SCYPHeR: Rare Disease and Health Inequalities Theme: Improving diagnosis and treatment options for children in South Yorkshire with rare, undiagnosed diseases by mining genomics data and correlating with functional analyses.

Rare disorders affect around 1 in 17 people. These are mostly genetic in origin, predominantly affect children and are often extremely severe and life limiting. Accurate molecular diagnoses enable the best clinical management and may lead to tailored treatments, improving outcomes for affected families. Improvements in sequencing technologies have led to vast progress in rare disease diagnostics in recent years, but even with whole genomes sequenced, over half of patients do not get a molecular diagnosis, limiting treatment, care, and support. Many of these missed diagnoses stem from genes which have not yet been linked to disorders, or variants which fall in non-coding, regulatory regions of the genome.

This project will utilise whole genome sequence data from over 57,000 individuals from the 100,000 Genomes Project (https://www.genomicsengland.co.uk/initiatives/100000-genomes-project) and the NHS Genomic Medicine Service (https://www.genomicsengland.co.uk/genomic-medicine/nhs-gms), including over 7,000 undiagnosed individuals from the Yorkshire region. You will identify candidate diagnostic variants in a set of potentially disease associated genes and non-coding loci and use a suite of bioinformatic approaches, rare disease databases and functional genomics data sources (including ChIP-seq, proteomics and RNA-Seq data) to prioritise the most likely causal variants. This will enable the identification of novel disease genes and loci. It will also provide diagnoses for patients with rare disease, having an immediate and lasting impact on affected families.

This is an interdisciplinary project, spanning multiple academic disciplines and healthcare. It will equip the candidate with experience in bioinformatics, computational genomics, RNA-seq, multi-omics, variant interpretation, and clinical genetics. These are excellent translational skills in high demand in academia, medicine and industry. The student will benefit from a supportive and dedicated supervisory team, extensive local expertise and a tailored training and development plan to help them succeed.

Entry Requirements

Candidates must have at least first or upper second class honours degree. An interest in bioinformatics and genetics is crucial. Experience of using the command line or coding (e.g. python, R) would be an advantage but is not a prerequisite.

How to Apply

Please complete a University Postgraduate Research Application form available here: https://www.sheffield.ac.uk/postgraduate/phd/apply/applying. 

Please clearly state the prospective supervisors in the respective box and select ‘School of Medicine & Population Health’ as the department.

Project start date – October 2025