Accelerating the Diagnosis and Treatment of Genetic Conditions by Empirically Interrogating Splice Variants at Scale

About the project

About the Project PhD only Summary Whole-genome sequencing currently diagnoses approximately half of all children whom we clinically suspect of having a rare monogenic condition. Non-coding variants such as deep intronic splice altering variants are likely to contribute many of the undiagnosed cases. Predicting the functional consequence of non-coding variation, particularly deep intronic splice sites is challenging. Recent work suggests that genomic context, rather than splice site strength, determines 96% of the variance in splice site usage. This means that it is currently very challenging to predict the majority of variants which alter splicing. We will use scalable mutagenesis approaches – such as prime editing and saturation genome editing to understand the relationship between genomic context, cis-regulatory elements, splice site strength and splicing. This has immediate translational utility for both diagnostic and therapeutic applications and generates calibration-scale datasets for machine-learning approaches. Project Aims We will perform cellular screens to systematically insert poison exons into the endogenous genomic context to understand: 1. What are the distal cis-regulatory elements that determine splice site usage? 2. What are the proximal cis-regulatory elements that affect splice site usage? 3. Can we use this information combined with machine learning tools to predict splice-modulating deep intronic variants? 4. Can we improve the diagnosis of children with rare neurodevelopmental conditions by applying these tools to clinical whole-genome sequencing datasets? 5. Can we use this information to better design therapeutic poison exons (for the downregulation of gene dosage). 6. Can we use this information to improve the design of therapeutic Antisense Oligonucleotides (ASOs)? How to Apply; If you are interested in this project, please go to the University pages and apply via the online portal; PhD https://www.postgraduate.study.cam.ac.uk/courses/directory/cvcnpdpcn/apply

Research areas