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Reduced reproductive success is associated with selective constraint on human genes

Nature volume 603, pages 858–863 (2022)Cite this article

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Abstract

Genome-wide sequencing of human populations has revealed substantial variation among genes in the intensity of purifying selection acting on damaging genetic variants1. Although genes under the strongest selective constraint are highly enriched for associations with Mendelian disorders, most of these genes are not associated with disease and therefore the nature of the selection acting on them is not known2. Here we show that genetic variants that damage these genes are associated with markedly reduced reproductive success, primarily owing to increased childlessness, with a stronger effect in males than in females. We present evidence that increased childlessness is probably mediated by genetically associated cognitive and behavioural traits, which may mean that male carriers are less likely to find reproductive partners. This reduction in reproductive success may account for 20% of purifying selection against heterozygous variants that ablate protein-coding genes. Although this genetic association may only account for a very minor fraction of the overall likelihood of being childless (less than 1%), especially when compared to more influential sociodemographic factors, it may influence how genes evolve over time.

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Fig. 1: Differences in male and female reproductive success as a function of cumulative rare deleterious genetic variation.
Fig. 2: Association of shet burden with traits known to be associated with reproductive success.

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Data availability

Raw data produced by this study are available as part of the UK Biobank data returns catalogue with application ID 44165: https://biobank.ndph.ox.ac.uk/ukb/docs.cgi?id=1.

Code availability

Code used as part of this project to perform phenotype testing, CNV calling, variant quality control and to generate all main text figures, supplementary figures and supplementary tables is available on github: https://github.com/HurlesGroupSanger/UKBBFertility. All statistical analysis in this manuscript was performed using R v3.6.0.

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Acknowledgements

We thank L. Parts, J. Kaplanis, M. Przeworski and G. Davey-Smith for useful discussions and advice on data analysis; M. Oud and J. Veltman for helpful discussions regarding infertility; G. Kalantzis and P. F. Palamara for assistance with correcting for recent ancestry; and the INTERVAL study for sharing genotyping and exome data that enabled us to refine our CNV filtering methodology. This work has been funded by core Wellcome funding to the Wellcome Sanger Institute (grant WT098051) and as part of a Medical Research Council (MRC) Centre Grant to the MRC Centre for Neuropsychiatric Genetics and Genomics (grant MR/L010305/1). K.B. is supported by a fellowship from the Bank of Sweden Tercentenary Foundation (Riksbankens Jubileumsfond). This work has been conducted using the UK Biobank Resource under application numbers 14421 (to G.K.) and 44165 (to H.C.M.).

Author information

Author notes

  1. Eugene J. Gardner

    Present address: Medical Research Council (MRC) Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, UK

Authors and Affiliations

  1. Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, Hinxton, UK

    Eugene J. Gardner, Matthew D. C. Neville, Kaitlin E. Samocha, Mari E. K. Niemi, Hilary C. Martin & Matthew E. Hurles

  2. Max Planck Institute for Demographic Research, Rostock, Germany

    Kieron Barclay

  3. Demography Unit, Department of Sociology, Stockholm University, Stockholm, Sweden

    Kieron Barclay & Martin Kolk

  4. Swedish Collegium for Advanced Study, Uppsala, Sweden

    Kieron Barclay

  5. Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK

    George Kirov

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Contributions

E.J.G., M.D.C.N. and K.E.S. assessed the contribution of rare genetic variation to the phenotypes and vital statistics presented in this manuscript. E.J.G. and G.K. performed CNV calling. E.J.G. and M.E.K.N. annotated and assessed SNV and indel variants from provided WES data. K.B., M.K., E.J.G. and M.E.H. curated and analysed Swedish IQ data. E.J.G., K.E.S., H.C.M. and M.E.H. designed experiments, oversaw the study and wrote the manuscript.

Corresponding author

Correspondence to Matthew E. Hurles.

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Competing interests

M.E.H. is a founder of, director of, consultant to, and holds shares in, Congenica Ltd and is a consultant to the AstraZeneca Centre for Genomics Research.

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Extended data figures and tables

Extended Data Fig. 1 Odds ratio estimates for the association of shet burden with having children for all variant classes.

Identical plot to main text Fig. 1a, but with additional data for synonymous, missense, and duplication shet scores, separated into females (violet) and males (jade). Asterisks indicate significance after Bonferroni correction for 20 tests (P < 2.5 × 10−3; Methods).

Extended Data Fig. 2 Effect size estimate for the association of shet burden with number of children for individuals with children.

Shown are the effect size estimates for the association of shet burden with number of children, separated into females (purple) and males (jade), but with all childless individuals in the UK Biobank removed. Like Main Text Fig. 1a, the regression used to generate the displayed result used the raw number of children, live births for females and children fathered for males, rather than a binary value for having children. Asterisks indicate significance after Bonferroni correction for 20 tests (P < 2.5 × 10−3; Methods).

Extended Data Fig. 3 Odds ratio estimate for shet burden stratified by age group.

Shown are odds ratio estimates for the association of shet burden with having children, stratified by participant age (y-axis) and separated into females (violet) and males (jade). Age range intervals are left-open. Dash of the line indicates whether the estimate comes from shet burden calculated from deletions (long dash), PTVs (short dash), or from a fixed effects meta-analysis (no dash). Also shown for reference are the results for all individuals regardless of age (All Ages), which is identical to the result shown in main text Fig. 1b. Asterisks indicate significance after Bonferroni correction for 20 tests (P < 2.5 × 10−3; Methods).

Extended Data Fig. 4 Mediation of the relationship between shet burden and childlessness by various disorders.

Plotted is the deletion and PTV meta-analysis -log10 P value for the association between shet burden and having children, corrected by one of 1,294 ICD-10 codes from a combination of general practitioner, hospital episode records, and self-reported conditions (left) or hospital episode records alone (right) separately for males (top) and females (bottom). Remaining ICD-10 codes at different levels on the ICD-10 hierarchy not displayed here are plotted in Supplementary Figs. 8 and 9. Results are ordered first by ICD-10 chapter (x-axis) and then by increasing -log10 P value (y-axis). The arrow for code Z37 indicates the point is below the scale of the y-axis with -log10 P value indicated in parentheses. Visual outliers are labelled and do not imply a significant change in the effect size of shet on childlessness.

Extended Data Fig. 5 Mediation of the association between shet burden and childlessness by various disorders.

Depicted are the results of our primary association between childlessness and individual shet burden corrected for presence/absence of approximately 2,000 different disorders, diseases, and health factors queried from (A,B) hospital episode statistics and (C,D) complete health outcomes data as represented by the ICD-10 medical coding system separately for (A,C) males and (B,D) females (see main text methods). Shown on the x-axis is the -log10 p value for the association of shet with having children, corrected for a given diagnostic code. On the y-axis is the -log10 p value for having a given medical code on likelihood of having children; p values are placed above or below y = 0 based on the direction of effect, with disorders which are associated with having children above and those associated with not having children below. Codes were chosen for labeling to highlight outliers and not based on any statistical criteria. Codes with points at the top or bottom of plots have -log10 p values ≥ 100. Color of points and text is based on the ICD-10 chapter. Please note that text labels do not necessarily represent the full official name of a given ICD-10 code.

Extended Data Fig. 6 Association of eight relevant phenotypes/demographic measures with the likelihood of having children among UK Biobank participants.

Shown are the results of a logistic regression estimating the odds ratio for the relationship of (A) having a partner at home, (B) ever having had sex (C) completing university, (D) having a severe mental health disorder, (E) household income, (F) fluid intelligence, (G) Townsend deprivation index, and (H) engaging in same sex sexual behaviour with likelihood of having children, separated into females (violet) and males (jade). 95% confidence intervals for all plots are included, but may be invisible at the resolution of the figure. Please note that the scales of the x-axis for plots (A) and (B) are different from plots (C-H) due to the relatively stronger association of these traits with having children.

Extended Data Fig. 7 Effect size estimates for the association between shet burden and Townsend Deprivation Index.

Shown are the effect size estimates for the association between shet burden and Townsend Deprivation Index, separated into females (purple) and males (jade). Units are unnormalized Townsend Deprivation Indices for each individual in the UK Biobank. Asterisks indicate significance after Bonferroni correction for 20 tests (p < 2.5 × 10−3; Methods).

Extended Data Fig. 8 The role of individual phenotypes in the relationship between shet burden, childlessness, and fitness.

(A) Odds ratio estimates for the association of cumulative deleterious variation for a combined meta-analysis (deletions + PTVs) with childlessness (middle), corrected for a combination of whether or not a study participant has a mental health (MH) disorder, a partner at home, a university degree, infertility (as ascertained from Complete Health Outcomes Data; Methods), or ever had sex; traits included in each model are indicated as coloured boxes (males – jade, females – violet) on the y-axis. Stars within boxes indicate either nominal (*) or Bonferroni-corrected (**) significance level with childlessness for each covariate independently when correcting for PTV shet burden. For all possible combinations of these traits, see Supplementary Fig. 17. As indicated by coloured boxes, all models include shet burden and were run separately for males and females. The marginal bar plot to the right gives the proportion of the variance in childlessness explained by shet burden as calculated for PTVs only, scaled to the model which only includes shet burden (i.e. the model on the bottom of the plot). (B) Predicted reduction in overall fitness as a factor of individual shet burden. Displayed is the expected reduction in fitness as a factor of increasing shet burden, independently for each sex. Error is shown as the lighter shaded area surrounding the trend line, and is based on the confidence intervals on the odds ratio as determined by our logistic regression model (Fig. 1b; Methods). The dashed line represents the theoretical reduction in fitness as predicted by shet4.

Extended Data Fig. 9 The association of shet burden with childlessness.

Identical to Extended Data Fig. 8B, except in this instance, the y-axis represents predicted childlessness as a factor of individual shet burden, rather than predicted reduction in fitness. Values at x = 0 represent actual mean childlessness among UK Biobank males (jade) and females (violet) with an shet burden of 0.

Supplementary information

Supplementary Information

This file contains Supplementary Figs. 1–21, Supplementary Notes 1–3 and references.

Reporting Summary

Supplementary Table 1

Phenotypes assessed in this study.

Supplementary Table 2

Results from logistic models corrected for ICD-10 codings.

Supplementary Table 3

Covariates for training the CNV random forest QC model.

Supplementary Table 4

Catalogue of odds ratios and betas for associations reported in this manuscript.

Supplementary Table 5

Raw fertility statistics for all Swedish males born 1965–1967.

Supplementary Table 6

Values used for estimating contribution of mental health phenotypes to overall fitness.

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Gardner, E.J., Neville, M.D.C., Samocha, K.E. et al. Reduced reproductive success is associated with selective constraint on human genes. Nature 603, 858–863 (2022). https://doi.org/10.1038/s41586-022-04549-9

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