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Sex differences in genetic architecture in the UK Biobank

Nature Genetics volume 53, pages 1283–1289 (2021)Cite this article

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Matters Arising to this article was published on 09 January 2023

Abstract

Males and females present differences in complex traits and in the risk of a wide array of diseases. Genotype by sex (GxS) interactions are thought to account for some of these differences. However, the extent and basis of GxS are poorly understood. In the present study, we provide insights into both the scope and the mechanism of GxS across the genome of about 450,000 individuals of European ancestry and 530 complex traits in the UK Biobank. We found small yet widespread differences in genetic architecture across traits. We also found that, in some cases, sex-agnostic analyses may be missing trait-associated loci and looked into possible improvements in the prediction of high-level phenotypes. Finally, we studied the potential functional role of the differences observed through sex-biased gene expression and gene-level analyses. Our results suggest the need to consider sex-aware analyses for future studies to shed light onto possible sex-specific molecular mechanisms.

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Fig. 1: Sex-specific heritability estimate comparison.
Fig. 2: Genetic correlations between the sexes across traits.
Fig. 3: Manhattan plots for traits with most lead sdSNPs.

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Data availability

This research was conducted using the UK Biobank Resource under project 788. Data from the GTEx project v.6p release was also employed. Protected genotype data were accessed via dbGaP and processed gene expression data were downloaded from the GTEx portal, which is openly available: https://gtexportal.org. The GIANT cohort’s summary statistics were employed to compare findings (openly available: https://portals.broadinstitute.org/collaboration/giant), as well as Pulit et al.’s summary statistics pertaining to a GIANT-UKB meta-analysis21 (openly available: https://github.com/lindgrengroup/fatdistnGWAS). The authors declare that the data supporting the findings of the present study are available within the paper and its supplementary information files. The GWAS summary statistics of both autosomal and X-chromosome variants from sex-stratified models are openly available from the University of Edinburgh DataShare repository within the following collection: https://datashare.ed.ac.uk/handle/10283/3908 (clinical binary traits https://doi.org/10.7488/ds/3046, nonclinical binary traits https://doi.org/10.7488/ds/3047, nonbinary traits https://doi.org/10.7488/ds/3048 and LogMM results https://doi.org/10.7488/ds/3049).

Code availability

We used DISSECT (v.1.15.2c, 24 May 2018, which is publicly available at http://www.dissect.ed.ac.uk under GNU Lesser General Public License v.3), PLINK (v.1.9 and v.2.0, freely available online at https://www.cog-genomics.org/plink2), BGENIX (v.1.0 freely available online at https://bitbucket.org/gavinband/bgen), LD score regression (v.1.0.1, freely available online at https://github.com/bulik/ldsc), MAGMA (v.1.06, freely available online at https://ctg.cncr.nl/software/magma), FUMA (freely available online at https://fuma.ctglab.nl), GCTA (v.1.91.4, freely available at https://cnsgenomics.com/software/gcta) and REGENIE (v.1.0.7, freely available online at https://github.com/rgcgithub/regenie/tree/v1.0.7-latest). Customized code created to perform the analysis is openly available (https://zenodo.org/record/4844680), with https://doi.org/10.5281/zenodo.4844680.

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Acknowledgements

This research was funded by the BBSRC through program grants (nos. BBS/E/D/10002070 (to A. Tenesa), BBS/E/D/30002275 (to A. Tenesa) and BBS/E/D/30002276 (to A. Tenesa, J.P., K.R. and A. Talenti)); MRC research grants (nos. MR/P015514/1 (to A. Tenesa) and MR/R025851/1 (to A. Tenesa and O.C.-X.)); HDR-UK award (no. HDR-9004 (to A. Tenesa)); and the Roslin Foundation’s Steve Bishop PhD Fellowship (to E.B.). This research has been conducted using the UK Biobank Resource under project 788.

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Authors and Affiliations

  1. The Roslin Institute, Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Easter Bush Campus, Midlothian, UK

    Elena Bernabeu, Konrad Rawlik, Andrea Talenti, James Prendergast & Albert Tenesa

  2. Institute of Genetics and Cancer, University of Edinburgh, Western General Hospital, Edinburgh, UK

    Oriol Canela-Xandri & Albert Tenesa

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  1. Elena Bernabeu
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Contributions

A. Tenesa conceived the study. A. Tenesa, O.C.-X., K.R. and E.B. designed the genetic architecture, prediction, masking and eQTL analyses. A. Talenti and J.P. designed the gene-level analyses. O.C.-X., K.R. and E.B. pre-processed the data and conducted modeling. E.B. conducted the statistical analyses and prepared the initial manuscript. All authors contributed and commented on the development of the manuscript.

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Correspondence to Albert Tenesa.

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Peer review information Nature Genetics thanks the anonymous reviewers for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Genetic and residual variance fold differences between the sexes.

Barplot of log2 fold difference in variance between males and females for binary (top) and non-binary (bottom) traits with a significantly different heritability between the sexes at a q < 0.05 threshold. Pink bars represent fold change between the sexes in genetic variance, and blue bars represent fold change between the sexes in residual variance. Fold change is calculated as log2(male variance/female variance), thus positive fold change = larger variance in males, negative fold change = larger variance in females.

Extended Data Fig. 2 Comparison of genetic variance and evolvability estimates between the sexes.

Top plots: scatterplots comparing male genetic variance to female genetic variance for binary (left) and non-binary (right) traits. Bottom plots: scatterplots comparing male evolvability to female evolvability for binary (left) and non-binary (right) traits. Each point represents a trait, and pink points indicate traits for which the genomic parameter considered (genetic variance, evolvability) between the sexes is significantly different (q < 0.05, see Methods). Basal metabolic rate was removed as an outlier.

Extended Data Fig. 3 Genetic correlations across studies.

Barplot comparing genetic correlation estimates from this effort to the literature for several non-binary traits (‘WHR’: waist-hip circumference ratio, ‘Body Fat %’: body fat percentage, ‘BMI’: body-mass index, ‘WC’: waist circumference, ‘HC’: hip circumference, and ‘Height’: standing height). Height of bar indicates genetic correlation estimate (rg), different colors corresponding to different publications, as represented in legend by surname of first author12,14,16 and the values of which, are shown in Supplementary Table 2. Error bars represent 95% confidence intervals of rg estimates (rg + /−1.96SErg).

Extended Data Fig. 4 Distribution of sdSNPs across the genome.

Manhattan plot of number of sdSNPs (p < 1 × 10−8, two-sided t-test, see Methods) per genomic position, each point representing a genetic variant, and its height the number of traits it affects in a sexually different manner, for (a) non-binary traits and (b) binary traits.

Extended Data Fig. 5 Distributions of potentially masked SNPs.

For binary (left) and non-binary (right) traits with at least one potentially masked variant: (a) histogram of number of masked genetic variants, (b) histogram of proportion of masked variants that presented opposite sign effects between the sexes, (c) histogram of proportion of masked SNPs that were found to possess significantly different genetic effects between the sexes (two-sided t-test, p < 1 × 10−8 threshold, see Methods) and (d) histogram of the proportion of lead sdSNPs that were found to be masked.

Extended Data Fig. 6 Comparison of number of lead sdSNPs to number of masked variants.

For binary (left) and non-binary (right) traits with at least one sdSNP and one potentially masked variant, shown is a scatterplot of number of lead sdSNPs against number of potentially masked variants (p < 1 × 10−8, and LD clumped within sex, see Methods).

Extended Data Fig. 7 Gene-set enrichment heatmap.

Heatmap with hierarchical clustering of FUMA set enrichment -log10 q-values for 251 gene sets that were found to be significantly differentially enriched between males and females (Fisher’s exact test q < 0.05), as well as significantly differentially enriched to GWAS background genes (Fisher’s exact test q < 0.05). x-axis corresponds to male or female dominant genes (see Methods) for different traits, and y axis to gene sets. Color within plot represents scaled -log10 q-value of enrichment, and ‘Sex’ horizontal column indicates the sex in which the genes considered are dominant (pink for females, and blue for males).

Extended Data Fig. 8 Sex-biased eQTL plot for rs56705452.

Relationship of genotype at variant rs56705452 with the expression of the transcript ENSG00000204520.8 in muscle skeletal tissue, for males (pink) and females (blue). Each point represents an individual, the x-axis its genotype at the considered variant, and the y-axis gene expression levels for the considered transcript.

Supplementary information

Supplementary Information

Supplementary Methods, Results, Figs. 1–9 and captions for Supplementary Tables 1–24.

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Supplementary Tables

Supplementary Tables 1–24. Captions for each table included in Supplementary Note.

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Bernabeu, E., Canela-Xandri, O., Rawlik, K. et al. Sex differences in genetic architecture in the UK Biobank. Nat Genet 53, 1283–1289 (2021). https://doi.org/10.1038/s41588-021-00912-0

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