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EASTBIO Design of breeding programs to improve honeybee health and production

Project Description

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

This PhD project will design breeding programs to improve honeybee health and production through a unique collaboration between researchers, practitioners, beekeepers and bee-farmers.

Honeybees are under pressure due to pests and environmental changes. There are competing views on what type of a honeybee are best suited different environmental conditions and management practices. These views are largely based on bee morphology and productivity traits, with limited quantitative evidence and analysis.

The issue can be addressed via a three-pronged approach of
(i) collecting quantitative evidence for different types of bees, conditions and practices, (ii) associating phenotypic variation to said evidence and (iii) designing modern, efficient and sustainable data-driven breeding programs.

To this end the project will be organised in three work-packages.

First, quantitative evidence will be collected from a sample of colonies in the UK and/or New Zealand. The focal phenotype traits will be health status and production along with the state-of-the-art high-frequency and high-volume Internet-of-things (IoT) colony sensors and meta-data (management, location, and weather). In addition, colonies will be whole-genome sequenced.

Second, the collected data will be analysed. Specifically, regressing phenotypes on bee morphology, whole-genome sequence variants, management, location and weather descriptors. The genome and location will be modelled by accounting phylogenetic and spatial relationships to disentangle genetic and environmental variation. In addition, associations with IoT colony data will be evaluated with machine learning to build predictive models for health status. These results will provide quantitative evidence about differences due to genetic, management and environmental effects.

Third, the results from the first two work-packages will be used to build an in-silico honeybee breeding framework. This framework will be used to engineer most suitable breeding programs for different conditions and practices. Particular attention will be given to spatial distribution and therefore unavoidable hybridization between different bee types. Further, emphasis will be given to design sustainable breeding programs (in terms of genetic diversity) that can address current and future challenges. In summary the outcome will be a set of practical breeding schemes that will improve health and production under different conditions and practices.

The successful candidate will be embedded in the research labs of the principal supervisor (HighlanderLab). 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 successful candidate will 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 and machine learning with some scripting/programming to improve honeybee 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.

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

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.
Download application and reference forms via:
View Website


T Regan, M W Barnett, … and T C Freeman Characterisation of the British honeybee metagenome. Nature Communications, 9: 4995, 2018.
M L Selle, I Steinsland, J M Hickey, and G Gorjanc. Modelling spatial variation in agricultural field trials with INLA. Theoretical and Applied Genetics, accepted, 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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