Enhancing genetic association power in endometriosis through unsupervised clustering of clinical subtypes identified from electronic health records.

Endometriosis is a complex and heterogeneous condition affecting 10% of reproductive-age women, and yet, it often goes undiagnosed for several years. Limited observed heritability (7%) of large genetic association studies may be attributable to underlying heterogeneity of disease mechanisms. Therefore, we conducted this study to investigate genetic associations across sub-phenotypes of endometriosis. We performed unsupervised clustering of 4,078 women with endometriosis based on known endometriosis risk factors, symptoms, and concomitant conditions. The clusters were characterized by examining electronic health record (EHR) data and comprehensive chart reviews. We then performed genetic association for each cluster with 39 endometriosis-associated loci (Total endometriosis cases = 12,350). We identified five sub-phenotype clusters: (1) pain comorbidities, (2) uterine disorders, (3) pregnancy complications, (4) cardiometabolic comorbidities, and (5) EHR-asymptomatic. Bonferroni significant loci included PDLIM5 for the cluster 1, GREB1 for cluster 2, WNT4 for cluster 3, RNLS for cluster 4, and ABO for cluster 5. The difference in associations between the groups suggests complex and varied genetic mechanisms of endometriosis and its symptoms. This study enhances our understanding of the clinical patterns of endometriosis sub-phenotypes, showcasing the innovative approach employed to investigate this complex disease.