Genetic variant Evidence using Novel tools to Elucidate Pathophysiology: Accelerating Translation to Health (GenePath) - The ML/AI Tools to Advance Genomic Translational Research (MAGen) consortium is a national, highly functional, network infrastructure that will enhance the accuracy and precision of predicting how individuals with pathogenic variants manifest disease. Our MAGen Coordinating Center (CC) Team is composed of leading experts at Vanderbilt University Medical Center. As the CC, we will support the Development Sites (DS) to synthesize Genetic variant Evidence using Novel tools to Elucidate Pathophysiology: Accelerating Translation to Health’ (called ‘GENEPATH’) as well as support the Consortium in defining critical connections to optimally create tools that provide a holistic prediction with explanation of how a variant causes disease in an individual in the context of their life along with exploring the ethical, legal, and social implications (ELSI) of integrating ML/AI tools into genomic medicine. This work requires that GENEPATH bring together experts on variant interpretation, protein function, genomic medicine, genetic anthropology, informatics, genomic consortium coordination, and all aspects of ELSI, along with our unique skills in consensus building to support DSs in coordination, ELSI research projects, and development of a common data model. Functionally, 1) we will serve as a central home for the Consortium by implementing the Scientific Operations Unit. This Unit will coordinate all Consortium activities including supporting the Consortium and its Steering Committee to establish, monitor, and reach program goals, providing project management with deep knowledge of machine learning, ELSI, and genomic medicine to ensure milestones are met, and structuring Consortium collaboration to promote synergy. 2) We will establish a flexible technical architecture adoptable by development sites through the Data & Machine Learning Unit which will create common data models that handle multi-domain, structured and unstructured data, and plan the cross-validation protocols, as well as collaboratively define specifications for AI/ML variant characterization tools. 3) We will build trust and credibility through the Engagement in ELSI Unit by ensuring authentic communication with patients, communities, and providers to guide Consortium planning. GENEPATH efforts will allow us to broaden our understanding of how variants manifest in disease, leading to a more precise and effective use of genetic variation in research and healthcare.
