Lek, M. et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285–291 (2016).
Cassa, C. A. et al. Estimating the selective effects of heterozygous protein-truncating variants from human exome data. Nat. Genet. 49, 806–810 (2017).
Collins, R. L. et al. A structural variation reference for medical and population genetics. Nature 581, 444–451 (2020).
Weghorn, D. et al. Applicability of the mutation-selection balance model to population genetics of heterozygous protein-truncating variants in humans. Mol. Biol. Evol. 36, 1701–1710 (2019).
Darwin, C. The Descent of Man, and Selection in Relation to Sex (A. L. Burt, 1874); https://doi-org.ezp.lib.cam.ac.uk/10.5962/bhl.title.16749
Ganna, A. et al. Ultra-rare disruptive and damaging mutations influence educational attainment in the general population. Nat. Neurosci. 19, 1563–1565 (2016).
Männik, K. et al. Copy number variations and cognitive phenotypes in unselected populations. JAMA 313, 2044–2054 (2015).
Huguet, G. et al. Measuring and estimating the effect sizes of copy number variants on general intelligence in community-based samples. JAMA Psychiatry 75, 447–457 (2018).
Ganna, A. et al. Quantifying the impact of rare and ultra-rare coding variation across the phenotypic spectrum. Am. J. Hum. Genet. 102, 1204–1211 (2018).
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).
Szustakowski, J. D. et al. Advancing human genetics research and drug discovery through exome sequencing of the UK Biobank. Nat. Genet. 53, 942–948 (2021).
Barthold, J. A., Myrskylä, M. & Jones, O. R. Childlessness drives the sex difference in the association between income and reproductive success of modern Europeans. Evol. Hum. Behav. 33, 628–638 (2012).
Dudel, C. & Klüsener, S. Estimating men’s fertility from vital registration data with missing values. Popul. Stud. 73, 439–449 (2019).
Birth Summary Tables, England and Wales 2019 (Office of National Statistics, 2020).
Oud, M. S. et al. A systematic review and standardized clinical validity assessment of male infertility genes. Hum. Reprod. 34, 932–941 (2019).
Bult, C. J. et al. Mouse Genome Database (MGD) 2019. Nucleic Acids Res. 47, D801–D806 (2019).
Lopes, A. M. et al. Human spermatogenic failure purges deleterious mutation load from the autosomes and both sex chromosomes, including the gene DMRT1. PLoS Genet. 9, e1003349 (2013).
Skjaerven, R., Wilcox, A. J. & Lie, R. T. A population-based study of survival and childbearing among female subjects with birth defects and the risk of recurrence in their children. N. Engl. J. Med. 340, 1057–1062 (1999).
Lie, R. T., Wilcox, A. J. & Skjaerven, R. Survival and reproduction among males with birth defects and risk of recurrence in their children. JAMA 285, 755–760 (2001).
Power, R. A. et al. Fecundity of patients with schizophrenia, autism, bipolar disorder, depression, anorexia nervosa, or substance abuse vs their unaffected siblings. JAMA Psychiatry 70, 22–30 (2013).
Allen, M. S. The role of personality in sexual and reproductive health. Curr. Dir. Psychol. Sci. 28, 581–586 (2019).
Buss, D. M. et al. International preferences in selecting mates: a study of 37 cultures. J. Cross. Cult. Psychol. 21, 5–47 (1990).
Pawłowski, B. & Dunbar, R. I. Impact of market value on human mate choice decisions. Proc. Biol. Sci. 266, 281–285 (1999).
Buss, D. M. & Schmitt, D. P. Mate preferences and their behavioral manifestations. Annu. Rev. Psychol. 70, 77–110 (2019).
Fieder, M., Huber, S. & Bookstein, F. L. Socioeconomic status, marital status and childlessness in men and women: an analysis of census data from six countries. J. Biosoc. Sci. 43, 619–635 (2011).
Nettle, D. & Pollet, T. V. Natural selection on male wealth in humans. Am. Nat. 172, 658–666 (2008).
Miettinen, A., Rotkirch, A., Szalma, I., Donno, A. & Tanturri, M.-L. Increasing Childlessness in Europe: Time Trends and Country Differences Working Paper 33 (Family and Societies, 2015).
Jalovaara, M. et al. Education, Gender, and Cohort Fertility in the Nordic Countries. Eur. J. Popul. 35, 563–586 (2019).
Fieder, M. & Huber, S. The effects of sex and childlessness on the association between status and reproductive output in modern society. Evol. Hum. Behav. 28, 392–398 (2007).
GTEx Consortium. The Genotype–Tissue Expression (GTEx) project. Nat. Genet. 45, 580–585 (2013).
Trivers, R. in Sexual Selection and the Descent of Man (ed. Campbell, B.) (Aldine, 1972).
Bateman, A. J. Intra-sexual selection in Drosophila. Heredity 2, 349–368 (1948).
Parker, G. A. & Pizzari, T. in Current Perspectives on Sexual Selection 119–163 (Springer, 2015).
Kolk, M. & Barclay, K. Cognitive ability and fertility among Swedish men born 1951-1967: evidence from military conscription registers. Proc. Biol. Sci. 286, 20190359 (2019).
Kendall, K. M. et al. Cognitive performance among carriers of pathogenic copy number variants: analysis of 152,000 UK Biobank subjects. Biol. Psychiatry 82, 103–110 (2017).
Davis, K. A. S. et al. Mental health in UK Biobank—development, implementation and results from an online questionnaire completed by 157,366 participants: a reanalysis. BJPsych Open 6, e18 (2020).
Tyrrell, J. et al. Genetic predictors of participation in optional components of UK Biobank. Nat. Commun. 12, 886 (2021).
Stefansson, H. et al. CNVs conferring risk of autism or schizophrenia affect cognition in controls. Nature 505, 361–366 (2014).
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).
Watanabe, K. et al. A global overview of pleiotropy and genetic architecture in complex traits. Nat. Genet. 51, 1339–1348 (2019).
Barban, N. et al. Genome-wide analysis identifies 12 loci influencing human reproductive behavior. Nat. Genet. 48, 1462–1472 (2016).
Verweij, R. M. et al. Sexual dimorphism in the genetic influence on human childlessness. Eur. J. Hum. Genet. 25, 1067–1074 (2017).
Clark, D. W. et al. Associations of autozygosity with a broad range of human phenotypes. Nat. Commun. 10, 4957 (2019).
Stanley, K. E. et al. Causal genetic variants in stillbirth. N. Engl. J. Med. 383, 1107–1116 (2020).
Kaplanis, J. et al. Evidence for 28 genetic disorders discovered by combining healthcare and research data. Nature 586, 757–762 (2020).
Girirajan, S. et al. Phenotypic heterogeneity of genomic disorders and rare copy-number variants. N. Engl. J. Med. 367, 1321–1331 (2012).
Costain, G., Chow, E. W. C., Silversides, C. K. & Bassett, A. S. Sex differences in reproductive fitness contribute to preferential maternal transmission of 22q11.2 deletions. J. Med. Genet. 48, 819–824 (2011).
De Rubeis, S. et al. Synaptic, transcriptional and chromatin genes disrupted in autism. Nature 515, 209–215 (2014).
Berrington, A. in Demographic Research Monographs 57–76 (Springer, 2017).
Betzig, L. Means, variances, and ranges in reproductive success: comparative evidence. Evol. Hum. Behav. 33, 309–317 (2012).
Bycroft, C. et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 562, 203–209 (2018).
Wang, K. et al. PennCNV: an integrated hidden Markov model designed for high-resolution copy number variation detection in whole-genome SNP genotyping data. Genome Res. 17, 1665–1674 (2007).
Macé, A. et al. New quality measure for SNP array based CNV detection. Bioinformatics 32, 3298–3305 (2016).
Liaw, A. & Wiener, M. Classification and regression by randomforest. R News 2, 285 (2002).
Di Angelantonio, E. et al. Efficiency and safety of varying the frequency of whole blood donation (INTERVAL): a randomised trial of 45,000 donors. Lancet 390, 2360–2371 (2017).
Fromer, M. et al. Discovery and statistical genotyping of copy-number variation from whole-exome sequencing depth. Am. J. Hum. Genet. 91, 597–607 (2012).
Backenroth, D. et al. CANOES: detecting rare copy number variants from whole exome sequencing data. Nucleic Acids Res. 42, e97 (2014).
Packer, J. S. et al. CLAMMS: a scalable algorithm for calling common and rare copy number variants from exome sequencing data. Bioinformatics 32, 133–135 (2016).
Crawford, K. et al. Medical consequences of pathogenic CNVs in adults: analysis of the UK Biobank. J. Med. Genet. 56, 131–138 (2019).
McLaren, W. et al. The Ensembl variant effect predictor. Genome Biol. 17, 122 (2016).
Rentzsch, P., Schubach, M., Shendure, J. & Kircher, M. CADD-splice-improving genome-wide variant effect prediction using deep learning-derived splice scores. Genome Med. 13, 31 (2021).
Karczewski, K. J. et al. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature 581, 434–443 (2020).
Samocha, K. E. et al. Regional missense constraint improves variant deleteriousness prediction. Preprint at https://doi.org/10.1101/148353 (2017).
Zhao, H. et al. CrossMap: a versatile tool for coordinate conversion between genome assemblies. Bioinformatics 30, 1006–1007 (2014).
Kersey, P. J. et al. Ensembl Genomes 2016: more genomes, more complexity. Nucleic Acids Res. 44, D574–D580 (2016).
Landrum, M. J. et al. ClinVar: improving access to variant interpretations and supporting evidence. Nucleic Acids Res. 46, D1062–D1067 (2018).
Van Hout, C. V. et al. Exome sequencing and characterization of 49,960 individuals in the UK Biobank. Nature 586, 749–756 (2020).
Nait Saada, J. et al. Identity-by-descent detection across 487,409 British samples reveals fine scale population structure and ultra-rare variant associations. Nat. Commun. 11, 6130 (2020).
Virtanen, P. et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261–272 (2020).
Balduzzi, S., Rücker, G. & Schwarzer, G. How to perform a meta-analysis with R: a practical tutorial. Evid. Based. Ment. Health 22, 153–160 (2019).
Population and Welfare Department. Multi-Generation Register 2016: A Description of Contents and Quality (Statistics Sweden, 2017).
Carlstedt, B. Cognitive Abilities—Aspects of Structure, Process and Measurement. Doctoral thesis, Univ. of Gothenburg (2000).
Hällsten, M. Inequality across three and four generations in egalitarian Sweden: 1st and 2nd cousin correlations in socio-economic outcomes. Res. Soc. Stratif. Mobil. 35, 19–33 (2014).
Mårdberg, B. & Carlstedt, B. Swedish Enlistment Battery (SEB): construct validity and latent variable estimation of cognitive abilities by the CAT‐SEB. Int. J. Sel. 6, 107–114 (1998).
Rönnlund, M., Carlstedt, B., Blomstedt, Y., Nilsson, L.-G. & Weinehall, L. Secular trends in cognitive test performance: Swedish conscript data 1970–1993. Intelligence 41, 19–24 (2013).