (Minimac4 method, Eagle v2.3). Other permission-based tools used in this study were MGI Precision Health DataDirect and ENCORE.

References

  1. Padmanabhan, S. & Dominiczak, A. F. Genomics of hypertension: the road to precision medicine. Nat. Rev. Cardiol. 18, 235–250 (2021).

    Article  CAS  PubMed  Google Scholar 

  2. Mills, K. T., Stefanescu, A. & He, J. The global epidemiology of hypertension. Nat. Rev. Nephrol. 16, 223–237 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Evangelou, E. et al. Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits. Nat. Genet. 50, 1412–1425 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Regitz-Zagrosek, V. & Gebhard, C. Gender medicine: effects of sex and gender on cardiovascular disease manifestation and outcomes. Nat. Rev. Cardiol. 20, 236–247 (2023).

    Article  PubMed  Google Scholar 

  5. Colafella, K. M. M. & Denton, K. M. Sex-specific differences in hypertension and associated cardiovascular disease. Nat. Rev. Nephrol. 14, 185–201 (2018).

    Article  PubMed  Google Scholar 

  6. Maric-Bilkan, C. et al. Report of the National Heart, Lung, and Blood Institute Working Group on Sex Differences Research in Cardiovascular Disease: scientific questions and challenges. Hypertension 67, 802–807 (2016).

    Article  CAS  PubMed  Google Scholar 

  7. Carey, R. M., Moran, A. E. & Whelton, P. K. Treatment of hypertension: a review. JAMA 328, 1849–1861 (2022).

    Article  CAS  PubMed  Google Scholar 

  8. Whelton, P. K. et al. 2017 ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA Guideline for the Prevention, Detection, Evaluation, and Management of High Blood Pressure in Adults: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Hypertension 71, e13–e115 (2018).

    CAS  PubMed  Google Scholar 

  9. Unger, T. et al. 2020 International Society of Hypertension global hypertension practice guidelines. J. Hypertens. 38, 982–1004 (2020).

    Article  CAS  PubMed  Google Scholar 

  10. Lewington, S. et al. Age-specific relevance of usual blood pressure to vascular mortality: a meta-analysis of individual data for one million adults in 61 prospective studies. Lancet 360, 1903–1913 (2002).

    Article  PubMed  Google Scholar 

  11. Patel, S. A., Winkel, M., Ali, M. K., Narayan, K. M. & Mehta, N. K. Cardiovascular mortality associated with 5 leading risk factors: national and state preventable fractions estimated from survey data. Ann. Intern Med. 163, 245–253 (2015).

    Article  PubMed  Google Scholar 

  12. GRF Collaborators Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 392, 1923–1994 (2018).

  13. Elfassy, T. et al. Blood pressure and cardiovascular disease mortality among US adults: a sex-stratified analysis, 1999–2019. Hypertension 80, 1452–1462 (2023).

    Article  CAS  PubMed  Google Scholar 

  14. Oliva, M. et al. The impact of sex on gene expression across human tissues. Science 369, eaba3066 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Battle, A. et al. Genetic effects on gene expression across human tissues. Nature 550, 204–213 (2017).

    Article  PubMed  Google Scholar 

  16. Lucas, G. et al. Post-genomic update on a classical candidate gene for coronary artery disease: ESR1. Circ. Cardiovasc. Genet. 4, 647–654 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Hoffmann, T. J. et al. Genome-wide association analyses using electronic health records identify new loci influencing blood pressure variation. Nat. Genet. 49, 54–64 (2017).

    Article  CAS  PubMed  Google Scholar 

  18. Lee, D. et al. Tissue-specific and tissue-agnostic effects of genome sequence variation modulating blood pressure. Cell Rep. 42, 113351 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Sudlow, C. et al. UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 12, e1001779 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  20. Pirastu, N. et al. Genetic analyses identify widespread sex-differential participation bias. Nat. Genet. 53, 663–671 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Zawistowski, M. et al. The Michigan Genomics Initiative: a biobank linking genotypes and electronic clinical records in Michigan Medicine patients. Cell Genom. 3, 100257 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Sakaue, S. et al. A cross-population atlas of genetic associations for 220 human phenotypes. Nat. Genet. 53, 1415–1424 (2021).

    Article  CAS  PubMed  Google Scholar 

  23. Georges, A. et al. Genetic investigation of fibromuscular dysplasia identifies risk loci and shared genetics with common cardiovascular diseases. Nat. Commun. 12, 6031 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Honigberg, M. C. et al. Polygenic prediction of preeclampsia and gestational hypertension. Nat. Med. 29, 1540–1549 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Steinthorsdottir, V. et al. Genetic predisposition to hypertension is associated with preeclampsia in European and Central Asian women. Nat. Commun. 11, 5976 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Adlam, D. et al. Genome-wide association meta-analysis of spontaneous coronary artery dissection identifies risk variants and genes related to artery integrity and tissue-mediated coagulation. Nat. Genet. https://doi.org/10.1038/s41588-023-01410-1 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  27. Ventura-Clapier, R. et al. Sex in basic research: concepts in the cardiovascular field. Cardiovasc. Res. 113, 711–724 (2017).

    Article  CAS  PubMed  Google Scholar 

  28. Hartman, R. J. G., Huisman, S. E. & den Ruijter, H. M. Sex differences in cardiovascular epigenetics-a systematic review. Biol. Sex. Differ. 9, 19 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  29. Kim, E. S. H., Saw, J., Kadian-Dodov, D., Wood, M. & Ganesh, S. K. FMD and SCAD: sex-biased arterial diseases with clinical and genetic pleiotropy. Circ. Res. 128, 1958–1972 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Levy, D. et al. Genome-wide association study of blood pressure and hypertension. Nat. Genet. 41, 677–687 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Cabrera, C. P. et al. Over 1000 genetic loci influencing blood pressure with multiple systems and tissues implicated. Hum. Mol. Genet. 28, R151–R161 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Cho, S. M. J. et al. Measured blood pressure, genetically predicted blood pressure, and cardiovascular disease risk in the UK Biobank. JAMA Cardiol. 7, 1129–1137 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Vaura, F. et al. Polygenic risk scores predict hypertension onset and cardiovascular risk. Hypertension 77, 1119–1127 (2021).

    Article  CAS  PubMed  Google Scholar 

  34. Rawlik, K., Canela-Xandri, O. & Tenesa, A. Evidence for sex-specific genetic architectures across a spectrum of human complex traits. Genome Biol. 17, 166 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Bernabeu, E. et al. Sex differences in genetic architecture in the UK Biobank. Nat. Genet. 53, 1283–1289 (2021).

    Article  CAS  PubMed  Google Scholar 

  36. Zhu, C. et al. Amplification is the primary mode of gene-by-sex interaction in complex human traits. Cell Genom. 3, 100297 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Gerdts, E. et al. Sex differences in arterial hypertension. Eur. Heart J. 43, 4777–4788 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Ehret, G. B. et al. The genetics of blood pressure regulation and its target organs from association studies in 342,415 individuals. Nat. Genet. 48, 1171–1184 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Magnani, L. & Lupien, M. Chromatin and epigenetic determinants of estrogen receptor alpha (ESR1) signaling. Mol. Cell Endocrinol. 382, 633–641 (2014).

    Article  CAS  PubMed  Google Scholar 

  40. Achinger-Kawecka, J. et al. Epigenetic reprogramming at estrogen-receptor binding sites alters 3D chromatin landscape in endocrine-resistant breast cancer. Nat. Commun. 11, 320 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Menazza, S. & Murphy, E. The expanding complexity of estrogen receptor signaling in the cardiovascular system. Circ. Res. 118, 994–1007 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Hall, J. M., Couse, J. F. & Korach, K. S. The multifaceted mechanisms of estradiol and estrogen receptor signaling. J. Biol. Chem. 276, 36869–36872 (2001).

    Article  CAS  PubMed  Google Scholar 

  43. Traupe, T. et al. Distinct roles of estrogen receptors alpha and beta mediating acute vasodilation of epicardial coronary arteries. Hypertension 49, 1364–1370 (2007).

    Article  CAS  PubMed  Google Scholar 

  44. Joy, S. et al. The isoflavone Equol mediates rapid vascular relaxation: Ca2+-independent activation of endothelial nitric-oxide synthase/Hsp90 involving ERK1/2 and Akt phosphorylation in human endothelial cells. J. Biol. Chem. 281, 27335–27345 (2006).

    Article  CAS  PubMed  Google Scholar 

  45. Widder, J. et al. Improvement of endothelial dysfunction by selective estrogen receptor-alpha stimulation in ovariectomized SHR. Hypertension 42, 991–996 (2003).

    Article  CAS  PubMed  Google Scholar 

  46. Manson, J. E. et al. Menopausal hormone therapy and health outcomes during the intervention and extended poststopping phases of the Women’s Health Initiative randomized trials. JAMA 310, 1353–1368 (2013).

    Article  CAS  PubMed  Google Scholar 

  47. Hodis, H. N. et al. Hormone therapy and the progression of coronary-artery atherosclerosis in postmenopausal women. N. Engl. J. Med. 349, 535–545 (2003).

    Article  CAS  PubMed  Google Scholar 

  48. Grady, D. et al. Cardiovascular disease outcomes during 6.8 years of hormone therapy: Heart and Estrogen/progestin Replacement Study follow-up (HERS II). JAMA 288, 49–57 (2002).

    Article  PubMed  Google Scholar 

  49. Gornik, H. L. et al. First International Consensus on the diagnosis and management of fibromuscular dysplasia. Vasc. Med. 24, 164–189 (2019).

    Article  PubMed  Google Scholar 

  50. Nikpay, M. et al. A comprehensive 1,000 Genomes-based genome-wide association meta-analysis of coronary artery disease. Nat. Genet. 47, 1121–1130 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Almontashiri, N. A. M. et al. 9p21.3 coronary artery disease risk variants disrupt TEAD transcription factor-dependent transforming growth factor β regulation of p16 expression in human aortic smooth muscle cells. Circulation 132, 1969–1978 (2015).

    Article  CAS  PubMed  Google Scholar 

  52. Saw, J. et al. Chromosome 1q21.2 and additional loci influence risk of spontaneous coronary artery dissection and myocardial infarction. Nat. Commun. 11, 4432 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Yang, W. et al. Coronary-heart-disease-associated genetic variant at the COL4A1/COL4A2 locus affects COL4A1/COL4A2 expression, vascular cell survival, atherosclerotic plaque stability and risk of myocardial infarction. PLoS Genet. 12, e1006127 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  54. Koyama, S. et al. Population-specific and trans-ancestry genome-wide analyses identify distinct and shared genetic risk loci for coronary artery disease. Nat. Genet. 52, 1169–1177 (2020).

    Article  CAS  PubMed  Google Scholar 

  55. Francis, C. M. et al. Genome-wide associations of aortic distensibility suggest causality for aortic aneurysms and brain white matter hyperintensities. Nat. Commun. 13, 4505 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Pirruccello, J. P. et al. Deep learning enables genetic analysis of the human thoracic aorta. Nat. Genet. 54, 40–51 (2022).

    Article  CAS  PubMed  Google Scholar 

  57. Honigberg, M. C. et al. Long-term cardiovascular risk in women with hypertension during pregnancy. J. Am. Coll. Cardiol. 74, 2743–2754 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  58. Kivioja, A. et al. Increased risk of preeclampsia in women with a genetic predisposition to elevated blood pressure. Hypertension 79, 2008–2015 (2022).

    Article  CAS  PubMed  Google Scholar 

  59. Ganesh, S. K. et al. Effects of long-term averaging of quantitative blood pressure traits on the detection of genetic associations. Am. J. Hum. Genet. 95, 49–65 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Loh, P.-R. et al. Efficient Bayesian mixed-model analysis increases association power in large cohorts. Nat. Genet. 47, 284–290 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Pruim, R. J. et al. LocusZoom: regional visualization of genome-wide association scan results. Bioinformatics 26, 2336–2337 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Teslovich, T. M. et al. Biological, clinical and population relevance of 95 loci for blood lipids. Nature 466, 707–713 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Fritsche, L. G. et al. Association of polygenic risk scores for multiple cancers in a phenome-wide study: results from the Michigan Genomics Initiative. Am. J. Hum. Genet. 102, 1048–1061 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Zhou, W. et al. Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies. Nat. Genet. 50, 1335–1341 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Hoffmann, T. J. et al. Next generation genome-wide association tool: design and coverage of a high-throughput European-optimized SNP array. Genomics 98, 79–89 (2011).

    Article  CAS  PubMed  Google Scholar 

  66. Kvale, M. N. et al. Genotyping Informatics and quality control for 100,000 subjects in the Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort. Genetics 200, 1051–1060 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  67. Hoffmann, T. J. et al. Design and coverage of high throughput genotyping arrays optimized for individuals of East Asian, African American, and Latino race/ethnicity using imputation and a novel hybrid SNP selection algorithm. Genomics 98, 422–430 (2011).

    Article  CAS  PubMed  Google Scholar 

  68. Hoffmann, T. J. et al. A large genome-wide association study of QT interval length utilizing electronic health records. Genetics 222, iyac157 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  69. Khramtsova, E. A. et al. Sex differences in the genetic architecture of obsessive-compulsive disorder. Am. J. Med. Genet. B 180, 351–364 (2019).

    Article  CAS  Google Scholar 

  70. UK Biobank Neale laboratory. http://www.nealelab.is/uk-biobank/

  71. Westerman, K. E. et al. GEM: scalable and flexible gene-environment interaction analysis in millions of samples. Bioinformatics 37, 3514–3520 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. de Leeuw, C. A., Mooij, J. M., Heskes, T. & Posthuma, D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput. Biol. 11, e1004219 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  73. Watanabe, K., Taskesen, E., van Bochoven, A. & Posthuma, D. Functional mapping and annotation of genetic associations with FUMA. Nat. Commun. 8, 1826 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  74. Carithers, L. J. & Moore, H. M. The Genotype-Tissue Expression (GTEx) Project. Biopreserv. Biobank. 13, 307–308 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  75. Giambartolomei, C. et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 10, e1004383 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  76. Urbut, S. M., Wang, G., Carbonetto, P. & Stephens, M. Flexible statistical methods for estimating and testing effects in genomic studies with multiple conditions. Nat. Genet. 51, 187–195 (2019).

    Article  CAS  PubMed  Google Scholar 

  77. Law, C. W., Chen, Y., Shi, W. & Smyth, G. K. voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 15, R29 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  78. Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell. 38, 576–589 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Gheorghe, M. et al. A map of direct TF–DNA interactions in the human genome. Nucleic Acids Res. 47, e21 (2019).

    Article  PubMed  Google Scholar 

  80. Puig, R. R., Boddie, P., Khan, A., Castro-Mondragon, J. A. & Mathelier, A. UniBind: maps of high-confidence direct TF–DNA interactions across nine species. BMC Genomics 22, 482 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Sheffield, N. C. & Bock, C. LOLA: enrichment analysis for genomic region sets and regulatory elements in R and Bioconductor. Bioinformatics 32, 587–589 (2016).

    Article  CAS  PubMed  Google Scholar 

  82. The ENCODE Project Consortium An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).

  83. Moore, J. E. et al. Expanded encyclopaedias of DNA elements in the human and mouse genomes. Nature 583, 699–710 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  84. Schmidt, E. M. et al. GREGOR: evaluating global enrichment of trait-associated variants in epigenomic features using a systematic, data-driven approach. Bioinformatics 31, 2601–2606 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Johnson, T. Efficient Calculation for Multi-SNP Genetic Risk Scores. American Society of Human Genetics Annual Meeting, San Francisco, November 6–10 (2012).

  86. Mbatchou, J. et al. Computationally efficient whole-genome regression for quantitative and binary traits. Nat. Genet. 53, 1097–1103 (2021).

    Article  CAS  PubMed  Google Scholar 

  87. Bulik-Sullivan, B. K. et al. LD score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Bulik-Sullivan, B. et al. An atlas of genetic correlations across human diseases and traits. Nat. Genet. 47, 1236–1241 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Finucane, H. K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228–1235 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Clarke, G. M. et al. Basic statistical analysis in genetic case–control studies. Nat. Protoc. 6, 121–133 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Auton, A. et al. A global reference for human genetic variation. Nature 526, 68–74 (2015).

    Article  PubMed  Google Scholar 

  92. Yavorska, O. O. & Burgess, S. MendelianRandomization: an R package for performing Mendelian randomization analyses using summarized data. Int. J. Epidemiol. 46, 1734–1739 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  93. Broadbent, J. R. et al. MendelianRandomization v0.5.0: updates to an R package for performing Mendelian randomization analyses using summarized data. Wellcome Open Res. 5, 252 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  94. Delaneau, O. et al. A complete tool set for molecular QTL discovery and analysis. Nat. Commun. 8, 15452 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Wang, G., Sarkar, A, Carbonetto, P & Stephens, M. A simple new approach to variable selection in regression, with application to genetic fine mapping. J. R. Stat. Soc. Ser. B Stat. Methodol. https://doi.org/10.1111/rssb.12388 (2020).

  96. Zou, Y., Carbonetto, P., Wang, G. & Stephens, M. Fine-mapping from summary data with the ‘sum of single effects’ model. PLoS Genet. 18, e1010299 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Machiela, M. J. & Chanock, S. J. LDlink: a web-based application for exploring population-specific haplotype structure and linking correlated alleles of possible functional variants. Bioinformatics 31, 3555–3557 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Hartiala, J. A. et al. Genome-wide analysis identifies novel susceptibility loci for myocardial infarction. Eur. Heart J. 42, 919–933 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Levin, M. G. et al. Genome-wide association and multi-trait analyses characterize the common genetic architecture of heart failure. Nat. Commun. 13, 6914 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. Miyazawa, K. et al. Cross-ancestry genome-wide analysis of atrial fibrillation unveils disease biology and enables cardioembolic risk prediction. Nat. Genet. 55, 187–197 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Roychowdhury, T. et al. Regulatory variants in TCF7L2 are associated with thoracic aortic aneurysm. Am. J. Hum. Genet. 108, 1578–1589 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Mishra, A. et al. Stroke genetics informs drug discovery and risk prediction across ancestries. Nature 611, 115–123 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Bakker, M. K. et al. Genome-wide association study of intracranial aneurysms identifies 17 risk loci and genetic overlap with clinical risk factors. Nat. Genet. 52, 1303–1313 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  104. Aragam, K. G. et al. Discovery and systematic characterization of risk variants and genes for coronary artery disease in over a million participants. Nat. Genet. 54, 1803–1815 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  105. Jiang, L., Zheng, Z., Fang, H. & Yang, J. A generalized linear mixed model association tool for biobank-scale data. Nat. Genet. 53, 1616–1621 (2021).

    Article  CAS  PubMed  Google Scholar 

  106. Traylor, M. et al. Genetic basis of lacunar stroke: a pooled analysis of individual patient data and genome-wide association studies. Lancet Neurol. 20, 351–361 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. Shah, S. et al. Genome-wide association and Mendelian randomisation analysis provide insights into the pathogenesis of heart failure. Nat. Commun. 11, 163 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  108. Christophersen, I. E. et al. Large-scale analyses of common and rare variants identify 12 new loci associated with atrial fibrillation. Nat. Genet. 49, 946–952 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. Nielsen, J. B. et al. Biobank-driven genomic discovery yields new insight into atrial fibrillation biology. Nat. Genet. 50, 1234–1239 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  110. Roselli, C. et al. Multi-ethnic genome-wide association study for atrial fibrillation. Nat. Genet. 50, 1225–1233 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. van der Harst, P. & Verweij, N. Identification of 64 novel genetic loci provides an expanded view on the genetic architecture of coronary artery disease. Circ. Res. 122, 433–443 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  112. Malik, R. et al. Multiancestry genome-wide association study of 520,000 subjects identifies 32 loci associated with stroke and stroke subtypes. Nat. Genet. 50, 524–537 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  113. Nauffal, V. et al. Genetics of myocardial interstitial fibrosis in the human heart and association with disease. Nat. Genet. 55, 777–786 (2023).

    Article  CAS  PubMed  Google Scholar 

  114. UK Biobank Neale laboratory. Rapid GWAS of thousands of phenotypes for 337,000 samples in the UK Biobank; http://www.nealelab.is/blog/2017/7/19/rapid-gwas-of-thousands-of-phenotypes-for-337000-samples-in-the-uk-biobank

Download references

Acknowledgements

This work was supported by the National Institutes of Health (NIH; R35HL161016) and the University of Michigan Frankel Cardiovascular Center M-BRISC program. S.K.G. was supported by the NIH (R01HL139672, R01HL122684, R01HL086694, R35HL161016), Department of Defense, and the University of Michigan A. Alfred Taubman Institute. X.Z. was supported by R01HG009124 and R35HL161016. M.-L.Y. was supported by R35HL161016. T.G. was supported by NIH T35 T35HL007690-38. We acknowledge the University of Michigan Precision Health Initiative and Medical School Central Biorepository for providing biospecimen storage, management, processing and distribution services and the Center for Statistical Genetics in the Department of Biostatistics at the School of Public Health for the MGI genotype data curation and management in support of this research. We acknowledge the GERA working group in the Kaiser Permanente Research Program for their valuable contribution to the replication of BP studies. We acknowledge the working groups that have contributed to each individual CVD study and made their data available through the GWAS catalog, CVDKP database or other publicly accessible sources. Special thanks go to the MEGASTROKE and GIGASTROKE consortia, with funding details available at http://www.megastroke.org/acknowledgments.html. The ISGC Intracranial Aneurysm working group, the CARDIoGRAMplusC4D Consortium and the CardioMetabolic Consortium CHD working group are acknowledged for their contributions to CAD/MI data. The GTEx Project was supported by the Common Fund of the Office of the Director of the NIH, and by NCI, NHGRI, NHLBI, NIDA, NIMH and NINDS. The GTEx data were obtained from the GTEx Portal and dbGaP (phs000424). We thank the participants in the UKB, GERA and MGI studies, as well as all the studies accessed for the analyses.

Author information

Authors and Affiliations

  1. Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan, Ann Arbor, MI, USA

    Min-Lee Yang, Trisha Gupte & Santhi K. Ganesh

  2. Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA

    Min-Lee Yang & Santhi K. Ganesh

  3. Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA

    Min-Lee Yang & Santhi K. Ganesh

  4. Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA

    Chang Xu & Xiang Zhou

  5. Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA

    Chang Xu & Xiang Zhou

  6. Department of Epidemiology & Biostatistics, and Institute for Human Genetics, School of Medicine, University of California, San Francisco, San Francisco, CA, USA

    Thomas J. Hoffmann

  7. Division of Research, Kaiser Permanente, Oakland, CA, USA

    Carlos Iribarren

Authors
  1. Min-Lee Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chang Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Trisha Gupte
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Thomas J. Hoffmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Carlos Iribarren
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xiang Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Santhi K. Ganesh
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

S.K.G., M.-L.Y., C.X. and X.Z. designed the experiments, interpreted the results and wrote the paper. C.X. and M.-L.Y. contributed to the UKB discovery stage GWAS analysis and summary, as well as the sensitivity and permutation analyses related to it. M.-L.Y. contributed to the MGI replication stage genetic association and PGS validation analysis. T.G. acquired the clinical data from MGI and interpreted the analysis result. T.J.H. and C.I. contributed to the GERA replication stage analysis. M.-L.Y. summarized analysis results and conducted the follow-up analyses under the supervision of X.Z. and S.K.G.

Corresponding author

Correspondence to Santhi K. Ganesh.

Ethics declarations

Competing interests

The authors declare no competing interests. The University of Michigan has filed for patents on generic risk tools for FMD and SCAD.

Peer review

Peer review information

Nature Medicine thanks the anonymous reviewers for their contribution to the peer review of this work. Primary Handling Editor: Michael Basson, in collaboration with the Nature Medicine team.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 BP gene-by-sex effects.

Volcano plots of LD-pruned (a) SBP, (b) DBP, (c) and PP loci are shown for: (i) all sex-specific trait-associated loci, (ii) unique sex-specific trait-associated loci identified by sex-stratified BP GWAS only, and (iii) loci from sex-dimorphic effects analysis (SDE P < 1 × 10−5). Each line represents a locus, connecting males-only (blue dots) and females-only (purple dots) GWAS results, based on linear mixed models adjusted with BP-associated covariates. The two-sided P values reported are unadjusted. Line length indicates differences in effect size or association strength, with horizontal differences showing directional discrepancies and vertical differences indicating significance magnitude. (d) LocusZoom plots for regional association results from females-only, males-only, and SDE DBP GWAS are shown, for chromosomes 13q34-COL4A1/COL4A2 and 19q13.2 ACTN4/CAPN12, with female-specific associations (Pfemale ≤ 5 × 10−8, Pmale > 5 × 10−8) and strong SDE effects (SDE PGC < 5 × 10−5), ±250 kb. 1000 G EUR LD reference was used.

Extended Data Fig. 2 DBP gene-sex interaction TWAS analysis identified COL4A1.

Manhattan plots show gene-based transcriptome-wide association studies (TWAS) for DBP using genotype-sex summary statistics from the GEM program (Fig. 3c) based on a linear mixed model adjusted with BP-associated covariates. MAGMA software was used to map all SNPs to 19,184 protein coding genes based on genomic locations. P-values presented are unadjusted and from two-tailed tests. The red dashed line indicates the genome-wide significance threshold at P = 2.61 × 10−6, defined as P = 0.05/19184. Results are shown for (a) gene-sex interaction analysis with a matched sample size between sexes from UKB (Nmale=145,375 and Nfemale = 143,102) (b) gene-sex interaction analysis included all available UKB samples (Nmale=166,379 and Nfemale = 193,523).

Extended Data Fig. 3 Comparison of colocalization findings at sex-specific BP loci.

(a) Arterial cis-eQTL-associated genes (eGenes) colocalized (PP.H4 > 0.5) with female-specific, male-specific, or non-sex-specific BP-associated loci are summarized with Venn diagrams. (b) Frequencies of arterial eGenes colocalized (PP.H4 > 0.5) with sex-specific BP-associated regions were compared to non-sex-specific BP-associated regions using a Fisher’s exact test; the table presents a summary of all BP loci, either sex-specific or non-sex-specific. (c) Arterial eGenes for sex-specific BP loci with sex-biased colocalization are shown; These loci specifically colocalize with the combined-sex eQTL data in only one sex of the GWAS data, indicated by pp.abf.H4 > 50% in one sex and pp.abf.H4 < 50% in the other sex. (d) eGenes for GWAS loci exhibiting sex-specific associations and sex-biased expression in arterial tissues, based on the GTEx v8 sex-biased genes database; heatmap values represent the largest effect size of differential expression by sex (F-M) in aorta, coronary artery, and tibial artery, indicating the magnitude of female-biased (red) or male-biased (blue) regulation for each gene. Male-specific associated loci and female-specific associated loci are denoted as 1 and 2, respectively. Strong functional candidate genes are labeled through colocalization of GWAS and eQTL in (e). O: pp.abf.H4 > 0.9. o: pp.abf.H4 > 0.75. c: pp.abf.H4 > 0.5.

Extended Data Fig. 4 TFBS enrichment at sex-specific BP-associated loci.

(a) Beeswarm plots show the top 10 ranked TFs based on TFBS enrichment tests of autosomal sex-specific BP loci using LOLA’s reference ChIP-Seq datasets (268 TFs out of 4166 datasets). LD-pruned GWAS loci associated with BP in females only (SBP: 86 loci, DBP: 183 loci, PP: 140 loci), males only (SBP: 37 loci, DBP: 44 loci, PP: 60 loci), and their combinations were tested using Unibind in a ±500 Kb window, comparing genomic locations of all arterial expressed genes as background. Each plot displays the distribution of enrichment test results for each TF. P values are unadjusted. The heatmap presents each of top 10 enriched TFs, scaling from 0 to 1 (highest enrichment), from these 12 separate tests of TFBS enrichment. (b) Beeswarm plots show the top 10 ranked TFs based on differential TFBS enrichment analyses within a 500Kb window, comparing sex-specific and non-sex-specific BP loci. P values are unadjusted.

Extended Data Fig. 5 Sex-specific epigenomic tissue enrichments.

ENCODE database’s human H3K27ac ChIP-seq experiments narrow peak data (N = 294), consisting of 158 females from 40 tissues, 131 males from 34 tissues, and 3 individuals of unknown sex, were utilized to examine the epigenomic features of GWAS loci associated with sex-specific BP traits. Results were in comparison to those from the combined-sex BP GWAS. (a) GWAS female-specific loci (SBP: 86 loci, DBP: 183 loci, PP: 140 loci), (b) male-specific loci (SBP: 37 loci, DBP: 44 loci, PP: 60 loci), and (c) all BP loci identified in the combined-sex samples (288 SBP loci, 505 DBP loci, 473 PP loci), were tested for their enrichment of open chromatin regions in tissues of the same sex. The GREGOR program (using EUR as the LD reference) was employed which was based on a non-parametric statistical method to assess the enrichment P value that represents the cumulative probability that the overlap with control SNPs surpasses that observed with GWAS index SNPs. P values are unadjusted. The best P value for each tissue was reported. Blue filled bars indicate enrichments that passed the Bonferroni correction P value threshold. F: female. M: male. O: unknown sex.

Extended Data Fig. 6 Predicting hypertension in MGI using sex-stratified polygenic scores.

(a) The AUC of ROC and Brier score were utilized to assess the PGS for SBP, DBP, and PP for hypertension (HTN) prediction. PGS’s were estimated for each MGI sample by combining risk scores of the top loci identified in the female-only (F), male-only (M), or combined-sex (C) UKB BP GWAS. Logistic regression models were trained on 80% of the data and tested on the remaining 20% using a total of 45,500 individuals from MGI (Nfemale=12,427 controls and 10,502 cases; Nmale = 8,691 controls and 11,880 cases). The analyses included PGSBP only model, or models with PGSBP and relevant covariates (age, age2, BMI, sex if applicable, and PC1-PC5) as variables to predict HTN in female or male individuals. (b) Forest plots illustrate odds ratios (OR) and minus log10(P) demonstrating the associations between PGS’s and HTN status (Nfemale=12,427 controls and 10,502 cases; Nmale = 8,691 controls and 11,880 cases). This analysis employed logistic regression with identical covariates and the same MGI samples as in (a). Sensitivity analyses were conducted by using only the top 50 loci from each GWAS to derive PGS’s. The middle points represent the mean odds ratios for each study, and the lines indicate the 95% confidence intervals (mean + /-1.96 SD) for these ratios. P values are unadjusted (two-tailed). (c) Proportion of variance in phenotype explained (PVE) by a given SNP. PVE was calculated based on all top LD-pruned loci identified in UKB female, male, or combined-sex BP GWAS association, or only the top 50 loci from each study.

Extended Data Fig. 7 Cardiovascular risk in high and low SBP polygenic score groups.

Samples in the top 10% PGSSBP groups were compared to the bottom 10% PGSSBP group for their hypertension (HTN), coronary artery disease (CAD), and stroke frequencies in UKB and MGI, using a Fisher’s exact test. Forest plots depict odds ratios (OR) and minus log10(P). UKB (Nfemale=174,664, Nfemale = 174,664 males; including sample sizes for HTN cases: Nfemale = 43,171, Nmale = 53,612; CAD cases: Nfemale = 2323, Nmale = 8471; stroke cases: Nfemale = 1831, Nmale = 3116) and MGI (Nfemale=23,921, Nmale = 21,077; including sample sizes for HTN cases: Nfemale = 11,171, Nmale = 12,265; CAD cases, Nfemale = 2089, Nmale = 3819; stroke cases: Nfemale = 980, Nmale = 1089). The middle points represent the mean odds ratios for each study, and the lines indicate the 95% confidence intervals (mean ± 1.96 SD) for these ratios. P values (wo-tailed) are unadjusted.

Extended Data Fig. 8 Cross-trait sex-specific colocalization of BP-associated loci with CVDs.

Loci identified in the analyses of SBP (a), DBP (b) and PP (c) with colocalization posterior probability >0.50 in only one sex and the differences in colocalization posterior probability between females and males >0.5, in a ±250 Kb window. The closest gene to the index SNP is shown for each region. Heatmap values reflect the differences (ranging from −1 to 1) in the posterior probabilities from the colocalization of each CVD trait and female-only BP associations, and the colocalization of each CVD trait and male-only BP associations. Blue and red colors represent regions with male-biased and female-biased colocalization, respectively. AS: any stroke. AIS: any ischemic stroke. LAS: large artery stroke. CES: cardioembolic stroke. SVS: small vessel stroke. ICA: intracranial aneurysms. SAH: subarachnoid hemorrhage. uIA: unruptured intracranial aneurysms; HTN: hypertension. CAD: Coronary artery disease. MI: myocardial infarction. FMD: fibromuscular dysplasia. PE: preeclampsia. HF: heart failure; AF: atrial fibrillation; AA: aortic aneurysm; AA.TAA: thoracic aortic aneurysm; AortaD.aa: thoracic aorta dimensions, ascending aortic diameter; AortaD.da: thoracic aorta dimensions, descending aortic diameter; SCAD: spontaneous coronary artery dissection.

Extended Data Fig. 9 Regional association plots of loci with female-specific colocalization signals.

LocusZoom plots are shown for the BP sex-stratified GWAS and FMD GWAS23, SCAD GWAS26, and PE GWAS22, using 1000 G EUR as LD reference (r2 > 0.2), in a ±250 kb window. P value presented are two-tailed and un-adjusted. (a) The chromosome 9p21 CDKN2B-AS1 region, with a lead SNP rs1333047 in PP females and a lead SNP rs2383206 in FMD (LD r2 = 0.9). (b) The 4q32.1 MAP9/GUCY1A1(GUCY1A3) region, with the same lead SNP, rs17033041, in PP in females and in FMD (c) The 21q22.11 LINC00310 region lead SNP rs9305545 in DBP in females and a lead SNP of rs28451064 in SCAD or FMD (LD r2 = 0.81). (d) The 4q21.21 FGF5 region lead SNP rs11099097 in PP in females and lead SNP rs13125101 in PE (LD r2 = 0.95 for these two SNPs).

Extended Data Fig. 10 Tissue gene expression of COL4A1 and COL4A2.

Gene expression of (a) COL4A1 and (b) COL4A2 was queried in GTEx v8 portal (dbGaP Accession phs000424.v8. p2) for all available human tissues. Expression values shown in transcripts per million (TPM) were calculated from a gene model with isoforms collapses to a single gene, for which box plots are shown as median, 25%, and 75%. Outliers are defined as points outside the 1.5x interquartile range. NAdipose - Subcutaneous = 663, NAdipose - Visceral = 541, NAdrenal Gland = 258, NArtery - Aorta = 432, NArtery - Coronary = 240, NArtery - Tibial = 663, NBladder = 21, NBrain - Amygdala = 152, NBrain - Anterior cingulate cortex = 176, NBrain - Caudate = 246, NBrain - Cerebellar Hemisphere = 215, NBrain - Cerebellum = 241, NBrain - Cortex = 255, NBrain - Frontal Cortex = 209, NBrain - Hippocampus = 197, NBrain- Hypothalamus - =202, NBrain - Nucleus accumbens (basal ganglia)=246, NBrain - Putamen (basal ganglia)=205, NBrain - Spinal cord (cervical c-1) = 159, NBrain - Substantia nigra = 139, NBreast - Mammary Tissue = 459, NCells - Cultured fibroblasts = 504, NCells - EBV-transformed lymphocytes = 174, NCervix - Ectocervix = 9, NCervix - Endocervix = 10, NColon - Sigmoid = 373, NColon - Transverse = 406, NEsophagus - Gastroesophageal Junction = 375, NEsophagus - Mucosa = 555, NEsophagus - Muscularis=515, NFallopian Tube = 9, NHeart - Atrial Appendage = 429, NHeart - Left Ventricle = 432, NKidney - Cortex = 85, NKidney - Medulla = 4, NLiver = 226, NLung = 578, NMinor Salivary Gland = 162, NMuscle - Skeletal = 803, NNerve - Tibial = 619, NOvary = 180, NPancreas = 328, NPituitary = 283, NProstate = 245, NSkin - Not Sun Exposed (Suprapubic)=604, NSkin - Sun Exposed (Lower leg)=701, NSmall Intestine - Terminal Ileum = 187, NSpleen = 241, NStomach = 359, NTestis = 361, NThyroid = 653, NUterus = 142, NVagina = 156, NWhole Blood = 755.

Supplementary information

Supplementary Information

Supplementary Figs. 1–9.

Reporting Summary

Supplementary Tables

Supplementary Tables 1–25.

Source data

Source Data Fig. 1

Source data for Fig. 1.

Source Data Fig. 2

Source data for Fig. 2.

Source Data Fig. 3

Source data for Fig. 3.

Source Data Fig. 4

Source data for Fig. 4.

Source Data Fig. 5

Source data for Fig. 5.

Source Data Table 1

Source data for Table 1.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yang, ML., Xu, C., Gupte, T. et al. Sex-specific genetic architecture of blood pressure. Nat Med 30, 818–828 (2024). https://doi.org/10.1038/s41591-024-02858-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41591-024-02858-2

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing