NERC GW4+ DTP PhD Studentship: Improving genomewide association study strategies for application on environmental bacteria

This project is one of a number that are in competition for funding from the NERC Great Western Four+ Doctoral Training Partnership (GW4+ DTP). The GW4+ DTP consists of the Great Western Four alliance of the University of Bath, University of Bristol, Cardiff University and the University Exeter plus six Research Organisation partners: British Antarctic Survey, British Geological Survey, Centre for Ecology and Hydrology, the Met Office, the Natural History Museum and Plymouth Marine Laboratory. The partnership aims to provide a broad training in earth and environmental sciences, designed to train tomorrow’s leaders in earth and environmental science. For further details about the programme, please see https://nercgw4plus.ac.uk/ .

Supervisory team -

Main Supervisor: Dr. Lauren A. Cowley, Department of Biology and Biochemistry, University of Bath
Co-Supervisor: Prof. Sam K. Shepperd, Department of Biology and Biochemistry, University of Bath
Co-Supervisor: Prof. Ed Feil, Department of Biology and Biochemistry, University of Bath
Co-Supervisor, Dr. Ruth C. Massey, School of Cellular and Molecular Medicine, University of Bristol

Project background -

Genomewide association studies (GWAS) are a powerful tool in discovering the genetics underlying observed important phenotypes and have been used very successfully in human genetics for several heritable diseases. 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 or genes in bacterial genomes are not acquired independently and it is therefore difficult to differentiate the mutation or gene that is causing the phenotype change from those that are simply hitch hiking with it (linkage disequilibrium).

Methods developed to achieve bacterial GWAS results of high confidence could be extremely useful in determining the genetic elements associated with phenotypes of environmental importance, such as toxin production, environment survivability, antibiotic resistance or host niche. Previous studies by co-supervisors Professor Sam Sheppard and Dr Ruth Massey (Sheppard et al., 2013, Recker et al., 2017) have shown the capabilities of GWAS in bacteria.

Project aims and methods -

NERC funds world-leading basic, strategic and applied research. This project comes under the Living World NERC research theme and more specifically under the genomes and evolution section within that. This studentship will use state of the art genomics and cutting edge computational tools to wrangle big data to understand phenotypes of environmental bacteria.

The aims of the studentship will be:
• To develop a novel approach of longitudinal and spatial sampling in big data that offers an opportunity to resolve genuine associations from spurious one’s due to linkage.
• This will be tested by using datasets that are deeply sampled longitudinally and testing time periods individually to see if association gene lists change over time and if certain genes remain constant in their assigned significance. In recombining bacteria, sampling over time causes linkage between loci to decline (Arnold and Hanage 2017).
• Simulations will be run using the state of the art computational simulation tool Fwdpp (Thornton 2017) to gauge the optimum time period needed to allow enough recombination to happen to decrease spurious linkage enough to make GWAS accurate. The student will aim to estimate the minimum number of required genomes and frequency of sampling when given the recombination rate of the species.
• Systematic testing will be performed in a variety of large genomic datasets to develop a robust protocol to provide high confidence results.

To limit the complexity of linkage disequilibrium, highly recombinogenic environmental bacteria such as Legionella pneumophila datasets will be used in initial strategies.

Candidate -

Essential:
• A high scoring degree in microbiology, biology or computer science (2.1 or higher)

Preferred:
• A master’s degree in Bioinformatics or computer science
• Experience of at least one computer programming language

Anticipated start date: 30 September 2019

Candidates should apply using University of Bath’s online application form https://www.bath.ac.uk/study/pg/applications.pl#bio-sci

You may apply for more than one project if you wish but you should submit a separate personal statement relevant to each one.

Further information on the Department of Biology & Biochemistry may be found here http://www.bath.ac.uk/departments/department-of-biology-biochemistry/