Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Aug 11;14(1):89.
doi: 10.1186/s13073-022-01088-w.

Limited evidence for blood eQTLs in human sexual dimorphism

Eleonora Porcu  1   2   3 , Annique Claringbould  4   5 , Antoine Weihs  6 , Kaido Lepik  7   8 ; BIOS Consortium; Tom G Richardson  9   10 , Uwe Völker  11   12 , Federico A Santoni  13   14 , Alexander Teumer  12   15 , Lude Franke  4 , Alexandre Reymond #  16 , Zoltán Kutalik #  17   18   19
Affiliations

Limited evidence for blood eQTLs in human sexual dimorphism

Eleonora Porcu et al. Genome Med. .

Abstract

Background: The genetic underpinning of sexual dimorphism is very poorly understood. The prevalence of many diseases differs between men and women, which could be in part caused by sex-specific genetic effects. Nevertheless, only a few published genome-wide association studies (GWAS) were performed separately in each sex. The reported enrichment of expression quantitative trait loci (eQTLs) among GWAS-associated SNPs suggests a potential role of sex-specific eQTLs in the sex-specific genetic mechanism underlying complex traits.

Methods: To explore this scenario, we combined sex-specific whole blood RNA-seq eQTL data from 3447 European individuals included in BIOS Consortium and GWAS data from UK Biobank. Next, to test the presence of sex-biased causal effect of gene expression on complex traits, we performed sex-specific transcriptome-wide Mendelian randomization (TWMR) analyses on the two most sexually dimorphic traits, waist-to-hip ratio (WHR) and testosterone levels. Finally, we performed power analysis to calculate the GWAS sample size needed to observe sex-specific trait associations driven by sex-biased eQTLs.

Results: Among 9 million SNP-gene pairs showing sex-combined associations, we found 18 genes with significant sex-biased cis-eQTLs (FDR 5%). Our phenome-wide association study of the 18 top sex-biased eQTLs on >700 traits unraveled that these eQTLs do not systematically translate into detectable sex-biased trait-associations. In addition, we observed that sex-specific causal effects of gene expression on complex traits are not driven by sex-specific eQTLs. Power analyses using real eQTL- and causal-effect sizes showed that millions of samples would be necessary to observe sex-biased trait associations that are fully driven by sex-biased cis-eQTLs. Compensatory effects may further hamper their detection.

Conclusions: Our results suggest that sex-specific eQTLs in whole blood do not translate to detectable sex-specific trait associations of complex diseases, and vice versa that the observed sex-specific trait associations cannot be explained by sex-specific eQTLs.

PubMed Disclaimer

Conflict of interest statement

T.G.R. is now employed part-time by Novo Nordisk outside of this work. The other authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Manhattan plot of sex-specific eQTLs. The figure summarizes the results of our sex-specific eQTL discovery scan. SNP-gene pairs are plotted on the x-axis according to the SNP position on each autosomal chromosome in alternating light and dark blue against the P-values obtained upon testing for sex difference between effects in men and women (shown as –log10(Pdiff)). The red dotted line marks the 5% FDR threshold significance level (Pdiff=2.6×10−6), and SNPs in loci exceeding this threshold are highlighted in green
Fig. 2
Fig. 2
Heatmap of sex-specific trait-associated SNPs. The figure summarizes the results of sex-specific trait-associations driven by sex-specific eQTLs. For each sex-specific eGenes and for each phenotype, we plotted the minimum Pdiff obtained testing for sex-difference between GWAS-effects in men and women (shown as –log10(Pdiff))
Fig. 3
Fig. 3
Power analysis results. a Plot showing the power to detect significantly different effect in sex-specific GWAS collecting 190,000 females and 170,000 males for different percentiles of causal effect. b Plot showing the total sample size (females + males) needed to detect significantly different effect in sex-specific GWAS at power > 80% for different percentiles of causal effect
See this image and copyright information in PMC

References

    1. Whitacre CC, Reingold SC, O'Looney PA. A gender gap in autoimmunity. Science. 1999;283(5406):1277–1278. doi: 10.1126/science.283.5406.1277. - DOI - PubMed
    1. Lerner DJ, Kannel WB. Patterns of coronary heart disease morbidity and mortality in the sexes: a 26-year follow-up of the Framingham population. Am Heart J. 1986;111(2):383–390. doi: 10.1016/0002-8703(86)90155-9. - DOI - PubMed
    1. Dumitrescu L, et al. Sex differences in the genetic predictors of Alzheimer's pathology. Brain. 2019;142(9):2581–2589. doi: 10.1093/brain/awz206. - DOI - PMC - PubMed
    1. Graham SE, et al. Sex-specific and pleiotropic effects underlying kidney function identified from GWAS meta-analysis. Nat Commun. 2019;10(1):1847. doi: 10.1038/s41467-019-09861-z. - DOI - PMC - PubMed
    1. Hartiala JA, et al. Genome-wide association study and targeted metabolomics identifies sex-specific association of CPS1 with coronary artery disease. Nat Commun. 2016;7:10558. doi: 10.1038/ncomms10558. - DOI - PMC - PubMed

Publication types