Abstract
The genetic landscape of human Mendelian diseases is shaped by mutation and selection. Although selection on heterozygotes is well-established in autosomal-dominant disorders, convincing evidence for selection in carriers of pathogenic variants associated with recessive conditions is limited. Here, we studied heterozygous pathogenic variants in 1,929 genes, which cause recessive diseases when bi-allelic, in n = 378,751 unrelated European individuals from the UK Biobank. We find evidence suggesting fitness effects in heterozygous carriers for recessive genes, especially for variants in constrained genes across a broad range of diseases. Our data suggest reproductive effects at the population level, and hence natural selection, for autosomal-recessive disease variants. Further, variants in genes that underlie intellectual disability are associated with lower educational attainment in carriers, and we observe an altered genetic landscape, characterized by a threefold reduction in the calculated frequency of bi-allelic intellectual disability in the population relative to other recessive disorders.
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Data availability
The raw data used in this study are available as part of the UK Biobank dataset.
Code availability
The code used to generate the data for this project is available via GitHub at https://github.com/Genome-Bioinformatics-RadboudUMC/ukbb_recessive_public.
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Acknowledgements
We thank E. Gardner, J. Hampstead, H. Martin and M. Hurles for fruitful discussions and S. Carmi for advising about population genetics matters. This research has been conducted using the UK Biobank Resource under application number 66493. This project was financially supported by a VIDI grant from the Dutch Research Council (grant no. 917-17-353 to C.G.) and an AI for Health PhD grant from Radboudumc. This project was supported by a gift from the Koum Foundation (to E.L.-L.). E.L.-L. is Robin Chemers Neustein Director of Medical Genetics.
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H.G.B., C.G. and E.L.-L. supervised the study; H.F. and G.K. developed a data collection pipeline and statistical methods and analysed data. H.F., G.K., H.G.B., C.G. and E.L.-L. wrote the manuscript.
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Extended data
Extended Data Fig. 1 Association of genetic burden for recessive disease with childlessness for different constraint scores.
Associations of genetic burden with childlessness and hair color (as a control phenotype) for heterozygous PLPs in recessive genes (purple), PLPs in recessive genes excluding carriers of LoF in non-recessive genes (orange), and singleton LoFs in highly constrained (s-het > 0.15) non-recessive genes (green). Colored lines indicate odds ratios for the phenotypes with 99% confidence intervals. The dashed gray line indicates OR = 1, which serves as the reference point. Statistically significant deviations from OR = 1 are tested using a two-sided Wald test. The corresponding p-values are displayed in the figure and adjusted for multiple comparisons using the Bonferroni correction. Statistically significant associations are marked with an asterisk. The results are for three different s-het scores: Weghorn (a), Cassa (b) and pLI (c).
Extended Data Fig. 2 Simulations of the effect of sample size on detection of the association with childlessness.
Simulated estimation of the odds-ratios (a) and corresponding p-values (b), (c) depicted with a spread (95% percentile interval for n = 20 simulations per cohort size) for the effect of cohort size on the association of childlessness with the genetic burden of PLPs. Shown are simulation results for heterozygous carriers of PLPs in recessive genes (purple) and of carriers of singleton LoFs carriers in non-recessive highly constrained genes (green). The dotted gray line marks the significance level of P = 0.05.
Extended Data Fig. 3 Association of genetic burden for recessive disease with educational attainment, fluid intelligence score, and log-transformed number of ICD-10 diagnoses for different constraint scores.
Associations of genetic burden with educational attainment (measured in years of education), fluid intelligence score and log-transformed number of ICD-10 diagnoses for PLPs in all recessive (purple) genes and singleton LoFs in non-recessive highly constrained (green) genes. Colored lines indicate effect sizes (ES; estimated regression coefficients) with 99% confidence intervals. The dashed gray line indicates ES = 0, which serves as the reference point. Statistically significant deviations from ES = 0 are tested using a two-sided Wald test. The corresponding p-values are displayed in the figure and adjusted for multiple comparisons using the Bonferroni correction. Statistically significant associations are marked with an asterisk. The results are for three different s-het scores: Weghorn (a), Cassa (b) and pLI (c).
Extended Data Fig. 4 Association of genetic burden with various phenotypes for PLPs in recessive ID genes and all other recessive genes, using different constraint scores.
The comparison between PLPs in recessive ID genes (red) and all other recessive genes (blue) for the effects of genetic burden on childlessness, educational attainment (measured in years of education), log-transformed number of ICD-10 diagnoses, and hair color (as a control phenotype). The results are for three different s-het scores: Weghorn (a), Cassa (b), and pLI (c). Colored lines indicate odds ratio (OR; for childlessness and hair color) or effect sizes (ES; estimated regression coefficients) with 99% confidence intervals; dashed gray line indicates the OR = 1 (for childlessness and hair color) or ES = 0, which serve as the reference point. Statistically significant deviations from these points are tested using a two-sided Wald test. The corresponding p-values are displayed in the figure and adjusted for multiple comparisons using the Bonferroni correction. Statistically significant associations are marked with an asterisk.
Extended Data Fig. 5 Consanguinity ratio scores (CR) for different disorder groups in three European populations.
Consanguinity ratio scores (CRs) for 13 disorder groups calculated for 3 European populations: UK Biobank (blue), Dutch (magenta) and Estonian (orange) cohorts. Scores for the Dutch and Estonian cohorts are taken from Fridman et al.; Scores marked in asterisk are significantly higher than seen in a random set of recessive genes with the same coding length (Methods). We note that CR scores for some of the other disorder groups (Skeletal, Neuromuscular, Hematologic) are similar to or higher than the CR score obtained for ID genes, yet these did not reach significance.
Extended Data Fig. 6 Correlation of allele frequencies in disease categories between a Dutch cohort and the UK Biobank.
Correlation of average PLP allele frequencies (AF) per disorder group between the UK Biobank and Dutch cohorts with 95% confidence interval for the regression estimate (derived from n = 1,000 bootstrap samples). The X-axis shows the AF in the UK Biobank cohort, Y-axis shows the AF in the Dutch cohort. Pearson correlation ρ = 0.791, 95%CI = [0.425, 0.935], two-sided non-adjusted P = 0.001. AF for the Dutch cohort are taken from Fridman et al.
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Fridman, H., Khazeeva, G., Levy-Lahad, E. et al. Reproductive and cognitive phenotypes in carriers of recessive pathogenic variants. Nat Hum Behav (2025). https://doi.org/10.1038/s41562-025-02204-7
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DOI: https://doi.org/10.1038/s41562-025-02204-7
