Matthew J. Welland, K. D. Ahlquist, Paul De Fazio, Christina Austin-Tse, Lynn Pais, Laura Wedd, Samantha Bryen, Rocío Rius, Michael Franklin, Caitlin Morrison, Giles Hall, Laura Gauthier, Alex Bloemendal, David Francis, Andrew Mallett, Amali Mallawaarachchi, Paul J. Lockhart, Richard J. Leventer, Ingrid E. Scheffer, Katherine B. Howell, Karin S. Kassahn, Hamish S. Scott, Julie McGaughran, John Christodoulou, David R. Thorburn, Bryony A. Thompson, Chirag V. Patel, Greg Smith, Anne O’Donnell‐Luria, Simon Sadedin, Heidi L. Rehm, Sebastian Lunke, Jeremiah Wander, Kaitlin E. Samocha, Cas Simons, Daniel G. MacArthur, Zornitza Stark
Reanalysis of genomic data in rare disease is highly effective in increasing diagnostic yields but remains limited by manual approaches. Automation and optimization for high specificity will be necessary to ensure scalability, adoption and sustainability of iterative reanalysis. We developed Talos, an open-source tool that automates variant prioritization by integrating dynamically updated gene-disease and variant-level evidence with inheritance-aware filtering and validated its performance using data from 1,089 individuals with rare disease. Trio-based analysis identified 90% of known diagnoses, returning 1.3 variants per case on average. Variant burden reduced to one variant per 200 cases on iterative monthly reanalysis. Application to an unselected cohort of 4,735 undiagnosed individuals identified 241 diagnoses (5.1% yield): 78 (32%) due to new gene-disease relationships, 54 (22%) due to new variant-level evidence and 109 (45%) due to improved analysis strategies. Our automated, iterative reanalysis model demonstrates the feasibility of delivering frequent, systematic reanalysis at scale.