Expanding genetic diagnoses into non-coding regions of the genome

This project is suitable for Honours, Masters, MPhil, MD.

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/s

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.

Method

We use a range of computational methods and statistics, but can support across levels of skill and experience. Using clinical and public data, we investigate research tools to determine if they can be applied in clinical genomic diagnostics and evaluate their predictive power and impact to determine clinical recommendations. We will access a variety of publicly available data and software, with analysis techniques including those used in health quality assessment and diagnostic evaluation.

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.