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:

Using human genetics to understand the disease impacts of testosterone in men and women

Nature Medicine volume 26, pages 252–258 (2020)Cite this article

Subjects

Abstract

Testosterone supplementation is commonly used for its effects on sexual function, bone health and body composition, yet its effects on disease outcomes are unknown. To better understand this, we identified genetic determinants of testosterone levels and related sex hormone traits in 425,097 UK Biobank study participants. Using 2,571 genome-wide significant associations, we demonstrate that the genetic determinants of testosterone levels are substantially different between sexes and that genetically higher testosterone is harmful for metabolic diseases in women but beneficial in men. For example, a genetically determined 1 s.d. higher testosterone increases the risks of type 2 diabetes (odds ratio (OR) = 1.37 (95% confidence interval (95% CI): 1.22–1.53)) and polycystic ovary syndrome (OR = 1.51 (95% CI: 1.33–1.72)) in women, but reduces type 2 diabetes risk in men (OR = 0.86 (95% CI: 0.76–0.98)). We also show adverse effects of higher testosterone on breast and endometrial cancers in women and prostate cancer in men. Our findings provide insights into the disease impacts of testosterone and highlight the importance of sex-specific genetic analyses.

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: Cluster analysis of male identified sex hormone signals.
Fig. 2: Cluster analysis of female identified sex hormone signals.
Fig. 3: Plots showing the odds of T2D and PCOS per unit higher testosterone and SHBG using genetic instruments in MR analyses.
Fig. 4: Plots showing the odds of cancer per unit higher testosterone and SHBG using genetic instruments in MR analyses.

Similar content being viewed by others

Data availability

All data used in discovery analyses are available from UK Biobank upon request (https://www.ukbiobank.ac.uk).

References

  1. Ding, E. L., Song, Y., Malik, V. S. & Liu, S. Sex differences of endogenous sex hormones and risk of type 2 diabetes: a systematic review and meta-analysis. JAMA 295, 1288–1299 (2006).

    CAS  PubMed  Google Scholar 

  2. Yao, Q.-M., Wang, B., An, X.-F., Zhang, J.-A. & Ding, L. Testosterone level and risk of type 2 diabetes in men: a systematic review and meta-analysis. Endocr. Connect. 7, 220–231 (2018).

    CAS  PubMed  Google Scholar 

  3. Bann, D. et al. Changes in testosterone related to body composition in late midlife: findings from the 1946 British birth cohort study. Obesity (Silver Spring) 23, 1486–1492 (2015).

    CAS  Google Scholar 

  4. Baillargeon, J., Kuo, Y.-F., Westra, J. R., Urban, R. J. & Goodwin, J. S. Testosterone prescribing in the United States, 2002–2016. JAMA 320, 200–202 (2018).

    PubMed  PubMed Central  Google Scholar 

  5. Handelsman, D. J. Global trends in testosterone prescribing, 2000–2011: expanding the spectrum of prescription drug misuse. Med. J. Aust. 199, 548–551 (2013).

    PubMed  Google Scholar 

  6. Bhasin, S. et al. Testosterone therapy in men with hypogonadism: an endocrine society clinical practice guideline. J. Clin. Endocrinol. Metab. 103, 1715–1744 (2018).

    PubMed  Google Scholar 

  7. Endogenous Hormones and Prostate Cancer Collaborative Group, Roddam, A. W., Allen, N. E., Appleby, P. & Key, T. J. Endogenous sex hormones and prostate cancer: a collaborative analysis of 18 prospective studies. J. Natl Cancer Inst. 100, 170–183 (2008).

    Google Scholar 

  8. Klap, J., Schmid, M. & Loughlin, K. R. The relationship between total testosterone levels and prostate cancer: a review of the continuing controversy. J. Urol. 193, 403–413 (2015).

    CAS  PubMed  Google Scholar 

  9. Watts, E. L. et al. Low free testosterone and prostate cancer risk: a collaborative analysis of 20 prospective studies. Eur. Urol. 74, 585–594 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Snyder, P. J. et al. Lessons from the testosterone trials. Endocr. Rev 39, 369–386 (2018).

    PubMed  PubMed Central  Google Scholar 

  11. Grossmann, M., Hoermann, R., Wittert, G. & Yeap, B. B. Effects of testosterone treatment on glucose metabolism and symptoms in men with type 2 diabetes and the metabolic syndrome: a systematic review and meta-analysis of randomized controlled clinical trials. Clin. Endocrinol. (Oxf.) 83, 344–351 (2015).

    CAS  Google Scholar 

  12. Huang, G. et al. Long-term testosterone administration on insulin sensitivity in older men with low or low-normal testosterone levels. J. Clin. Endocrinol. Metab. 103, 1678–1685 (2018).

    PubMed  PubMed Central  Google Scholar 

  13. Huang, G. et al. Testosterone dose-response relationships with cardiovascular risk markers in androgen-deficient women: a randomized, placebo-controlled trial. J. Clin. Endocrinol. Metab. 99, E1287–E1293 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Chan, K. J., Liang, J. J., Jolly, D., Weinand, J. D. & Safer, J. D. Exogenous testosterone does not induce or exacerbate the metabolic features associated with PCOS among transgender men. Endocr. Pract. 24, 565–572 (2018).

    PubMed  Google Scholar 

  15. Ding, E. L. et al. Sex hormone-binding globulin and risk of type 2 diabetes in women and men. N. Engl. J. Med. 361, 1152–1163 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Perry, J. R. B. et al. Genetic evidence that raised sex hormone binding globulin (SHBG) levels reduce the risk of type 2 diabetes. Hum. Mol. Genet. 19, 535–544 (2010).

    CAS  PubMed  Google Scholar 

  17. Ohlsson, C. et al. Genetic determinants of serum testosterone concentrations in men. PLoS Genet. 7, e1002313 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Eriksson, A. L. et al. Genetic determinants of circulating estrogen levels and evidence of a causal effect of estradiol on bone density in men. J. Clin. Endocrinol. Metab. 103, 991–1004 (2018).

    PubMed  PubMed Central  Google Scholar 

  19. Coviello, A. D. et al. A genome-wide association meta-analysis of circulating sex hormone-binding globulin reveals multiple loci implicated in sex steroid hormone regulation. PLoS Genet. 8, e1002805 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Ruth, K. S. et al. Genome-wide association study with 1000 genomes imputation identifies signals for nine sex hormone-related phenotypes. Eur. J. Hum. Genet. 24, 284–290 (2016).

    CAS  PubMed  Google Scholar 

  21. Chen, H. et al. Fkbp52 regulates androgen receptor transactivation activity and male urethra morphogenesis. J. Biol. Chem. 285, 27776–27784 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Odet, F., Verot, A. & Le Magueresse-Battistoni, B. The mouse testis is the source of various serine proteases and serine proteinase inhibitors (SERPINs): serine proteases and SERPINs identified in Leydig cells are under gonadotropin regulation. Endocrinology 147, 4374–4383 (2006).

    CAS  PubMed  Google Scholar 

  23. Mahajan, A. et al. Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps. Nat. Genet. 50, 1505–1513 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Conway, G. et al. The polycystic ovary syndrome: a position statement from the European Society of Endocrinology. Eur. J. Endocrinol. 171, P1–P29 (2014).

    CAS  PubMed  Google Scholar 

  25. Jayasena, C. N. et al. A systematic review of randomized controlled trials investigating the efficacy and safety of testosterone therapy for female sexual dysfunction in postmenopausal women. Clin. Endocrinol. (Oxf.) 90, 391–414 (2019).

    Google Scholar 

  26. Brand, J. S., van der Tweel, I., Grobbee, D. E., Emmelot-Vonk, M. H. & van der Schouw, Y. T. Testosterone, sex hormone-binding globulin and the metabolic syndrome: a systematic review and meta-analysis of observational studies. Int. J. Epidemiol. 40, 189–207 (2011).

    PubMed  Google Scholar 

  27. Rees, D. A. & Dayan, C. M. Commentary: testosterone and the metabolic syndrome: cause or consequence? Int. J. Epidemiol. 40, 207–209 (2011).

    PubMed  Google Scholar 

  28. Bycroft, C. et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 562, 203–209 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Vermeulen, A., Verdonck, L. & Kaufman, J. M. A critical evaluation of simple methods for the estimation of free testosterone in serum. J. Clin. Endocrinol. Metab. 84, 3666–3672 (1999).

    CAS  PubMed  Google Scholar 

  30. Chung, M. C., Gombar, S. & Shi, R. Z. Implementation of automated calculation of free and bioavailable testosterone in epic beaker laboratory information system. J. Pathol. Inform. 8, 28 (2017).

    PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Day, F. R., Loh, P.-R., Scott, R. A., Ong, K. K. & Perry, J. R. B. A robust example of collider bias in a genetic association study. Am. J. Hum. Genet. 98, 392–393 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Day, N. et al. EPIC-Norfolk: study design and characteristics of the cohort. European Prospective Investigation of Cancer. Br. J. Cancer 80(Suppl 1), 95–103 (1999).

    PubMed  Google Scholar 

  34. Yang, J., Lee, S. H., Goddard, M. E. & Visscher, P. M. GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88, 76–82 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Metsalu, T. & Vilo, J. ClustVis: a web tool for visualizing clustering of multivariate data using principal component analysis and heatmap. Nucleic Acids Res. 43, W566–W570 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Zhu, Z. et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat. Genet. 48, 481–487 (2016).

    CAS  PubMed  Google Scholar 

  37. Finucane, H. K. et al. Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types. Nat. Genet. 50, 621–629 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Ganna, A. et al. Large-scale GWAS reveals insights into the genetic architecture of same-sex sexual behavior. Science 365, eaat7693 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Bowden, J., Davey Smith, G. & Burgess, S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int. J. Epidemiol. 44, 512–525 (2015).

    PubMed  PubMed Central  Google Scholar 

  40. Bowden, J., Davey Smith, G., Haycock, P. C. & Burgess, S. Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator. Genet. Epidemiol. 40, 304–314 (2016).

    PubMed  PubMed Central  Google Scholar 

  41. Haas, M. E. et al. Genetic association of albuminuria with cardiometabolic disease and blood pressure. Am. J. Hum. Genet. 103, 461–473 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Kemp, J. P. et al. Identification of 153 new loci associated with heel bone mineral density and functional involvement of GPC6 in osteoporosis. Nat. Genet. 49, 1468–1475 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Jones, S. E. et al. Genome-wide association analyses of chronotype in 697,828 individuals provides insights into circadian rhythms. Nat. Commun. 10, 343 (2019).

    PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This research has been conducted using the UK Biobank resource under application numbers 9072, 9055, 9797 and 44448. The authors acknowledge the use of the University of Exeter High-Performance Computing facility in carrying out this work. We thank K. Patel, R. Andrews and T. McDonald for their advice on the derivation of testosterone measures and their roles in PCOS and diabetes. We thank the MAGIC consortium and I. Barroso for sharing prepublication sex-specific glycemic trait GWAS data. A.R.W. and T.M.F. are supported by the European Research Council grant no. SZ-245 50371-GLUCOSEGENES-FP7-IDEAS-ERC. R.N.B. is funded by the Wellcome Trust and Royal Society grant no. 104150/Z/14/Z. J.T. is supported by the Academy of Medical Sciences Springboard award which is supported by the Wellcome Trust and GCRF (grant no. SBF004\1079). This work was supported by the Medical Research Council (Unit Programme numbers MC_UU_12015/1 and MC_UU_12015/2).

Author information

Author notes

  1. Mark I McCarthy

    Present address: Genentech, San Francisco, CA, USA

  2. These authors contributed equally: Katherine S. Ruth, Felix R. Day, Jessica Tyrrell, Deborah J. Thompson, Anna Murray, Ken K. Ong, Timothy M. Frayling, John R. B. Perry.

  3. A list of members and affiliations appears in the Supplementary Information.

Authors and Affiliations

  1. University of Exeter Medical School, University of Exeter, Exeter, UK

    Katherine S Ruth, Jessica Tyrrell, Andrew R Wood, Robin N Beaumont, Susan Martin, Anna Murray & Timothy M Frayling

  2. Medical Research Council (MRC) Epidemiology Unit, University of Cambridge, Cambridge, UK

    Felix R Day, Laura Wittemans, Alexander S. Busch, A. Mesut Erzurumluoglu, Benjamin Hollis, Claudia Langenberg, Nicholas J Wareham, Ken K Ong & John R. B. Perry

  3. Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK

    Deborah J Thompson & Douglas F Easton

  4. Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK

    Anubha Mahajan, Laura Wittemans & Mark I McCarthy

  5. International Center for Research and Research Training in Endocrine Disruption of Male Reproduction and Child Health, University of Copenhagen, Copenhagen, Denmark

    Alexander S. Busch

  6. Department of Growth and Reproduction, University of Copenhagen, Copenhagen, Denmark

    Alexander S. Busch

  7. Department of Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia

    Tracy A. O’Mara

  8. Oxford Centre for Diabetes, University of Oxford, Oxford, UK

    Mark I McCarthy

  9. Oxford NIHR Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK

    Mark I McCarthy

  10. MRC Biostatistics Unit, University of Cambridge, Cambridge, UK

    Stephen Burgess

  11. BHF Cardiovascular Epidemiology Unit, University of Cambridge, Cambridge, UK

    Stephen Burgess

  12. Department of Paediatrics, University of Cambridge, Cambridge, UK

    Ken K Ong

Authors
  1. Katherine S Ruth
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Felix R Day
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jessica Tyrrell
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Deborah J Thompson
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Andrew R Wood
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Anubha Mahajan
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Robin N Beaumont
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Laura Wittemans
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Susan Martin
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Alexander S. Busch
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. A. Mesut Erzurumluoglu
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Benjamin Hollis
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Tracy A. O’Mara
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Mark I McCarthy
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Claudia Langenberg
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Douglas F Easton
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Nicholas J Wareham
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Stephen Burgess
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Anna Murray
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Ken K Ong
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Timothy M Frayling
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. John R. B. Perry
    View author publications

    You can also search for this author in PubMed Google Scholar

Consortia

The Endometrial Cancer Association Consortium

Contributions

K.S.R., F.R.D., J.T., D.J.T., J.R.B.P. and T.M.F. analyzed the data. K.S.R., F.R.D., J.T., J.R.B.P., T.M.F., K.K.O. and A. Murray drafted the manuscript. D.J.T., A.R.W., A. Mahajan, R.N.B., L.W., S.M., A.S.B., A.M.E., B.H., T.A.O’M., M.I.M., C.L., D.F.E., N.J.W., S.B. and the ECA consortium contributed data and advised on analysis. J.R.B.P., T.M.F., K.K.O., A. Murray and S.B. designed and led the study. All co-authors commented on and revised the manuscript.

Corresponding authors

Correspondence to Timothy M Frayling or John R. B. Perry.

Ethics declarations

Competing interests

The views expressed in this article are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health. M.I.M. has served on advisory panels for Pfizer, NovoNordisk and Zoe Global, and has received honoraria from Merck, Pfizer, Novo Nordisk and Eli Lilly, and research funding from Abbvie, Astra Zeneca, Boehringer Ingelheim, Eli Lilly, Janssen, Merck, NovoNordisk, Pfizer, Roche, Sanofi Aventis, Servier and Takeda. As of June of 2019, M.I.M. is an employee of Genentech, and a holder of Roche stock. T.M.F. holds an MRC CASE studentship with GSK and has consulted for Sanofi, Servier and Boerhinger Ingelheim.

Additional information

Peer review information Jennifer Sargent was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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

Extended data

Extended Data Fig. 1 Replication of identified SHBG signals in CHARGE meta-analysis.

a) Effect size comparison performed against published estimates from the CHARGE male SHBG meta-analysis (N = 12,401). b) Effect size comparison performed against published estimates from the CHARGE female SHBG meta-analysis (N = 9,390). The SHBG cis locus (which had a concordant effect direction) has been excluded to maintain an appropriate scale.

Extended Data Fig. 2 Relationship between measured sex hormone levels in the EPIC-Norfolk study and polygenic score for increased sex hormone level.

a) Total testosterone levels in the EPIC-Norfolk study by polygenic score for increased total testosterone (n = 5,334 men; n = 3,804 women). b) SHBG levels in the EPIC-Norfolk study by polygenic score for increased SHBG (n = 5,694 men; n = 5,476 women). Bars denote the standard error around the point estimate of the mean. Effect on hormone is given in standard deviations (SDs). SHBG = sex hormone binding globulin.

Extended Data Fig. 3 LD score regression analysis of enrichment of sex hormone signals in 53 GTEx tissues and cell types.

a) Analysis in men. b) Analysis in women.

Extended Data Fig. 4 Results of inverse-variance weighted Mendelian randomization analysis of sex hormone genetic instruments on metabolic traits and body composition outcomes.

Dot plots representing the change in the following metabolic outcomes and body composition traits in males and females per unit higher sex hormone: a) Total lean mass. b) Total fat mass. c) BMI. d) Waist-hip ratio adjusted for BMI. e) Fasting insulin. f) Fasting glucose. g) Type 2 diabetes. Bars indicate 95% confidence interval around the point estimate from inverse-variance weighted analysis. Analyses are based on association statistics generated in a maximum of: total and specific testosterone, n = 194,453 men and n = 230,454 women; bioavailable testosterone, n = 178,782 men and n = 188,507 women; SHBG, n = 180,726 men and n = 189,473 women; total lean mass and total fat mass, n = 9,102 men and n = 10,406 women; BMI, n = 152,893 men and n = 171,977 women; WHR adjusted for BMI, n = 93,480 men and n = 116,742 women; fasting insulin, n = 47,806 men and n = 50,404 women; fasting glucose, n = 67,506 men and n = 73,089 women; T2D, n = 34,990 cases and n = 150,760 controls in men and n = 17,790 cases and n = 243,645 controls in women. Numbers of genetic variants included in the analyses are given in Supplementary Table 20, 21, 23 and 26. BMI = body mass index; SHBG = sex hormone binding globulin; T = testosterone; T Specific = testosterone cluster; WHR = waist-hip ratio.

Extended Data Fig. 5 Results of Mendelian randomization analysis in men of genetic instruments for testosterone and SHBG on the outcome of Type 2 diabetes.

Plots show effect on ln(odds) of Type 2 diabetes (y axes) in men of the following sex hormone genetic instruments (x axes; effect size in units). a) Total testosterone. b) Steiger filtered total testosterone. c) Bioavailable testosterone. d) Steiger filtered bioavailable testosterone. e) Testosterone specific cluster. f) Steiger filtered testosterone specific cluster. g) SHBG. h) Steiger filtered SHBG. i) SHBG specific cluster. j) Steiger filtered SHBG specific cluster. P-values and effect size estimates (indicated by lines) are from Egger (pink), IVW (blue), and median IV (red) Mendelian randomization analyses. Bars indicate 95% confidence interval around the point estimate for each genetic variant. Analyses are based on association statistics generated in a maximum of: total testosterone (including specific and Steiger filtered), n = 194,453; bioavailable testosterone (including Steiger filtered), n = 178,782; SHBG (including specific and Steiger filtered), n = 180,726; T2D, n = 34,990 cases and n = 150,760 controls. Numbers of genetic variants included in the analyses are given in Supplementary Table 20. SHBG = sex hormone binding globulin.

Extended Data Fig. 6 Results of inverse-variance weighted Mendelian randomization analysis of sex hormone genetic instruments on cancer outcomes.

Dot plots representing the change in the odds of the following cancers per unit higher sex hormone in males or females, as appropriate. a) Prostate cancer in males. b) Breast cancer (all types) and estrogen receptor positive (ER + ) and negative (ER-) subtypes in females. c) Endometrial cancer in females. d) Ovarian cancer in females. Bars indicate 95% confidence interval around the point estimate from inverse-variance weighted analyses. Analyses are based on association statistics generated in a maximum of: total and specific testosterone, n = 194,453 men and n = 230,454 women; bioavailable testosterone, n = 178,782 men and n = 188,507 women; SHBG, n = 180,726 men and n = 189,473 women; estradiol, n = 206,927 men; prostate cancer, 67,158 cases and 48,350 controls; breast cancer, n = 105,974 cases and n = 122,977 controls; ER negative subtype breast cancer, n = 21,468 cases and n = 100,594 controls; ER positive subtype breast cancer, n = 69,501 cases and n = 95,039 controls; endometrial cancer, n = 12,270 cases and n = 46,126 controls; ovarian cancer, n = 25,509 cases and n = 40,941 controls. Numbers of genetic variants included in the analyses are given in Supplementary Table 25 and 27. SHBG = sex hormone binding globulin; T = testosterone; T Specific = testosterone cluster.

Extended Data Fig. 7 Results of inverse-variance weighted Mendelian randomization analysis in females of sex hormone genetic instruments on PCOS.

Dot plot represents the odds of PCOS per unit higher sex hormone. Bars indicate 95% confidence interval around the point estimate from inverse-variance weighted analyses. Analyses are based on association statistics generated in a maximum of: total and specific testosterone, n = 230,454; bioavailable testosterone, n = 188,507; SHBG, n = 189,473; PCOS, n = 10,074 cases and n = 103,164 controls. Numbers of genetic variants included in the analyses are given in Supplementary Table 21. PCOS = polycystic ovary syndrome; SHBG = sex hormone binding globulin; T = testosterone; T Specific = testosterone cluster.

Extended Data Fig. 8 Results of Mendelian randomization analysis in women of genetic instruments for testosterone and SHBG on the outcome of PCOS.

Plots show effect on ln(odds) of PCOS (y axes) of the following sex hormone genetic instruments in women (x axes; effect size in units). a) Total testosterone. b) Steiger filtered total testosterone. c) Bioavailable testosterone. d) Steiger filtered bioavailable testosterone. e) Testosterone specific cluster. f) Steiger filtered testosterone specific cluster. g) SHBG. h) Steiger filtered SHBG. i) SHBG specific cluster. j) Steiger filtered SHBG specific cluster. P-values and effect size estimates (indicated by lines) are from Egger (pink), IVW (blue), and median IV (red) Mendelian randomization analyses. Bars indicate 95% confidence interval around the point estimate for each genetic variant. Analyses are based on association statistics generated in a maximum of: total testosterone (including specific and Steiger filtered), n = 230,454; bioavailable testosterone (including Steiger filtered), n = 188,507; SHBG (including specific and Steiger filtered), n = 189,473; PCOS, n = 10,074 cases and n = 103,164 controls. Numbers of genetic variants included in the analyses are given in Supplementary Table 21. PCOS = polycystic ovary syndrome; SHBG = sex hormone binding globulin.

Extended Data Fig. 9 Results of Mendelian randomization analysis in women of genetic instruments for testosterone and SHBG on the outcome of Type 2 diabetes.

Plots show effect on ln(odds) of Type 2 diabetes in women (y axes) of the following sex hormone genetic instruments in women (x axes; effect size in units). a) Total testosterone. b) Steiger filtered total testosterone. c) Bioavailable testosterone. d) Steiger filtered bioavailable testosterone. e) Testosterone specific cluster. f) Steiger filtered testosterone specific cluster. g) SHBG. h) Steiger filtered SHBG. i) SHBG specific cluster. j) Steiger filtered SHBG specific cluster. P-values and effect size estimates (indicated by lines) are from Egger (pink), IVW (blue), and median IV (red) Mendelian randomization analyses. Bars indicate 95% confidence interval around the point estimate for each genetic variant. Analyses are based on association statistics generated in a maximum of: total testosterone (including specific and Steiger filtered), n = 230,454; bioavailable testosterone (including Steiger filtered), n = 188,507; SHBG (including specific and Steiger filtered), n = 189,473; T2D, n = 17,790 cases and n = 243,645 controls. Numbers of genetic variants included in the analyses are given in Supplementary Table 21. SHBG = sex hormone binding globulin.

Supplementary information

Supplementary Information

Additional author information

Reporting Summary

Supplementary Tables

Supplementary Tables 1–29

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ruth, K.S., Day, F.R., Tyrrell, J. et al. Using human genetics to understand the disease impacts of testosterone in men and women. Nat Med 26, 252–258 (2020). https://doi.org/10.1038/s41591-020-0751-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41591-020-0751-5

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer