Abstract: The integration of genomics with electronic health records (EHRs) has transformed the landscape of biomedical research, offering unprecedented opportunities to uncover the genetic underpinnings of complex diseases and enhance risk prediction by combining clinical and molecular data. Conditions such as endometriosis, which affect millions globally and exhibit significant heterogeneity in presentation and progression, highlight the need for innovative approaches that bridge discovery and translational science. By leveraging methods and tools for genetic association studies, advanced risk modeling, and the integration of genomic and clinical data, researchers can explore disease mechanisms more deeply, uncover novel biomarkers, and refine strategies for individualized care. These advancements not only deepen our understanding of complex conditions but also provide a pathway for developing precision health solutions that address critical gaps in women’s health research.
Bio: Dr. Shefali Setia Verma, Ph.D., is an Assistant Professor in the Department of Pathology and Laboratory Medicine. As a biomedical and computational geneticist, she has expertise in developing and implementing statistical methods and tools to model the underlying genetic mechanisms of complex human diseases. Through her work on harnessing electronic health record (EHR) data for understanding the genetic underpinnings of common diseases, Dr. Verma has conducted multiple studies to determine the associations between specific genetic variations and diseases and has shown the utility of EHR-linked biobanks for discoveries in understanding disease mechanisms. She has also been an integral part of the Penn Medicine BioBank (PMBB) team since joining Penn, and her work has highlighted the use of EHR for delivering results back to patients. Since starting her lab in the Department of Pathology and Laboratory Medicine, Dr. Verma’s work has focused on developing EHR-based algorithms for discovering and clinical implementation of diseases unique to females. Dr. Verma describes her vision as developing automated statistical models for providing precision medicine in day-to-day clinical practice for the next-generations’ of healthcare.