Whole genome and exome sequencing have had a tremendous impact on diagnosing disease and identifying genes. While hundreds of unique disease-associated genes have been described as a result of this technology in the past five years, identifying gene changes that cause disease is often challenging due to the large number of rare changes (variants) that continue to be revealed.
Leveraging standard methods and information models to describe the clinical phenotype will not only assist in annotating, filtering, and prioritising likely causative variants but will also enable information processes like AI analysis to be automated. Establishing national guidelines for clinical phenotype data standards will enable interoperability across health systems, support diagnostic and research analysis and assist in supporting national data sharing of clinical diagnostic data.
This project will identify current state and territory clinical phenotype data collection methods and identify procedures to facilitate high-quality standardised data capture to facilitate national clinical data sharing. Initial collection of clinical data through electronic test request forms will also be investigated which will greatly support clinical diagnostic teams by providing the high-quality clinical data required for variant interpretation.
Professor Jozef Gecz
University of Adelaide