Reanalysis of genomic data in rare disease: current practice and attitudes among Australian clinical and laboratory genetics services

• Published in: European journal of human genetics : EJHG
• Main Authors: Best, Stephanie, Fehlberg, Zoe, Richards, Christopher, Quinn, Michael C J, Lunke, Sebastian, Spurdle, Amanda B, Kassahn, Karin S, Patel, Chirag, Vears, Danya F, Goranitis, Ilias, Lynch, Fiona, Robertson, Alan, Tudini, Emma, Christodoulou, John, Scott, Hamish, McGaughran, Julie, Stark, Zornitza
• Format: Journal Article
• Language: English
• Published: England 25.05.2024
• ISSN: 1018-4813; 1476-5438
• DOI: 10.1038/s41431-024-01633-8

Reanalyzing stored genomic data over time is highly effective in increasing diagnostic yield in rare disease. Automation holds the promise of delivering the benefits of reanalysis at scale. Our study aimed to understand current reanalysis practices among Australian clinical and laboratory genetics services and explore attitudes towards large-scale automated re-analysis. We collected audit data regarding testing and reanalysis volumes, policies and procedures from all Australian diagnostic laboratories providing rare disease genomic testing. A genetic health professionals' survey explored current practices, barriers to reanalysis, preferences and attitudes towards automation. Between 2018 and 2021, Australian diagnostic laboratories performed over 25,000 new genomic tests and 950 reanalyses, predominantly in response to clinician requests. Laboratory and clinical genetic health professionals (N = 134) identified workforce capacity as the principal barrier to reanalysis. No specific laboratory or clinical guidelines for genomic data reanalysis or policies were identified nationally. Perceptions of acceptability and feasibility of automating reanalysis were positive, with professionals emphasizing clinical and workflow benefits. In conclusion, there is a large and rapidly growing unmet need for reanalysis of existing genomic data. Beyond developing scalable automated reanalysis pipelines, leadership and policy are needed to successfully transform service delivery models and maximize clinical benefit.