Use of integrated genomic and functional genomic approaches to the diagnosis of rare inherited disease linked to nonsense mutations

Since the advent of Next Generation Sequencing (NGS), the rate of gene discovery in human disease has increased exponentially. This has supported a positive impact for NHS patients in whom diagnoses can now be made, guiding patient care and allowing more accurate genetic counselling. However, even then, for a significant cohort of patients a diagnosis is still not possible. Indeed despite the observation of apparently nonsense or potentially pathogenic splice site variants in a number of cases, NHS laboratories are unable to assign pathogenicity, as they cannot determine whether the variant has functional consequence. To overcome this lack of functional information, and to maximise findings from NGS, there is an absolute need for a novel integrated diagnostic approach. Specifically, combined interrogation of the clinical phenotype, NGS data, integrated bioinformatics (including in silico modelling of variant consequences), and transcriptomic and proteomic analysis will underpin a holistic approach to variant analysis and enhance diagnosis. Furthermore, if the genetic basis of a disorder is determined and its pathogenic impact understood at the molecular level via RNA and protein studies, this knowledge can be utilised to drive the development of personalised medicine.
Extensive NGS data, representing a diverse array of human phenotypes has already been derived at The Manchester Centre for Genomic Medicine, CMFT. This project will initially exploit those existing datasets from a bioinformatic and RNA perspective, specifically targeting a subset with a nonsense mutation variant. NGS data will be analysed using splicing and prediction variant tools and linked to other data such as those from protein-protein interaction databases, pathway and Gene Ontology enrichments, and information on post-translational modifications. For a cohort of selected patients, analysis of nonsense mediated decay, in vitro and cell based splicing assays and RNA sequencing of affected tissues/cells will allow predicted consequences to be assessed functionally at an RNA level. Mass spectrometry will support discovery and targeted proteomic strategies to extend the RNA analysis and to validate and characterise selected proteins from patient samples. Integrated bioinformatics will support mechanistic understanding of the consequences of the variants, both cis and trans acting.
This project operates at the boundaries of medical, computational and biological science. The student will develop unique interdisciplinary skills and work towards the ultimate aim of developing an informatics and 'omics pipeline. This pipeline will enhance the analysis of molecular genetic variants and thus improve clinical diagnosis rates, facilitate disease sub-type stratification and guide precision therapy.