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.

Identification of 371 genetic variants for age at first sex and birth linked to externalising behaviour

A Publisher Correction to this article was published on 28 July 2021

This article has been updated

Abstract

Age at first sexual intercourse and age at first birth have implications for health and evolutionary fitness. In this genome-wide association study (age at first sexual intercourse, N = 387,338; age at first birth, N = 542,901), we identify 371 single-nucleotide polymorphisms, 11 sex-specific, with a 5–6% polygenic score prediction. Heritability of age at first birth shifted from 9% [CI = 4–14%] for women born in 1940 to 22% [CI = 19–25%] for those born in 1965. Signals are driven by the genetics of reproductive biology and externalising behaviour, with key genes related to follicle stimulating hormone (FSHB), implantation (ESR1), infertility and spermatid differentiation. Our findings suggest that polycystic ovarian syndrome may lead to later age at first birth, linking with infertility. Late age at first birth is associated with parental longevity and reduced incidence of type 2 diabetes and cardiovascular disease. Higher childhood socioeconomic circumstances and those in the highest polygenic score decile (90%+) experience markedly later reproductive onset. Results are relevant for improving teenage and late-life health, understanding longevity and guiding experimentation into mechanisms of infertility.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Change in AFS and AFB over time, heritability by birth cohort and PGS prediction.
Fig. 2: Variance explained from PGSs for AFB and AFS using different methods in out-of-sample cohorts.
Fig. 3: Genetic correlations of AFB and AFS with a selection of related traits.
Fig. 4: MR of years of education on T2D and CAD adjusted for AFB and AFS.
Fig. 5: Gene prioritization of AFS and AFB.
Fig. 6: Summary GWAS of timing of onset of reproductive behaviour: AFS and AFB.

Data availability

Our policy is to make genome-wide summary statistics widely and publically available. Upon publication, summary statistics will be available on the GWAS catalogue website: https://www.ebi.ac.uk/gwas/downloads/summary-statistics.

The phenotype and genotype data for separate studies used in this GWAS are available upon application to each of the participating cohorts, who can be contacted directly to follow their different data access policies. Access to the UK Biobank is available through application with information available at: http://www.ukbiobank.ac.uk.

Code availability

No custom code was used, with all analyses and modelling using standard software as described in the Methods section and in detail in the Supplementary Information.

Change history

References

  1. 1.

    Mercer, C. H. et al. Changes in sexual attitudes and lifestyles in Britain through the life course and over time: findings from the National Surveys of Sexual Attitudes and Lifestyles (Natsal). Lancet 382, 1781–1794 (2013).

    PubMed  PubMed Central  Google Scholar 

  2. 2.

    Lara, L. A. S. & Abdo, C. H. N. Age at time of initial sexual intercourse and health of adolescent girls. J. Pediatr. Adolesc. Gynecol. 29, 417–423 (2016).

    PubMed  PubMed Central  Google Scholar 

  3. 3.

    Polimanti, R. et al. The interplay between risky sexual behaviors and alcohol dependence: genome-wide association and neuroimaging support for LHPP as a risk gene. Neuropsychopharmacology 42, 598–605 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. 4.

    Karlsson Linnér, R. et al. Genome-wide association analyses of risk tolerance and risky behaviors in over 1 million individuals identify hundreds of loci and shared genetic influences. Nat. Genet. 51, 245–257 (2019).

    PubMed  PubMed Central  Google Scholar 

  5. 5.

    Balbo, N., Billari, F. C. & Mills, M. Fertility in advanced societies: a review of research. Eur. J. Popul./Rev. Eur. Démographie 29, 1–38 (2013).

    Google Scholar 

  6. 6.

    Mills, M. C. et al. Why do people postpone parenthood? Reasons and social policy incentives. Hum. Reprod. Update 17, 848–860 (2011).

    PubMed  PubMed Central  Google Scholar 

  7. 7.

    Rahmioglu, N. et al. Genome-wide enrichment analysis between endometriosis and obesity-related traits reveals novel susceptibility loci. Hum. Mol. Genet. 24, 1185–1199 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Barban, N. et al. Genome-wide analysis identifies 12 loci influencing human reproductive behavior. Nat. Genet. 48, 1462–1472 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Martin, N. G., Eaves, L. J. & Eysenck, H. J. Genetical, environmental and personality factors influencing the age of first sexual intercourse in twins. J. Biosoc. Sci. 9, 91–97 (1977).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Day, F. R. et al. Physical and neurobehavioral determinants of reproductive onset and success. Nat. Genet. https://doi.org/10.1038/ng.3551 (2016).

  11. 11.

    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 

  12. 12.

    Tropf, F. C. et al. Hidden heritability due to heterogeneity across seven populations. Nat. Hum. Behav. 1, 757–765 (2017).

    PubMed  PubMed Central  Google Scholar 

  13. 13.

    Mills, M. C. Introducing Survival and Event History Analysis (Sage, 2011).

  14. 14.

    Singh, S., Darroch, J. E. & Frost, J. J. Socioeconomic disadvantage and adolescent women’s sexual and reproductive behavior: the case of five developed countries. Fam. Plann. Perspect. 33, 251 (2001).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Grotzinger, A. D. et al. Genomic structural equation modelling provides insights into the multivariate genetic architecture of complex traits. Nat. Hum. Behav. 3, 513–525 (2019).

    PubMed  PubMed Central  Google Scholar 

  17. 17.

    Davey Smith, G. What can Mendelian randomisation tell us about modifiable behavioural and environmental exposures? BMJ https://doi.org/10.1136/bmj.330.7499.1076 (2005).

  18. 18.

    Lee, J. J. et al. Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nat. Genet. 50, 1112–1121 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Liu, M. et al. Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use. Nat. Genet. 51, 237–244 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Zheng, J.-S. et al. The association between circulating 25-hydroxyvitamin D metabolites and type 2 diabetes in European populations: a meta-analysis and Mendelian randomisation analysis. PLoS Med. 17, e1003394 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Nelson, C. P. et al. Association analyses based on false discovery rate implicate new loci for coronary artery disease. Nat. Genet. 49, 1385–1391 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Lind, J. M., Hennessy, A. & Chiu, C. L. Association between a woman’s age at first birth and high blood pressure. Medicine (Baltimore) 94, e697 (2015).

    Google Scholar 

  23. 23.

    Patchen, L., Leoutsakos, J.-M. & Astone, N. M. Early parturition: is young maternal age at first birth associated with obesity? J. Pediatr. Adolesc. Gynecol. 30, 553–559 (2017).

    PubMed  PubMed Central  Google Scholar 

  24. 24.

    Kim, J. H., Jung, Y., Kim, S. Y. & Bae, H. Y. Impact of age at first childbirth on glucose tolerance status in postmenopausal women: the 2008–2011 Korean national health and nutrition examination survey. Diabetes Care 37, 671–677 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Day, F. et al. Large-scale genome-wide meta-analysis of polycystic ovary syndrome suggests shared genetic architecture for different diagnosis criteria. PLoS Genet. 14, e1007813 (2018).

    PubMed  PubMed Central  Google Scholar 

  26. 26.

    Eisenberg, D. T. A., Hayes, M. G. & Kuzawa, C. W. Delayed paternal age of reproduction in humans is associated with longer telomeres across two generations of descendants. Proc. Natl Acad. Sci. USA 109, 10251–10256 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Pers, T. H. et al. Biological interpretation of genome-wide association studies using predicted gene functions. Nat. Commun. 6, 5890 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Zeisel, A. et al. Molecular architecture of the mouse nervous system. Cell 174, 999–1014.e22 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Tabula Muris Consortium et al. Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris. Nature 562, 367–372 (2018).

    Google Scholar 

  30. 30.

    Yang, H., Robinson, P. N. & Wang, K. Phenolyzer: phenotype-based prioritization of candidate genes for human diseases. Nat. Methods 12, 841–843 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Vaez, A. et al. In silico post genome-wide association studies analysis of c-reactive protein loci suggests an important role for interferons. Circ. Cardiovasc. Genet. 8, 487–497 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. 32.

    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  PubMed Central  Google Scholar 

  33. 33.

    van der Wijst, M. G. P. et al. Single-cell RNA sequencing identifies celltype-specific cis-eQTLs and co-expression QTLs. Nat. Genet. 50, 493–497 (2018).

    PubMed  PubMed Central  Google Scholar 

  34. 34.

    Uhlén, M. et al. Proteomics. Tissue-based map of the human proteome. Science 347, 1260419 (2015).

    PubMed  Google Scholar 

  35. 35.

    Ellsworth, B. S. et al. FOXL2 in the pituitary: molecular, genetic, and developmental analysis. Mol. Endocrinol 20, 2796–2805 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 36.

    van Vliet, J. et al. Human KLF17 is a new member of the Sp/KLF family of transcription factors. Genomics 87, 474–482 (2006).

    PubMed  PubMed Central  Google Scholar 

  37. 37.

    Governini, L. et al. FOXL2 in human endometrium: hyperexpressed in endometriosis. Reprod. Sci. 21, 1249–1255 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Rico, C. et al. HIF1 activity in granulosa cells is required for FSH-regulated Vegfa expression and follicle survival in mice. Biol. Reprod. 90, 135 (2014).

    PubMed  PubMed Central  Google Scholar 

  39. 39.

    Dai, Z. et al. Caveolin-1 promotes trophoblast cell invasion through the focal adhesion kinase (FAK) signalling pathway during early human placental development. Reprod. Fertil. Dev. https://doi.org/10.1071/RD18296 (2019).

  40. 40.

    Artini, P. G. et al. Cumulus cells surrounding oocytes with high developmental competence exhibit down-regulation of phosphoinositol 1,3 kinase/protein kinase B (PI3K/AKT) signalling genes involved in proliferation and survival. Hum. Reprod. 32, 2474–2484 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Zheng, J. et al. Novel FSHβ mutation in a male patient with isolated FSH deficiency and infertility. Eur. J. Med. Genet. 60, 335–339 (2017).

    PubMed  PubMed Central  Google Scholar 

  42. 42.

    Yan, W., Burns, K. H., Ma, L. & Matzuk, M. M. Identification of Zfp393, a germ cell-specific gene encoding a novel zinc finger protein. Mech. Dev. 118, 233–239 (2002).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Lin, Y.-N., Roy, A., Yan, W., Burns, K. H. & Matzuk, M. M. Loss of zona pellucida binding proteins in the acrosomal matrix disrupts acrosome biogenesis and sperm morphogenesis. Mol. Cell. Biol. 27, 6794–6805 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. 44.

    Wieser, F. et al. Expression and regulation of CCR1 in peritoneal macrophages from women with and without endometriosis. Fertil. Steril. 83, 1878–1881 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Mei, J. et al. CXCL16/CXCR6 interaction promotes endometrial decidualization via the PI3K/ AKT pathway. Reproduction https://doi.org/10.1530/REP-18-0417 (2019).

  46. 46.

    Gusev, F. E. et al. Epigenetic-genetic chromatin footprinting identifies novel and subject-specific genes active in prefrontal cortex neurons. FASEB J. 33, 8161–8173 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Quinn, J. P., Savage, A. L. & Bubb, V. J. Non-coding genetic variation shaping mental health. Curr. Opin. Psychol. 27, 18–24 (2019).

    PubMed  PubMed Central  Google Scholar 

  48. 48.

    Barak, B. et al. Neuronal deletion of Gtf2i, associated with Williams syndrome, causes behavioral and myelin alterations rescuable by a remyelinating drug. Nat. Neurosci. 22, 700–708 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. 49.

    Li, Y. et al. Topoisomerase IIbeta is required for proper retinal development and survival of postmitotic cells. Biol. Open 3, 172–184 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. 50.

    Athanasiou, M. C. et al. The transcription factor E2F-1 in SV40 T antigen-induced cerebellar Purkinje cell degeneration. Mol. Cell. Neurosci. 12, 16–28 (1998).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. 51.

    Yang, X. et al. The association between NCAM1 levels and behavioral phenotypes in children with autism spectrum disorder. Behav. Brain Res. 359, 234–238 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. 52.

    Locke, A. E. et al. Genetic studies of body mass index yield new insights for obesity biology. Nature 518, 197–206 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. 53.

    Tu, S. et al. NitroSynapsin therapy for a mouse MEF2C haploinsufficiency model of human autism. Nat. Commun. 8, 1488 (2017).

    PubMed  PubMed Central  Google Scholar 

  54. 54.

    Shamir, A. et al. The importance of the NRG-1/ErbB4 pathway for synaptic plasticity and behaviors associated with psychiatric disorders. J. Neurosci. 32, 2988–2997 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. 55.

    Yang, J.-M. et al. erbb4 deficits in chandelier cells of the medial prefrontal cortex confer cognitive dysfunctions: implications for schizophrenia. Cereb. Cortex 29, 4334–4346 (2019).

    PubMed  PubMed Central  Google Scholar 

  56. 56.

    Day, F. R. et al. Causal mechanisms and balancing selection inferred from genetic associations with polycystic ovary syndrome. Nat. Commun. 6, 8464 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. 57.

    Baumgartner, H. K. et al. Characterization of choline transporters in the human placenta over gestation. Placenta 36, 1362–1369 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. 58.

    Peng, Z. et al. Liver X receptor β in the hippocampus: a potential novel target for the treatment of major depressive disorder? Neuropharmacology 135, 514–528 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. 59.

    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 

  60. 60.

    Tropf, F. C. et al. Human fertility, molecular genetics, and natural selection in modern societies. PLoS One 10, e0126821 (2015).

    PubMed  PubMed Central  Google Scholar 

  61. 61.

    Waren, E. B. & et al. Heterogeneity in polygenic scores for common human traits. Preprint at bioRxiv https://doi.org/10.1101/106062 (2017).

  62. 62.

    Ripke, S. et al. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  63. 63.

    Mills, M. C. & Rahal, C. The GWAS diversity monitor tracks diversity by disease in real time. Nat. Genet. 52, 242–243 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  64. 64.

    Chen, X.-K. et al. Teenage pregnancy and adverse birth outcomes: a large population based retrospective cohort study. Int. J. Epidemiol. 36, 368–373 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. 65.

    Bongaarts, J., Mensch, B. S. & Blanc, A. K. Trends in the age at reproductive transitions in the developing world: the role of education. Popul. Stud. (N. Y.). 71, 139–154 (2017).

    Google Scholar 

  66. 66.

    Willer, C. J., Li, Y. & Abecasis, G. R. METAL: Fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  67. 67.

    Finucane, H. K. et al. Partionining heritability by functional category using GWAS summary statistics. Nat. Genet. 47, 1228–1235 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  68. 68.

    Altman, D. G. & Bland, J. M. Interaction revisited: the difference between two estimates. Br. Med. J. 326, 219 (2003).

    Google Scholar 

  69. 69.

    Turley, P. et al. Multi-trait analysis of genome-wide association summary statistics using MTAG. Nat. Genet. 50, 229–237 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  70. 70.

    Harris, K. M. & et al. The National Longitudinal Study of Adolescent to Adult Health: research design. Carolina Population Center http://www.cpc.unc.edu/projects/addhealth/design (2009).

  71. 71.

    Buck, N. & McFall, S. Understanding society: design overview. Longit. Life Course Stud. 3, 5–17 (2012).

    Google Scholar 

  72. 72.

    Euesden, J., Lewis, C. M. & O’Reilly, P. F. PRSice: polygenic risk score software. Bioinformatics 31, btu848–btu1468 (2014).

    Google Scholar 

  73. 73.

    Vilhjálmsson, B. J. et al. Modeling linkage disequilibrium increases accuracy of polygenic risk scores. Am. J. Hum. Genet. 97, 576–592 (2015).

    PubMed  PubMed Central  Google Scholar 

  74. 74.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  75. 75.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  76. 76.

    Lee, J. J. et al. Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nat. Genet. https://doi.org/10.1038/s41588-018-0147-3 (2018).

  77. 77.

    Burgess, S., Butterworth, A. & Thompson, S. G. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet. Epidemiol. 37, 658–665 (2013).

    PubMed  PubMed Central  Google Scholar 

  78. 78.

    Bowden, J., Smith, G. D. & Burgess, S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int. J. Epidemiol. https://doi.org/10.1093/ije/dyv080 (2015).

  79. 79.

    Hemani, G., Bowden, J. & Davey Smith, G. Evaluating the potential role of pleiotropy in Mendelian randomization studies. Hum. Mol. Genet. 27, R195–R208 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  80. 80.

    Burgess, S. & Thompson, S. G. Multivariable Mendelian randomization: the use of pleiotropic genetic variants to estimate causal effects. Am. J. Epidemiol. 181, 251–260 (2015).

    PubMed  PubMed Central  Google Scholar 

  81. 81.

    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 

  82. 82.

    Woods, L. M. Geographical variation in life expectancy at birth in England and Wales is largely explained by deprivation. J. Epidemiol. Community Health 59, 115–120 (2005).

    PubMed  PubMed Central  Google Scholar 

  83. 83.

    Timshel, P. N., Thompson, J. J. & Pers, T. H. Genetic mapping of etiologic brain cell types for obesity. eLife 9, e55851 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  84. 84.

    Vosa, U. & Al., E. Unraveling the polygenic architecture of complex traits using blood eQTL metaanalysis. Preprint at bioRxiv https://doi.org/10.1101/447367 (2018).

  85. 85.

    Qi, T. et al. Identifying gene targets for brain-related traits using transcriptomic and methylomic data from blood. Nat. Commun. 9, 2282 (2018).

    PubMed  PubMed Central  Google Scholar 

  86. 86.

    Bult, C. J. et al. Mouse genome database (MGD) 2019. Nucleic Acids Res. 47, D801–D806 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

A detailed list of funding and other acknowledgements for each cohort can be found in Supplementary Sect. 14. This research was conducted using the UK Biobank resource under application 22276 and 9905. Funding was provided to M.C.M. by the ERC, SOCIOGENOME (615603), CHRONO (835079), ESRC/UKRI SOCGEN (ES/N011856/1), Wellcome Trust ISSF, Leverhulme Trust and Leverhulme Centre for Demographic Science, to N.B. by ERC GENPOP (865356), to F.C.T. by LabEx Ecode, French National Research Agency (ANR) Investissements d’Avenir (ANR-11-LABX-0047), to M.d.H. by Swedish Heart-Lung Foundation (20170872, 20200781, 20140543, 20170678, 20180706 and 20200602), Kjell and Märta Beijer Foundation and Swedish Research Council (2015-03657, 2019-01417). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. This study received ethical approval from the Department of Sociology, University of Oxford, and relevant ethical approval was obtained at the local level for the contributing datasets. The authors thank E. T. Akimova and S. Møllegaard for administrative work in the organization of the cohort information and author list.

Author information

Affiliations

Authors

Consortia

Contributions

M.C.M. and F.R.D. designed and led the study. M.C.M. wrote the paper and Supplementary Information with contributions by authors for respective analyses and comments by all authors. D.M.B. conducted phenotypic changes, phenotype preparation, LD score and genetic correlations, GenomicSEM and EFA and sex-specific effects. N.B. conducted GWAS meta-analysis, MTAG, PGS prediction, survival models and Cox models of longevity. F.C.T. and F.R.D. conducted the cohort QC. F.C.T. conducted GREML cohort heritability analysis and phenotype preparation in UKBB. F.R.D. ran MR and conducted GWAS analyses, J.R.B.P. conducted COJO and X chromosome analysis and K.K.O. provided comments and expertise throughout. N.v.Z. conducted DEPICT and Phenolyzer analyses. A.V. and H.S. conducted in silico sequencing and SMR analyses. T.H.P. conducted cell type enrichment analyses. M.d.H. integrated gene prioritization results and performed downstream analyses, for example, Human Protein Atlas; Entrez, GeneCards and Uniprot mining; and STRING protein–protein interaction analyses. Authors in the Human Reproductive Behaviour Consortium contributed valuable data, conducted cohort-specific GWAS and other analyses, and contributed through the administration, management and data collection for the participating cohorts. The eQTLGen and BIOS Consortia provided data for additional analyses. All authors reviewed and approved the final version of the paper, and code relies upon the standard packages described above.

Corresponding authors

Correspondence to Melinda C. Mills or Felix R. Day.

Ethics declarations

Competing interests

The main authors declare no competing interests. The views expressed in this article are those of the author(s) 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, 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 2019, M.I.M. is an employee of Genentech and a holder of Roche stock.

Additional information

Peer review information Nature Human Behaviour thanks Ahmed Elhakeem and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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

Supplementary information

Supplementary information

Supplementary Figs. 1–19, Supplementary Discussion, Supplementary Tables 3a (excerpt) and 14, Supplementary Authorship and Detailed Acknowledgements.

Reporting summary

Supplementary tables

Supplementary Tables 1–19.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Mills, M.C., Tropf, F.C., Brazel, D.M. et al. Identification of 371 genetic variants for age at first sex and birth linked to externalising behaviour. Nat Hum Behav (2021). https://doi.org/10.1038/s41562-021-01135-3

Download citation

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