Background
A molecular diagnosis informs many aspects of treatment for a patient with an inherited condition, however current techniques provide a diagnosis in only around 25% of cases. Non-coding regions of the genome remain underrepresented in clinical cases and variants in these regions are therefore a potential source of diagnoses for undiagnosed patients. Non-coding variants remain elusive as there is insufficient evidence to predict their impact and/or disease-causality. In order to improve diagnosis in these regions, we are working to determine which of the current research tools provide sufficient evidence to predict disease-causality in a clinical setting. We are testing methods to determine how to apply bioinformatic tools with best predictive power; to provide evidence based clinical recommendations for clinical diagnostics inclusive of non-coding regions. This work will increase diagnostic yield for patients undergoing genetic testing.
Aim
To improve diagnostic yield for patients with inherited disease by evidencing new methods for applying computational and experimental evidence in variant curation across expanded areas of the genome.
Project Potential
The projects that we offer are very applied and translate well for clinical understanding of genomics and genetic variant curation. We also support building bioinformatic, coding skills, statistics along with research translation and implementation projects aligned with this area. This project has real world translational potential as it will provide results enabling improved clinical diagnostic practice, to improve health care for patients.