About the Project
The University of Bath is inviting applications for this PhD opportunity based at the Milner Centre for Evolution, a unique, cross-faculty research centre bridging biology, health and education. The Centre is dedicated to a broad range of fundamental research questions relating to evolutionary biology; from in deep time, to the micro-evolutionary dynamics of a disease outbreak. We have a strong focus on public engagement and outreach. We are located in a dedicated multi-million-pound building that opened on the University campus in September 2018. For further information about the centre see https://www.bath.ac.uk/research-centres/milner-centre-for-evolution/.
Project Overview:
Genome-wide association studies (GWAS) are a powerful tool in discovering the genetics underlying observed phenotypes. In bacteria that reproduce asexually, the challenge with GWAS is the high linkage between polymorphisms due to low levels of recombination. A lot of the mutations in bacterial genomes are not acquired independently and it is difficult to differentiate the mutation that is causing the phenotype change from those that are hitch hiking with it. This project aims to develop a novel multi-year longitudinal sampling method to harness recombination in shuffling those gene linkages periodically. That shuffling will allow more accurate recognition of the causal genes in phenotypes by decoupling them. Methods developed to achieve bacterial GWAS results of high confidence could be extremely useful in determining the genetic elements associated with phenotypes of public health importance, such as disease severity or antibiotic resistance. We will also develop machine learning algorithms trained on associated data to provide a prediction mechanism for important infectious disease phenotypes.
Whole genome sequencing analysis projects have been transformative to the study of microbial genomics. This project will provide the student with extremely useful and valuable bioinformatics skills in an area of evolutionary microbiology that is expanding. We will also use machine learning to develop data science skills.
Candidate:
Applicants should hold, or expect to receive, a First Class or high Upper Second Class UK Honours degree (or the equivalent qualification gained outside the UK) in a relevant subject. A master’s level qualification would also be advantageous.
Applications:
Informal enquiries should be directed to Lauren Cowley, [Email Address Removed].
Formal applications should be made via the University of Bath’s online application form:
https://samis.bath.ac.uk/urd/sits.urd/run/siw_ipp_lgn.login?process=siw_ipp_app&code1=RDUBB-FP02&code2=0013
On the application form, please ensure that you quote ‘Evolution Education Trust’ in the Finance section and the supervisor’s name and project title in the ‘Your research interests’ section. Should you wish to be considered for more than project, quote the projects in order of preference and upload a separate personal statement relevant to each one.
More information about applying for a PhD at Bath may be found here:
http://www.bath.ac.uk/guides/how-to-apply-for-doctoral-study/
Interviews will take place in Bath on 14 June 2019.
Anticipated start date: 30 September 2019.
References
Sheppard, S.K., Didelot, X., Meric, G., Torralbo, A., Jolley, K.A., Kelly, D.J., Bentley, S.D., Maiden, M.C., Parkhill, J. and Falush, D., 2013. Genome-wide association study identifies vitamin B5 biosynthesis as a host specificity factor in Campylobacter. Proceedings of the national academy of sciences, 110(29), pp.11923-11927.
Recker, M., Laabei, M., Toleman, M.S., Reuter, S., Saunderson, R.B., Blane, B., Török, M.E., Ouadi, K., Stevens, E., Yokoyama, M. and Steventon, J., 2017. Clonal differences in Staphylococcus aureus bacteraemia-associated mortality. Nature microbiology, 2(10), p.1381.
Arnold, B.J. and Hanage, W.P., 2017. Longitudinal samples of bacterial genomes potentially bias evolutionary analyses. bioRxiv, p.103465.
Thornton, K.R., 2014. A C++ template library for efficient forward-time population genetic simulation of large populations. Genetics, 198(1), pp.157-166.
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