Sex differences have been shown in laboratory biomarkers; however, the extent to which this is due to genetics is unknown. In this study, we infer sex-specific genetic parameters (heritability and genetic correlation) across 33 quantitative biomarker traits in 181,064 females and 156,135 males from the UK Biobank study. We apply a Bayesian Mixture Model, Sex Effects Mixture Model (SEMM), to Genome-wide Association Study summary statistics in order to (1) estimate the contributions of sex to the genetic variance of these biomarkers and (2) identify variants whose statistical association with these traits is sex-specific. We find that the genetics of most biomarker traits are shared between males and females, with the notable exception of testosterone, where we identify 119 female and 445 male-specific variants. These include protein-altering variants in steroid hormone production genes (POR, UGT2B7). Using the sex-specific variants as genetic instruments for Mendelian randomization, we find evidence for causal links between testosterone levels and height, body mass index, waist and hip circumference, and type 2 diabetes. We also show that sex-specific polygenic risk score models for testosterone outperform a combined model. Overall, these results demonstrate that while sex has a limited role in the genetics of most biomarker traits, sex plays an important role in testosterone genetics.
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SEMM is publicly available as an R package on GitHub at https://github.com/rivas-lab/semm; additional scripts used in the analysis are included at https://github.com/rivas-lab/sex-diff-biomarker-genetics.
Ober C, Loisel DA, Gilad Y. Sex-specific genetic architecture of human disease. Nat Rev Genet. 2008;9:911–22.
Winkler TW, Justice AE, Graff M, Barata L, Feitosa MF, Chu S, et al. The influence of age and sex on genetic associations with adult body size and shape: a large-scale genome-wide interaction study. PLoS Genet. 2015;11:e1005378.
Khramtsova EA, Davis LK, Stranger BE. The role of sex in the genomics of human complex traits. Nat Rev Genet. 2019;20:173–90.
Rask-Andersen M, Karlsson T, Ek WE, Johansson Å. Genome-wide association study of body fat distribution identifies adiposity loci and sex-specific genetic effects. Nat Commun. 2019;10:339.
Bonfiglio F, Zheng T, Garcia-Etxebarria K, Hadizadeh F, Bujanda L, Bresso F, et al. Female-specific association between variants on chromosome 9 and self-reported diagnosis of irritable bowel syndrome. Gastroenterology. 2018;155:168–79.
Ostrom QT, Kinnersley B, Wrensch MR, Eckel-Passow JE, Armstrong G, Rice T, et al. Sex-specific genome-wide association study in glioma identifies new risk locus at 3p21.31 in females, and finds sex-differences in risk at 8q24.21. Scientific Reports. 2017;229112. https://doi.org/10.1101/229112.
Sinnott-Armstrong N, Tanigawa Y, Amar D, Mars NJ, Aguirre M, Venkataraman GR, et al. Genetics of 38 blood and urine biomarkers in the UK Biobank. bioRxiv. 2019; https://doi.org/10.1101/660506.
Yang J, Zeng J, Goddard ME, Wray NR, Visscher PM. Concepts, estimation and interpretation of SNP-based heritability. Nat Genet. 2017;49:1304–10.
Bulik-Sullivan B. Relationship between LD score and Haseman-Elston regression. bioRxiv; 2015. https://doi.org/10.1101/018283.
Hill WG. Estimation of heritability by regression using collateral relatives: linear heritability estimation. Genetical Res. 1978;32:265–74.
Ni G, Moser G, Schizophrenia Working Group of the Psychiatric Genomics Consortium, Wray NR, Lee SH. Estimation of genetic correlation via linkage disequilibrium score regression and genomic restricted maximum likelihood. Am J Hum Genet. 2018;102:1185–94.
Speed D, Balding DJ. SumHer better estimates the SNP heritability of complex traits from summary statistics. Nat Genet. 2019;51:277–84.
Sudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J, 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. 2015;12:e1001779.
Stringer S, Polderman TJC, Posthuma D. Author correction: majority of human traits do not show evidence for sex-specific genetic and environmental effects. Sci Rep. 2018;8:18060.
Rawlik K, Canela-Xandri O, Tenesa A. Evidence for sex-specific genetic architectures across a spectrum of human complex traits. Genome Biol. 2016;17:166.
Bycroft C, Freeman C, Petkova D, Band G, Elliott LT, Sharp K, et al. The UK Biobank resource with deep phenotyping and genomic data. Nature. 2018;562:203–9.
DeBoever C, Tanigawa Y, Lindholm ME, McInnes G, Lavertu A, Ingelsson E, et al. Medical relevance of protein-truncating variants across 337,205 individuals in the UK Biobank study. Nat Commun. 2018;9:1612.
Tanigawa Y, Li J, Justesen JM, Horn H, Aguirre M, DeBoever C, et al. Components of genetic associations across 2,138 phenotypes in the UK Biobank highlight novel adipocyte biology. Nat Commun. 2019;10:4064.
Hemani G, Zheng J, Elsworth B, Wade KH, Haberland V, Baird D, et al. The MR-Base platform supports systematic causal inference across the human phenome. Elife. 2018;7:e34408.
Shungin D, Winkler TW, Croteau-Chonka DC, Ferreira T, Locke AE, Mägi R, et al. New genetic loci link adipose and insulin biology to body fat distribution. Nature. 2015;518:187–96.
Wood AR, Esko T, Yang J, Vedantam S, Pers TH, Gustafsson S, et al. Defining the role of common variation in the genomic and biological architecture of adult human height. Nat Genet. 2014;46:1173–86.
Randall JC, Winkler TW, Kutalik Z, Berndt SI, Jackson AU, Monda KL, et al. Sex-stratified genome-wide association studies including 270,000 individuals show sexual dimorphism in genetic loci for anthropometric traits. PLoS Genet. 2013;9:e1003500.
Locke AE, Kahali B, Berndt SI, Justice AE, Pers TH, Day FR, et al. Genetic studies of body mass index yield new insights for obesity biology. Nature. 2015;518:197–206.
Perry JR, Day F, Elks CE, Sulem P, Thompson DJ, Ferreira T, et al. Parent-of-origin-specific allelic associations among 106 genomic loci for age at menarche. Nature. 2014;514:92–97.
Day FR, Ruth KS, Thompson DJ, Lunetta KL, Pervjakova N, Chasman DI, et al. Large-scale genomic analyses link reproductive aging to hypothalamic signaling, breast cancer susceptibility and BRCA1-mediated DNA repair. Nat Genet. 2015;47:1294–303.
Schumacher FR, Al Olama AA, Berndt SI, Benlloch S, Ahmed M, Saunders EJ, et al. Association analyses of more than 140,000 men identify 63 new prostate cancer susceptibility loci. Nat Genet. 2018;50:928–36.
CARDIoGRAMplusC4D Consortium, Deloukas P, Kanoni S, Willenborg C, Farrall M, Assimes TL, et al. Large-scale association analysis identifies new risk loci for coronary artery disease. Nat Genet. 2013;45:25–33.
Morris AP, Voight BF, Teslovich TM, Ferreira T, Segrè AV, Steinthorsdottir V, et al. Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nat Genet. 2012;44:981–90.
Malik R, Traylor M, Pulit SL, Bevan S, Hopewell JC, Holliday EG, et al. Low-frequency and common genetic variation in ischemic stroke: the METASTROKE collaboration. Neurology. 2016;86:1217–26.
Kelemen LE, Atkinson EJ, de Andrade M, Shane Pankratz V, Cunningham JM, Wang A, et al. Linkage analysis of obesity phenotypes in pre- and post-menopausal women from a United States mid-western population. BMC Med Genet. 2010;11:156.
Ohlsson C, Wallaschofski H, Lunetta KL, Stolk L, Perry JRB, Koster A, et al. Genetic determinants of serum testosterone concentrations in men. PLoS Genet. 2011;7:e1002313.
Xu X, Wells AB, O’Brien DR, Nehorai A, Dougherty JD. Cell type-specific expression analysis to identify putative cellular mechanisms for neurogenetic disorders. J Neurosci. 2014;34:1420–31.
Stabej LeQuesne, Williams P, James HJ, Tekman C, Stanescu M, Kleta HC, et al. STAG3 truncating variant as the cause of primary ovarian insufficiency. Eur J Hum Genet. 2016;24:135–8.
Idkowiak J, Cragun D, Hopkin RJ, Arlt W. Cytochrome P450 oxidoreductase deficiency. In: Adam MP, Ardinger HH, Pagon RA, Wallace SE, Bean LJH, Stephens K, et al., editors. GeneReviews®. Seattle: University of Washington; 2005.
Luo S, Au Yeung SL, Zhao JV, Burgess S, Schooling CM. Association of genetically predicted testosterone with thromboembolism, heart failure, and myocardial infarction: mendelian randomisation study in UK Biobank. BMJ. 2019;364:l476.
Zhao JV, Lam TH, Jiang C, Cherny SS, Liu B, Cheng KK, et al. A Mendelian randomization study of testosterone and cognition in men. Sci Rep. 2016;6:21306.
Eriksson J, Haring R, Grarup N, Vandenput L, Wallaschofski H, Lorentzen E, et al. Causal relationship between obesity and serum testosterone status in men: a bi-directional mendelian randomization analysis. PLoS One. 2017;12:e0176277.
Bowden J, Del Greco MF, Minelli C, Davey Smith G, Sheehan N, Thompson J. A framework for the investigation of pleiotropy in two-sample summary data Mendelian randomization. Stat Med. 2017;36:1783–802.
Handelsman DJ, Yeap B, Flicker L, Martin S, Wittert GA, Ly LP. Age-specific population centiles for androgen status in men. Eur J Endocrinol. 2015;173:809–17.
Antonio L, Wu FCW, O’Neill TW, Pye SR, Carter EL, Finn JD, et al. Associations between sex steroids and the development of metabolic syndrome: a longitudinal study in European men. J Clin Endocrinol Metab. 2015;100:1396–404.
Qian J, Du W, Tanigawa Y, Aguirre M, Tibshirani R, Rivas MA, et al. A fast and flexible algorithm for solving the Lasso in large-scale and ultrahigh-dimensional problems. bioRxiv. 2019; https://doi.org/10.1101/630079.
Yim JY, Kim J, Kim D, Ahmed A. Serum testosterone and non-alcoholic fatty liver disease in men and women in the US. Liver Int. 2018;38:2051–9.
Zachmann M, Ferrandez A, Mürset G, Gnehm HE, Prader A. Testosterone treatment of excessively tall boys. J Pediatr. 1976;88:116–23.
Ruth KS, Day FR, Tyrrell J, Thompson DJ, Wood AR, Mahajan A, et al. Using human genetics to understand the disease impacts of testosterone in men and women. Nat Med. 2020;26:252–8.
McInnes G, Tanigawa Y, DeBoever C, Lavertu A, Olivieri JE, Aguirre M, et al. Global Biobank Engine: enabling genotype-phenotype browsing for biobank summary statistics. Bioinformatics. 2019;35:2495–7.
Haring R, Baumeister SE, Völzke H, Dörr M, Felix SB, Kroemer HK, et al. Prospective association of low total testosterone concentrations with an adverse lipid profile and increased incident dyslipidemia. Eur J Cardiovasc Prev Rehabil. 2011;18:86–96.
Kim JJ, Kim D, Yim JY, Kang JH, Han KH. Polycystic ovary syndrome with hyperandrogenism as a risk factor for non-obese non-alcoholic fatty liver disease. Aliment Pharmacol Ther. 2017;45:1403–12.
Prescott J, Thompson DJ, Kraft P, Chanock SJ, Audley T, Brown J, et al. Genome-wide association study of circulating estradiol, testosterone, and sex hormone-binding globulin in postmenopausal women. PLoS One. 2012;7:e37815.
Cirillo D, Catuara-Solarz S, Morey C, Guney E, Subirats L, Mellino S, et al. Sex and gender differences and biases in artificial intelligence for biomedicine and healthcare. npj Digit Med. 2020;3:81.
This research has been conducted using the UK Biobank resource. We thank all the participants in the UK Biobank study. The primary and processed data used to generate the analyses presented here are available in the UK Biobank access management system (https://amsportal.ukbiobank.ac.uk/) for application 24983, “generating effective therapeutic hypotheses from genomic and hospital linkage data” (http://www.ukbiobank.ac.uk/wp-content/uploads/2017/06/24983-Dr-Manuel-Rivas.pdf). We would like to thank Stanford University and the Stanford Research Computing Center for providing computational resources and support that contributed to these research results.
EF is supported by the NIH NLM F31 Fellowship F31LM013053 and the Stanford Data Science program. RBA is supported by the NIH GM 102365, LM 005652, and the Chan Zuckerberg Biohub. YT is supported by a Funai Overseas Scholarship from the Funai Foundation for Information Technology and School of Medicine at Stanford University. FR is supported by a career development award from the National Heart, Lung, and Blood Institute, National Institutes of Health (1K01HL144607). NS-A is supported by a Stanford Graduate Fellowship. MAR is supported by National Human Genome Research Institute of the National Institutes of Health (R01HG010140) and the National Institute of Health Center for Multi- and Trans-Ethnic Mapping of Mendelian and Complex Diseases Grant (5U01 HG009080).
Conflict of interest
The authors filed a provisional patent application No. 62/925,133 titled “Genetic Determination of Hormone Levels and Applications Thereof.” The authors declare that they have no conflict of interest.
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Flynn, E., Tanigawa, Y., Rodriguez, F. et al. Sex-specific genetic effects across biomarkers. Eur J Hum Genet (2020). https://doi.org/10.1038/s41431-020-00712-w