A large-scale genome-wide association study on female genital tract polyps highlights role of DNA repair, cell proliferation, and cell growth.

Study question: Can a large-scale genome-wide association study (GWAS) meta-analysis identify genomic risk loci and likely involved genes for female genital tract (FGT) polyps, provide insights into the biological mechanism underlying their development, and inform of potential overlap with other traits, including endometrial cancer?

Summary answer: GWAS meta-analysis of FGT polyps highlights potentially shared mechanisms between polyp development and cancerous processes.

What is known already: Small-scale candidate gene studies have focused on biological processes such as oestrogen stimulation and inflammation to clarify the biology behind FGT polyps. However, the exact mechanism for the development of polyps is still elusive. At the same time, a genome-wide approach, which has become the gold standard in complex disease genetics, has never been used to uncover the genetics of the FGT polyps.

Study design, size, duration: We performed a GWAS meta-analysis including a total of 36 984 women with FGT polyps (International Classification of Diseases (ICD-10) diagnosis code N84) and 420 993 female controls (without N84 code) of European ancestry from the FinnGen study (11 092 cases and 94 394 controls), Estonian Biobank (EstBB, 14 008 cases and 112 799 controls), and the Pan-UKBB study (11 884 cases and 213 800 controls).

Participants/materials, setting, methods: GWAS meta-analysis and functional annotation of GWAS signals were performed to identify genetic risk loci and prioritize genes in associated loci. To explore associations with other traits, we performed a look-up of associated variants across multiple traits and health conditions, genetic correlation analysis, and phenome-wide association study (PheWAS) with ICD-10 diagnosis codes.

Main results and the role of chance: Our GWAS meta-analysis revealed 16 significant (P < 5 × 10-8) genomic risk loci. Based on exonic variants in GWAS signals, we prioritized EEFSEC, ODF3, PRIM1, PLCE1, LRRC34/MYNN, EXO1, and CHEK2 which are involved in DNA repair, cell proliferation, and cell growth. Several of the identified genomic loci have previously been linked to endometrial cancer and/or uterine fibroids, highlighting the potentially shared mechanisms underlying tissue overgrowth and cancerous processes. Genetic correlation analysis revealed a positive correlation with body mass index and reproductive traits, that can be classified as symptoms or risk factors of endometrial polyps (EPs), whereas a negative correlation was observed between FGT polyps and both menopause (genetic correlation estimate (rg) = -0.29, SE = 0.08, P = 8.8×10-4) and sex hormone-binding globulin (SHBG) (rg = -0.22, SE = 0.04, P = 2.4×10-8). On the phenotypic level, the strongest associations were observed with endometriosis, uterine fibroids, and excessive, frequent, and irregular menstruation.

Large scale data: The complete GWAS summary statistics will be made available after publication through the GWAS Catalog (https://www.ebi.ac.uk/gwas/).

Limitations, reasons for caution: In this study, we focused broadly on FGT polyps and did not differentiate between the polyp subtypes. Considering the prevalence of FGT polyp subtypes, we assumed that most women included in the study had EPs. Further research on the expression profile of FGT polyps could complement the GWAS study to substantiate the functional importance of the identified variants.

Wider implications of the findings: The study findings have the potential to significantly enhance our understanding of the genetic mechanisms involved, paving the way for future functional follow-up, which in turn could improve the diagnosis, risk assessment, and targeted treatment options, since surgery is the only line of treatment available for diagnosed polyps.

Study funding/competing interest(s): This study was funded by European Union through the European Regional Development Fund Project No. 2014-2020.4.01.15-0012 GENTRANSMED. Computations were performed in the High-Performance Computing Center of the University of Tartu. The study was also supported by the Estonian Research Council (grant no. PRG1076 and MOBJD1056) and Horizon 2020 innovation grant (ERIN, grant no. EU952516). All the authors declared no conflict of interest.