MedicalResearch.com Interview with: [caption id="attachment_61964" align="alignleft" width="136"] Ben Petrazzini[/caption] Ben Omega Petrazzini, B.Sc. Associate Bioinformatician Ron Do Laboratory [caption id="attachment_61965" align="alignleft" width="143"] Dr. Ron Do[/caption] Ron Do, Ph.D. Professor, Department of Genetics and Genomic Sciences Director, Center for Genomic Data Analytics Associate Director in Academic Affairs, The Charles Bronfman Institute for Personalized Medicine Charles Bronfman Professor in Personalized Medicine Icahn School of Medicine at Mount Sinai     MedicalResearch.com: What is the background for this study? Response: Rare coding variants directly affect protein function and can inform the role of a gene in disease. Discovery of rare coding variant associations for coronary artery disease (CAD) to date have only had limited success. Genetic studies typically use standard phenotyping approaches to classify cases versus controls for CAD. However, this phenotyping approach doesn’t capture disease progression or severity in individuals. We recently introduced an in-silico score for CAD (ISCAD) that tracks CAD progression, severity, underdiagnosis and mortality (Forrest et al. The Lancet, 2023, PMID 36563696). ISCAD was built using a machine learning model trained on clinical data from electronic health records (EHR). Importantly, ISCAD is a quantitative score that measures CAD on a spectrum. The quantitative nature of the score provides an opportunity to discover additional rare coding variant associations that may not have been detected with the standard case-control phenotyping approach. Here in this study, we performed a large-scale rare variant association study in the exome sequences of 604,915 individuals for ISCAD, a machine learning-based score for CAD.