Qualification Type: | PhD |
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Location: | Exeter |
Funding for: | UK Students |
Funding amount: | From £19,237 |
Hours: | Full Time |
Placed On: | 23rd August 2024 |
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Closes: | 17th September 2024 |
Reference: | 5216 |
Project description:
Sleep has been linked to mental health outcomes, and other diseases including dementia, diabetes and heart disease. Working with international collaborators, we recently identified over 350 changes in our DNA that play an important role in influencing sleep. However, these studies were limited by the scope of collected sleep data and the resolution of genetic data analysed. Furthermore, human genetic studies have not incorporated the activity of implicated genes in the suprachiasmatic nucleus (SCN) – a key brain region involved in regulating 24-hour physiological cycles (circadian rhythms), which are critical for determining sleep patterns.
Using available health data on 1.5 million individuals, this project aims to find new links between genetics and sleep and relate these to risk for mental health outcomes. The PhD project will involve combining large-scale computational analysis of 1) GP records, hospital data, sleep questionnaires, and wrist-worn activity monitors to derive estimates of sleep and identify individuals with sleep and mental health conditions, 2) >2 billion genetic variants captured from whole-genome sequencing to identify new genetic links with sleep and mental health, and 3) data from single-cell expression analyses to characterise the regulation of genes in individual cells in the SCN over a 24-hour period.
The student will be integrated into two interdisciplinary active research groups within the Department of Clinical and Biomedical Science in the Faculty of Health and Life Sciences. The student will attend regular lab meetings and journal clubs, covering bioinformatics, data science, and genomics. The student will present at seminars to gain experience in formal presentations. Training in genomics, quantitative skills (statistics, data science, and bioinformatics) and “big-data” handling will be provided by the supervisory team. Additional development opportunities will be provided through attendance and presentation of results at national and international conferences, and internal and external training courses.
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