The Dogslife Project (www.dogslife.ac.uk) is the first large-scale population-based epidemiological study documenting the incidence and prevalence of diseases in dogs over their life course, and the environmental influences that affect them (1). To date there are 8,500 dogs registered with the project, with data collected on over 45,000 illness episodes, and with dogs followed for over a decade of their life. A multitude of illness phenotypes have been reported in the cohort (2), and the project has identified environmental and genetic risks which are associated with canine phenotypes (3).
This interdisciplinary PhD project will evaluate the rich and diverse dataset collated by Dogslife to provide a doctoral training in quantitative epidemiology and genomics. During the 4-year studentship the successful candidate will evaluate existing and prospectively collected environmental, host genetic and microbial datasets for aging phenotypes, and analyse the interactions between them. In particular, a focus will be made to identify the early-life events which are associated with the development of high morbidity diseases and cumulative disease burdens later in life. Multiple risk variables such as duration and severity of the clinical signs, co-morbidities and preventative healthcare status will be modelled. The results will quantify lifestyle, environmental and genetic risks for common diseases.
A range of exploratory and analytical statistical/epidemiological techniques will be taught to the student, to enable the identification of single and composite risk factors for specific disease syndromes, and overall disease burden. During the studentship the candidate will learn and develop a diverse range of skills, including Linux, cluster computing, R programming, command line tools, SQL database analysis, survey and experimental design, and the collation and analysis of Google Trends and Analytics datasets. Analysis will include the use of multivariate regression models, machine learning, data simulations, Bayesian techniques, and complex data visualisation and mapping. The student can also engage in wet-laboratory work, leading to genome wide SNP chip and whole genome sequencing data acquisition, and subsequent analysis. Furthermore the project affords a successful candidate opportunities to present their work to a variety of different audiences, including the general public. This studentship provides the candidate with an opportunity to develop a bespoke, multidisciplinary research profile with impactful results, on a project that captures the public imagination.
Application Process:
EASTBIO Application and Reference Forms can be downloaded via http://www.eastscotbiodtp.ac.uk/how-apply-0
Please send your completed EASTBIO Application Form along with a copy of your academic transcripts to [Email Address Removed]
You should also ensure that two references have been sent to [Email Address Removed] by the deadline using the EASTBIO Reference Form.
Please refer to Get a studentship to fund your doctorate – UKRI and Annex B of the UKRI-291020-guidance-to-training-grant-terms-and-conditions.pdf for full eligibility criteria.
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