More than 200,000 women are diagnosed with endometriosis every year and over half of those women do not receive a definitive diagnosis until 8.5 years after the onset of symptoms and many times when they present with additional comorbidities. While several studies have suggested that genomic markers, environmental risk factors and inflammatory markers play crucial roles in endometriosis symptomatology, there are no effective tools available to predict an individual's risk of developing endometriosis or to predict its downstream effects. The long-term goal is to develop effective and non-invasive early screening tools to identify patients at risk of developing endometriosis and predict long-term effects. The main objective of this project is the development of models to predict the risk of endometriosis across varied clinical manifestations and associated long-term health outcomes. Our central hypothesis is that integrative risk models will successfully identify patients at risk of developing endometriosis and associated diseases that occur either concurrently with endometriosis (reproductive age) or after endometriosis development (long-term health outcomes), enabling early diagnosis and prevention. This general hypothesis will be tested via the following specific aims:(1) Develop an integrative risk model to predict patients at high risk of developing endometriosis; (2) Develop an integrative risk model combining genetic and nongenetic risk factors to predict clinical manifestations among women with endometriosis ; (3) Create a lifelong chronological map of endometriosis to identify individuals at risk of developing associated comorbidities. In aim 1, we will integrate genetic and non-genetic risk factors extracted from Electronic Health Records in linear and non-linear fashion to generate an EndoRisk model. In aim 2, we will generate a catalog of additional risk factors linked to various clinical manifestations of endometriosis and develop risk model for varied manifestations. In aim 3, we will evaluate mediating risk of endometriosis on associated comorbidities and develop a mediator risk prediction model for concomitant conditions and long-term health outcomes. At the successful completion of the proposed research, the expected outcomes will be rigorously evaluated non-invasive computational methods for screening and diagnosing endometriosis across various clinical manifestations and its long-term effects based on genetic and non-genetic factors. The proposed research is innovative because our novel methodology for integrated risk models will have immediate translational implications. These results will provide a strong basis for future development of strategies for improving patient outcomes and translating the knowledge to clinical practice by providing support for identifying patients at high, moderate, and mild risk of endometriosis, which is expected to have a significant impact on women suffering from endometriosis or its long-term effe...