Congenital heart disease (CHD) is the most common developmental defect amongst live births; its recorded birth prevalence globally is increasing. Whilst environmental factors have been shown to modulate CHD risk, the high observed recurrence rate suggests genetic factors dominate. Despite this, it is thought that only 20% of cases can be attributed to a known genetic cause. As such, improvements in our understanding of the genetic architecture of this disease are urgently needed to inform care decisions in the clinic.
Our group has previously employed supervised machine learning techniques to probe the genetics of cardiac development in the mouse. We trained a model using over 100 derived features on >10,000 genes annotated as either involved or not involved in cardiac development. This model was used to identify putative cardiac development genes across the remainder of the mouse genome. The proposed project would seek to validate these putative cardiac development genes by examining whether human CHD cases from the 100,000 Genomes Project exhibit excess deleterious variation in the homologues of these genes. We will perform burden testing on the 100,000 Genomes dataset for our predicted cardiac and non-cardiac development genes.
Human genomic data is highly sensitive. Research on these data currently relies on strict privacy controls which can often impede collaboration and prevent collation of large aggregate datasets on which modern genomics techniques thrive. Worse, there is evidence that the current paradigm is insufficient from a privacy viewpoint, with sample re-identification possible even when data has been anonymised. Privacy-preserving frameworks which allow analyses to proceed without access to the unencrypted data could simultaneously reduce barriers to large-scale genomics research and offer improved privacy for study participants. The prospective student would use the validation study of cardiac developmental genes as a model example around which to develop private approaches to genetic analyses, which could then be applied to similar analyses in other research domains.
Once the privacy preserving technologies have been developed, we will implement these technologies to interrogate additional congenital heart defect patient datasets from international studies to determine if our cardiac development gene predictions are applicable to populations outside of the UK.
Eligibility
Applicants must have obtained or be about to obtain a First or Upper Second class UK honours degree, or the equivalent qualifications gained outside the UK, in an appropriate area of science, engineering or technology.
Before you Apply
Applicants must make direct contact with preferred University of Manchester supervisors before applying. It is your responsibility to make arrangements to meet with potential supervisors, prior to submitting a formal online application.
How To Apply
To be considered for this project you MUST submit a formal online application form - full details on eligibility how to apply can be found on our website https://www.bmh.manchester.ac.uk/study/research/astar/#apply On the online application form select A*STAR PhD Programme.
Your application form must be accompanied by a number of supporting documents by the advertised deadlines. Without all the required documents submitted at the time of application, your application will not be processed and we cannot accept responsibility for late or missed deadlines. Incomplete applications will not be considered. If you have any queries regarding making an application please contact our admissions team [Email Address Removed]
Equality, Diversity and Inclusion
Equality, diversity and inclusion is fundamental to the success of The University of Manchester, and is at the heart of all of our activities. The full Equality, diversity and inclusion statement can be found on the website https://www.bmh.manchester.ac.uk/study/research/apply/equality-diversity-inclusion/
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