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. 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.

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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
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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

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