Identification and Validation of Novel Combinatorial Genetic Risk Factors for Endometriosis across Multiple UK and US Patient Cohorts.

Background: Endometriosis affects about 10% of women usually of reproductive age. It often has severe negative impacts on patients' quality of life, but the average time to a definitive diagnosis remains 7-9 years, and there are few effective therapeutic options. Relatively little is known about the genetic drivers of the disease even though its heritability is fairly high. A recent large genome wide association study (GWAS) meta-analysis identified 42 genomic loci associated with risk of endometriosis, but together these explain only 5% of disease variance. Methods: We used the PrecisionLife Results: We identified 1,709 disease signatures, comprising 2,957 unique SNPs in combinations of 2-5 SNPs, that were associated with increased prevalence of endometriosis in UKB. Pathways enriched in the disease signatures included cell adhesion, proliferation and migration, cytoskeleton remodeling, angiogenesis as well as biological processes involved in fibrosis and neuropathic pain.We observed a significant enrichment of these signatures (58-88%, Conclusion: Although using much smaller, less well-characterized datasets than the previous whole genome meta-GWAS study, combinatorial analysis has provided important new insights into the genetics and biology of endometriosis including reproducible biologically relevant genes that are overlooked by GWAS approaches.The 75 novel gene associations provide new insights and routes for study of the disease and potential new therapies. Several of the novel genes identified are credible targets for drug discovery, repurposing and/or repositioning. Using the disease signatures identified as genetic biomarkers in trials of candidates drugs targeting specific mechanisms will enable precision medicine-based approaches. We hope this will encourage new targeted therapy discovery efforts.