Human endometrium is a highly specialised tissue lining the inside of the uterus and is essential for female reproduction. Understanding genetic regulatory mechanisms shared between, and specific to, tissue types can aid identification of target genes relevant to disease pathways. Endometrium is considered a source of cells initiating lesions in endometriosis which occurs when tissue, similar to the endometrium, forms lesions outside the uterus. The disease affects 10% of reproductive aged women and costs the Australian economy $7.4 billion annually. Genome-wide association analyses have identified 27 endometriosis risk loci. However, specific gene targets and genetic mechanism’s behind the disease remain to be identified.
To better understand tissue specific genomic regulation in endometrium we analysed RNA-sequencing data from 206 endometrial samples identifying novel effects of genetic variation on gene expression in endometrium and compared these with datasets in other tissues. A total of 444 sentinel cis-eQTLs (P<2.57x10-9) were detected including 327 novel cis-eQTLs that have not been reported in endometrium previously. A large proportion (85%) of endometrial eQTLs were shared between tissues within the GTEx consortium and with the eQTLGen blood dataset. Genetic effects on gene expression in endometrium were highly correlated with reproductive (eg. uterus, ovary, vagina) and digestive tissues (eg. salivary gland, stomach), supporting evidence that genetic regulation of gene expression is shared between biologically similar tissues and cell types. Tissue enrichment analysis using endometriosis GWAS summary statistics showed that reproductive tissues were significantly enriched for expression of genes in endometriosis risk loci. Summary-data-based Mendelian Randomisation analyses identified putative functional genes associated with reproductive traits and diseases including endometriosis.
We identify strong genetic effects on transcription in endometrium. The results can be applied together with publicly available datasets to identify targets for endometrium-related traits and pathologies.