Genetic research and diagnosis of Endometriosis


Endometriosis is a gynaecological disease affecting around 10% of women of reproductive age. The main symptoms of endometriosis are pain and infertility, the effects of which can severely impact the day-to-day lives of affected individuals. It is generally accepted that endometriosis is caused by the implantation of tissue resembling endometrium tissue outside of the womb (Lalami et al., 2021).


Endometriosis is a notoriously difficult disease to diagnose for many reasons. One of the factors is the uncertainty that exists regarding the pathogenesis of endometriosis. Another factor is that the disease is generally not well understood by healthcare professionals. Furthermore, in comparison to many other diseases, it is far less homogenous. Given that there are three phenotypes of endometriosis, individuals can be asymptomatic, as well as carry the potential comorbid presence of adenomyosis.


The use of transvaginal ultrasound (TVAS) and MRI scans have greatly aided the process of diagnosing and distinguishing between two of the phenotypes (OMA and DIE) of the disease. However, it is not a catch-all diagnosis method since SUP may not show up in the imaging due to the size of the lesions being below the threshold for detection.


Identifying the genes related to Endometriosis is crucial for developing a better understanding of the disease. It can aid diagnosis and provide the information required to formulate a personalised medicine approach to an individual’s treatment. The heritability estimation of endometriosis is around 50% which has prompted researchers to undertake further investigation into the genes possibly associated with the disease.


There have been two primary modes of investigation, researchers began with Genome-Wide Association Studies (GWAS). The existence of several loci, including one near CCD42 and another near CDKN2A, were discovered to indicate an individual is at risk of the disease. A study found fourteen SNPs that were likely associated with endometriosis risk (chromosome 1, 2, 6, 7, 9 and 12; WNT4, GREB1, FN1, IL1A, ETAA1, RND3, ID4, NFE2L3, CDKN2B-AS1, VEZT, SYNE1, FSHB, ESR1, ARL14EP). However, the results were conflicting as the SNPs were localised in intervened and intronic regions. A significant GWAS study was based upon 5292 controls and 1907 cases and found an association between endometriosis and CDKN2B-AS1, on chromosome 9p21, and a possible association with WNT4, which has although been thought to be involved in female sex determination. Another study found that rs12700667 on chromosome 7p15.2, in the intergenic region located between NFE2L3 and HOXA10 (OR = 1.22) was significantly associated with the disease. That study consisted of 3194 cases and 7060 controls. Several unexpected findings were uncovered during the research. A study found that endometriosis and depression were linked by similar risk loci with an association with the gastric mucosa. Endometriosis was also found to be linked to migraines. The analysis found that Endometriosis and Migraine share their top 1% of most significant SNPs (Lalami et al., 2021).


Identification of underlying mutations in gynaecological diseases is of crucial importance to researchers, clinicians and patients due to the heterogeneous nature of the genome and different clinical manifestations. Selecting suitable cost-effectiveness genetic tests based on coverage area, and sequencing depth can improve diagnosis, treatments, and prevention. Targeted sequencing enriches specific genomic regions to allow sequencing reads to be optimised in regions that are of interest, producing enhanced coverage in specific areas with unrivalled cost-effectiveness. egSEQ hybridisation capture and amplicon-based targeted panels offer a complete solution from assay design through advanced bioinformatics analysis with different options for targeting genes of interest.


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Citations:

Lalami, I., Abo, C., Borghese, B., Chapron, C., & Vaiman, D. (2021). Genomics of endometriosis: From genome wide association studies to exome sequencing. International Journal of Molecular Sciences, 22(14), 7297.


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