Abstract
Identifying genetic determinants of reproductive success may highlight mechanisms underlying fertility and identify alleles under present-day selection. Using data in 785,604 individuals of European ancestry, we identified 43 genomic loci associated with either number of children ever born (NEB) or childlessness. These loci span diverse aspects of reproductive biology, including puberty timing, age at first birth, sex hormone regulation, endometriosis and age at menopause. Missense variants in ARHGAP27 were associated with higher NEB but shorter reproductive lifespan, suggesting a trade-off at this locus between reproductive ageing and intensity. Other genes implicated by coding variants include PIK3IP1, ZFP82 and LRP4, and our results suggest a new role for the melanocortin 1 receptor (MC1R) in reproductive biology. As NEB is one component of evolutionary fitness, our identified associations indicate loci under present-day natural selection. Integration with data from historical selection scans highlighted an allele in the FADS1/2 gene locus that has been under selection for thousands of years and remains so today. Collectively, our findings demonstrate that a broad range of biological mechanisms contribute to reproductive success.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 digital issues and online access to articles
$119.00 per year
only $9.92 per issue
Rent or buy this article
Get just this article for as long as you need it
$39.95
Prices may be subject to local taxes which are calculated during checkout
Data availability
Upon publication, the GWAS summary statistics will be made available at https://doi.org/10.17863/CAM.88397. Access to individual-level data from the multiple sources used in this GWAS can be obtained by bona fide scientists through application to each specific data provider; each data source is described in the Supplementary Note. Source data are provided with this paper.
Code availability
No custom code was used in this study. All analyses and modelling used standard software as described in the Methods and the Supplementary Information.
References
Day, F. R. et al. Causal mechanisms and balancing selection inferred from genetic associations with polycystic ovary syndrome. Nat. Commun. 6, 8464 (2015).
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).
Censin, J. C., Bovijn, J., Holmes, M. V. & Lindgren, C. M. Commentary: Mendelian randomization and women’s health. Int. J. Epidemiol. 48, 830–833 (2019).
O’Connor, L. J. et al. Extreme polygenicity of complex traits is explained by negative selection. Am. J. Hum. Genet. 105, 456–476 (2019).
Weissbrod, O. et al. Functionally informed fine-mapping and polygenic localization of complex trait heritability. Nat. Genet. 52, 1355–1363 (2020).
Balbo, N., Billari, F. C. & Mills, M. Fertility in advanced societies: a review of research. Eur. J. Popul. 29, 1–38 (2013).
Mills, M., Rindfuss, R. R., McDonald, P. & te Velde, E. Why do people postpone parenthood? Reasons and social policy incentives. Hum. Reprod. Update 17, 848–860 (2011).
Tropf, F. C. et al. Hidden heritability due to heterogeneity across seven populations. Nat. Hum. Behav. 1, 757–765 (2017).
Day, F. R. et al. Physical and neurobehavioral determinants of reproductive onset and success. Nat. Genet. 48, 617–623 (2016).
Barban, N. et al. Genome-wide analysis identifies 12 loci influencing human reproductive behavior. Nat. Genet. 48, 1462–1472 (2016).
Mills, M. et al. Identification of 371 genetic variants for age at first sex and birth linked to externalising behaviour. Nat. Hum. Behav. 5, 1717–1730 (2021).
Tropf, F. C. et al. Human fertility, molecular genetics, and natural selection in modern societies. PLoS ONE 10, e0126821 (2015).
Day, F. R. et al. Genomic analyses identify hundreds of variants associated with age at menarche and support a role for puberty timing in cancer risk. Nat. Genet. 49, 834–841 (2017).
Day, F. R. et al. Large-scale genomic analyses link reproductive aging to hypothalamic signaling, breast cancer susceptibility and BRCA1-mediated DNA repair. Nat. Genet. 47, 1294–1303 (2015).
Kong, A. et al. Selection against variants in the genome associated with educational attainment. Proc. Natl Acad. Sci. USA 114, E727–E732 (2017).
Beauchamp, J. P. Genetic evidence for natural selection in humans in the contemporary United States. Proc. Natl Acad. Sci. USA 113, 7774–7779 (2016).
Bulik-Sullivan, B. K. et al. LD score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).
Day, F. R., Ong, K. K. & Perry, J. R. B. Elucidating the genetic basis of social interaction and isolation. Nat. Commun. 9, 2457 (2018).
Okbay, A. et al. Genome-wide association study identifies 74 loci associated with educational attainment. Nature 533, 539–542 (2016).
Reddy, P. et al. Oocyte-specific deletion of Pten causes premature activation of the primordial follicle pool. Science 319, 611–613 (2008).
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).
Morgan, M. D. et al. Genome-wide study of hair colour in UK Biobank explains most of the SNP heritability. Nat. Commun. 9, 5271 (2018).
Hollis, B. et al. Genomic analysis of male puberty timing highlights shared genetic basis with hair colour and lifespan. Nat. Commun. 11, 1536 (2020).
Bansal, S. K., Gupta, N., Sankhwar, S. N. & Rajender, S. Differential genes expression between fertile and infertile spermatozoa revealed by transcriptome analysis. PLoS ONE 10, e0127007 (2015).
Thompson, D. J. et al. Genetic predisposition to mosaic Y chromosome loss in blood. Nature 575, 652–657 (2019).
Nakamura, N. et al. Disruption of a spermatogenic cell-specific mouse enolase 4 (eno4) gene causes sperm structural defects and male infertility. Biol. Reprod. 88, 90 (2013).
Ganna, A. et al. Large-scale GWAS reveals insights into the genetic architecture of same-sex sexual behavior. Science 365, eaat7693 (2019).
Mathieson, I. et al. Genome-wide patterns of selection in 230 ancient Eurasians. Nature 528, 499–503 (2015).
Field, Y. et al. Detection of human adaptation during the past 2000 years. Science 354, 760–764 (2016).
Grossman, S. R. et al. A composite of multiple signals distinguishes causal variants in regions of positive selection. Science 327, 883–886 (2010).
Grossman, S. R. et al. Identifying recent adaptations in large-scale genomic data. Cell 152, 703–713 (2013).
Giambartolomei, C. et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 10, e1004383 (2014).
Fumagalli, M. et al. Greenlandic Inuit show genetic signatures of diet and climate adaptation. Science 349, 1343–1347 (2015).
Ameur, A. et al. Genetic adaptation of fatty-acid metabolism: a human-specific haplotype increasing the biosynthesis of long-chain omega-3 and omega-6 fatty acids. Am. J. Hum. Genet. 90, 809–820 (2012).
Ye, K., Gao, F., Wang, D., Bar-Yosef, O. & Keinan, A. Dietary adaptation of FADS genes in Europe varied across time and geography. Nat. Ecol. Evol. 1, 167 (2017).
Buckley, M. T. et al. Selection in Europeans on fatty acid desaturases associated with dietary changes. Mol. Biol. Evol. 34, 1307–1318 (2017).
Mathieson, S. & Mathieson, I. FADS1 and the timing of human adaptation to agriculture. Mol. Biol. Evol. 35, 2957–2970 (2018).
Teslovich, T. M. et al. Biological, clinical and population relevance of 95 loci for blood lipids. Nature 466, 707–713 (2010).
Draisma, H. H. M. et al. Genome-wide association study identifies novel genetic variants contributing to variation in blood metabolite levels. Nat. Commun. 6, 7208 (2015).
Stoffel, W. et al. Dietary ω3-and ω6-polyunsaturated fatty acids reconstitute fertility of juvenile and adult Fads2-deficient mice. Mol. Metab. 36, 100974 (2020).
Klarin, D. et al. Genetics of blood lipids among ~300,000 multi-ethnic participants of the Million Veteran Program. Nat. Genet. 50, 1514–1523 (2018).
Siewert, K. M. & Voight, B. F. Detecting long-term balancing selection using allele frequency correlation. Mol. Biol. Evol. 34, 2996–3005 (2017).
Boutwell, B. et al. Replication and characterization of CADM2 and MSRA genes on human behavior. Heliyon 3, e00349 (2017).
Bitarello, B. D. et al. Signatures of long-term balancing selection in human genomes. Genome Biol. Evol. 10, 939–955 (2018).
Fry, A. et al. Comparison of sociodemographic and health-related characteristics of UK Biobank participants with those of the general population. Am. J. Epidemiol. 186, 1026–1034 (2017).
Wood, A. R. et al. Defining the role of common variation in the genomic and biological architecture of adult human height. Nat. Genet. 46, 1173–1186 (2014).
van der Most, P. J. et al. QCGWAS: a flexible R package for automated quality control of genome-wide association results. Bioinformatics 30, 1185–1186 (2014).
Winkler, T. W. et al. Quality control and conduct of genome-wide association meta-analyses. Nat. Protoc. 9, 1192–1212 (2014).
Price, A. L. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 38, 904–909 (2006).
Loh, P.-R. et al. Efficient Bayesian mixed-model analysis increases association power in large cohorts. Nat. Genet. 47, 284–290 (2015).
Rietveld, C. A. et al. GWAS of 126,559 individuals identifies genetic variants associated with educational attainment. Science 340, 1467–1471 (2013).
Willer, C. J., Li, Y. & Abecasis, G. R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010).
Evangelou, E. & Ioannidis, J. P. A. Meta-analysis methods for genome-wide association studies and beyond. Nat. Rev. Genet. 14, 379–389 (2013).
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).
Yang, J. et al. Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat. Genet. 44, 369–375 (2012).
Bulik-Sullivan, B. et al. An atlas of genetic correlations across human diseases and traits. Nat. Genet. 47, 1236–1241 (2015).
Manichaikul, A. et al. Robust relationship inference in genome-wide association studies. Bioinformatics 26, 2867–2873 (2010).
Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. GigaScience, 4, https://doi.org/10.1186/s13742-015-0047-8 (2015).
Zhu, Z. et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat. Genet. 48, 481–487 (2016).
Võsa, U. et al. Large-scale cis- and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression. Nat. Genet. 53, 1300–1310 (2021).
Finucane, H. K. et al. Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types. Nat. Genet. 50, 621–629 (2018).
GTEx Consortium. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348, 648–660 (2015).
Backman, J. D. et al. Exome sequencing and analysis of 454,787 UK Biobank participants. Nature 599, 628–634 (2021).
Ruth, K. S. et al. Using human genetics to understand the disease impacts of testosterone in men and women. Nat. Med. 26, 252–258 (2020).
Li, L. et al. Single-cell RNA-seq analysis maps development of human germline cells and gonadal niche interactions. Cell Stem Cell 20, 858–873.e4 (2017).
Zhang, Y. et al. Transcriptome landscape of human folliculogenesis reveals oocyte and granulosa cell interactions. Mol. Cell 72, 1021–1034.e4 (2018).
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).
Chen, S. et al. AfterQC: automatic filtering, trimming, error removing and quality control for fastq data. BMC Bioinf. 18, 80 (2017).
Pertea, M., Kim, D., Pertea, G. M., Leek, J. T. & Salzberg, S. L. Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown. Nat. Protoc. 11, 1650–1667 (2016).
Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).
The 1000 Genomes Project Consortium. A global reference for human genetic variation. Nature 526, 68–74 (2015).
Acknowledgements
This research was conducted using the UK Biobank Resource under application no. 9905. This work was supported by the Medical Research Council (Unit Programme numbers MC_UU_12015/2 and MC_UU_00006/2); ERC grant nos 615603, 835079 and 865356; ESRC ES/N011856/1; the Leverhulme Trust; the Leverhulme Centre for Demographic Science; and LabEx Ecodec ANR grant no. ANR-11-LABX-0047. Full study-specific and individual acknowledgements can be found in the Supplementary Information. The content is solely the responsibility of the authors and does not necessarily represent the official views of any of the funders. 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 2014/01/01/R3, 28 January 2014 (SOCIOGENOME) and revised with extension SOC/R2/001/C1A/21/60 7 July 2022 (CHRONO), and relevant ethical approval was obtained at the local level for the contributing datasets.
Author information
Authors and Affiliations
Consortia
Contributions
I.M., H.S., M.d.H., K.K.O., M.C.M. and J.R.B.P. designed the study. I.M., F.R.D., N.B., F.C.T., D.M.B., A.V., N.v.Z., B.D.B., E.J.G., M.d.H. and J.R.B.P. performed the analyses. All authors contributed to the data collection and curation and critically reviewed the manuscript.
Corresponding authors
Ethics declarations
Competing interests
J.R.B.P. and E.J.G. are employees of Adrestia Therapeutics. 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 has received 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. H.J.G. has received travel grants and speakers’ honoraria from Fresenius Medical Care, Neuraxpharm, Servier and Janssen Cilag as well as research funding from Fresenius Medical Care. The otherauthors declare no competing interests.
Peer review
Peer review information
Nature Human Behaviour thanks the anonymous reviewers for their contribution to the peer review of this work.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary information
Supplementary Information
Supplementary Note and Figs. 1–4.
Supplementary Tables
Supplementary Tables 1–25.
Source data
Source Data Fig. 2
Statistical source data.
Source Data Fig. 3
Statistical source data.
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.
About this article
Cite this article
Mathieson, I., Day, F.R., Barban, N. et al. Genome-wide analysis identifies genetic effects on reproductive success and ongoing natural selection at the FADS locus. Nat Hum Behav 7, 790–801 (2023). https://doi.org/10.1038/s41562-023-01528-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41562-023-01528-6
