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EASTBIO Efficient and Sustainable Genomic Breeding in Turkeys

Project Description

Royal (Dick) School of Veterinary Studies / The Roslin Institute

This PhD project will design an efficient and sustainable genomic breeding programme in turkeys in a unique collaboration with a global industry partner Aviagen. Modern breeding programmes improve a range of production, reproduction, health and welfare traits and at the same time balance the selection pressure against maintaining genetic variability. This balance is very important to safeguard the long-term sustainability of any breeding programme, including the ability to respond to changes in environment, technology or mutations.
Recent advent of high-throughput genomic data enables improvements upon the state of the art. This project will develop new ways of using the genomic data to increase the efficiency and sustainability in turkey breeding. It will specifically combine recent advances in population and quantitative genomics and mathematical optimisation and combine it with data from a real turkey breeding programme to design improved programme.

The project will be organised in three work-packages.

In the first work-package the real data will be analysed with the recent population and quantitative genomic methods to assess the magnitude of genetic (co-)variability and its turnover between generations. This will involve analyses of trait-agnostic and trait-specific genetic (co-)variability, including standing and mutational variance as well as additive and dominance components and inbreeding depression.

In the second work-package the results from the first work-package will be used to build an in-silico breeding framework to create a modelling environment for comparing alternative breeding strategies that aim to maximise the efficiency of converting genetic variation into genetic gain. Some of the strategies will involve a mathematical optimisation.

In the third work-package the results from the two work-packages will be combined to develop a case for support for a possible implementation in a real breeding programme.

The successful candidate will be embedded in the research labs of the principal supervisor (HighlanderLab) and the second supervisor. This will involve interaction with core scientists, post-docs, other students and visitors. The research labs work on the theory and practical application of genomics, data science and breeding for management and improvement of a diverse set of populations, including animals, plants and insects. The candidate will also have opportunity for interaction with research collaborators in genetics and breeding as well as statistics and optimisation. Importantly, the industry partner is located in the same region which will facilitate active collaboration and seamless three-month placement. The successful candidate will therefore be exposed to a range of science and its application in practice. Finally, the candidate will be exposed to time and project management with further development options.

The successful candidate will have interest in population and quantitative genomics, breeding, data science with some scripting/programming to improve agricultural populations. Preference will be given to candidates with a suitable MSc-equivalent degree. There is a possibility for the candidate to attend specific MSc courses on quantitative genetics or animal breeding as part of the PhD project.

Interested candidates should contact the supervisors for further information.

All candidates should have or expect to have a minimum of an appropriate upper 2nd class degree. To qualify for full funding students must be UK or EU citizens who have been resident in the UK for 3 years prior to commencement.

Funding Notes

Download application and reference forms via: View Website

Completed application form along with your supporting documents should be sent to our PGR student team at

Please send the reference request form to two referees. Completed forms for University of Edinburgh, Royal (Dick) School of Veterinary Studies and the Roslin Institute project should be returned to by the closing date: 5th January 2020.

It is your responsibility to ensure that references are provided by the specified deadline.


G Gorjanc, R C Gaynor, and J M Hickey. Optimal cross selection for long-term genetic gain in two-part programs with rapid recurrent genomic selection. Theoretical and Applied Genetics, 131(9):1953–1966, 2018.
J Jenko, M C McClure, D Matthews, J McClure, M Johnsson, G Gorjanc, and J M Hickey. Analysis of a large dataset reveals haplotypes carrying putatively recessive lethal and semi-lethal alleles with pleiotropic effects on economically important traits in beef cattle. Genetics Selection Evolution, 51(9), 2019.
G Gorjanc and J M Hickey. AlphaMate: a program for optimising selection, mainte- nance of diversity, and mate allocation in breeding programs. Bioinformatics, 34(19):3408– 3411, 2018.

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