Association Analysis of Rare Variants in Endometriosis (Epidemiology, Biostatistics, and Public Health, 2025)
INTRODUCTIONEndometriosis is a chronic, inflammatory gynecological condition characterized by the presence of endometrial-like tissue outside the uterus. It appears in various forms and can be classified based on location into several subtypes: superficial peritoneal, ovarian, deep infiltrating, extra-abdominal, and iatrogenic [1]. In recent years, advances in genetics have underscored the complex, multifactorial nature of endometriosis. Genome-wide association studies (GWAS) have identified common genetic variants linked to disease susceptibility. However, Whole Exome Sequencing (WES) has emerged as a powerful tool for uncovering rare, high-impact mutations that may explain severe or familial cases. WES not only complements GWAS findings but also opens new ways for understanding disease mechanisms, identifying biomarkers, and developing personalized therapeutic strategies. AIMThe aim of this study is to identify and compare genetic variants present in a cohort of healthy women and women with endometriosis by using WES. The goal is to discover potential genetic biomarkers and gain a better understanding of the molecular mechanisms underlying the disease, to improve early diagnosis, prognosis, and the development of targeted treatments for women with endometriosis. METHODSThis case-control study included 400 Italian women, 200 with laparoscopically confirmed endometriosis and 200 healthy controls. A key focus of the study was an in-depth quality control analysis: particular attention was devoted to evaluating the distribution and consistency of sequencing quality metrics—such as average coverage, read depth, genotype quality, and mapping quality—across the target regions. This thorough assessment was crucial to ensure homogeneous and comparable data across all samples. Only variants meeting stringent criteria—read depth >10, genotype quality ≥30, and mapping quality ≥40—and located in regions shared by at least 95% of the samples were retained, strictly following GATK best practices. This rigorous filtering was essential to achieve consistent variant calling across the cohort. To support this process, a comprehensive bioinformatic pipeline was developed for the processing, filtering, and statistical analysis of genetic data, ensuring high reliability and reproducibility across the entire workflow. Following quality control, we focused on rare (MAF