About the Project
The large variation is both a problem and an opportunity. It is a problem because of inadequate adaptability in low-input environments and inadequate productivity in high-input environments. Both extremes are reducing dairy performance and wasting natural resources. Even when the mean level of adaptability and productivity match the environment the segregation of Taurine and Indicine genomes is increasing variation beyond the parent breeds. This additional variation is driven by the segregation of additive genetic effects and the interaction of non-additive genetic effects of the two subspecies genomes as well as genome by environment interactions. This large variation is an opportunity for optimised breeding that would match genetics to the environment and create new improved genome combinations.
This project will comprise three work-packages.
The first work-package will analyse genomic data from population genetic perspective to quantify differences between the two sub-species breeds and the genetic composition of crossbreds. This work will make use of recent advances in population genetics to infer whole population trees with recombination across the two subspecies genomes.
The second work-package will expand the population genetic analyses with quantitative genetic analyses of dairy performance under various Brazilian environments and management practices. This will quantify the magnitude of additive and non-additive genetic variation and genotype by environment interaction as well as map this variation to regions of the two subspecies genomes.
The third work-package will leverage the previous work-packages to build a digital twin (modelling / stochastic simulation) breeding programme of the Girolando population and evaluate various breeding strategies in collaboration with stakeholders. The aim will be to develop a strategy for continuous matching and improvement of adaptability and productivity in Brazilian environments. This will use selective breeding and mating strategies based on phenotypic and genomic data.
Candidates interested in this project must have interest in population and quantitative genomics, breeding, data science, programming and agriculture. Candidates should have a suitable MSc-equivalent degree. There is a possibility for the candidate to attend MSc courses on quantitative genetics or animal breeding during the project.
Successful candidate will be embedded in supervisors’ labs to interact with scientists, post-docs, students, visitors and collaborators. The labs work on the theory and application of data science, genetics and breeding. Interaction with the industry partner and other stakeholders will provide a wide exposure to the application of science in practice.
Candidates can contact the supervisors for further information.
Funding information and application procedures:
This 4 year PhD project is part of a competition funded by EASTBIO BBSRC Doctoral Training Partnership (DTP) http://www.eastscotbiodtp.ac.uk/how-apply-0 .
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 send to [Email Address Removed] by the deadline using the EASTBIO Reference Form.
Please refer to UKRI (https://www.ukri.org/our-work/develop ing-people-and-skills/find-studentships-and-doctoral-training/get-a-studentship-to-fund-your-doctorate/) and Annex B of the UKRI Training Grant Terms and Conditions for full eligibility criteria (https://www.ukri.org/wp-content/uploads/2020/10/UKRI-291020-guidance-to-training-grant-terms-and-conditions.pdf).
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