Royal (Dick) School of Veterinary Studies / The Roslin Institute
Overview: Aquaculture is a growing industry that provides a sustainable source of high-quality protein. This project will focus on finding sustainable breeding strategies utilising genomic information to maximise genetic gain while maintaining genetic diversity in fish farming populations. The project will be largely computer based using data from our breeding company partner and hence would suit a student with an interest in genetics and data analysis. The four year project is a CASE (Collaborative award in Science and Engineering) Studentship with the international livestock breeding company Hendrix Genetics (https://www.hendrix-genetics.com/en/
). This provides the opportunity to work closely with this industry partner and for a placement within the company of three months or more as well as enhanced project support.
Background: Over the last decades world-wide demand for high quality food has continuously increased and hence efficient, sustainable, food production methods are essential. Aquaculture provides a source of high-quality animal protein that is much more resource-efficient in terms of feed conversion, water consumption and carbon footprint than protein from terrestrial farmed animals. Selective breeding is an intrinsically sustainable approach to agricultural improvement. Despite relatively recent domestication of farmed fish species, selection has already been successful at improving economically important traits. Genetic diversity in the breeding populations is still high and that, coupled with the high fecundity of fish, means that there is substantial scope for further improvement.
To enable selective breeding, prediction of breeding values is essential especially for traits that are difficult or expensive to measure on selection candidates, such as disease resistance or fillet quality. Genomic prediction utilises genome-wide SNP markers to improve accuracy of estimated breeding values for candidate breeding fish. While genomic selection has dramatically increased genetic gain in terrestrial livestock breeding programmes, it is at a formative stage in aquaculture breeding. The structure of fish populations and the development and economics of the industry present challenges to its implementation.
Specifically, the structure of breeding populations for the main farmed fish species consists on a number of lines (that varies depending on the generation interval of each species) that are managed as independent populations. Typically, this structure allows for relatively efficient within-line genomic prediction, but poor across-line prediction. In a current CASE studentship nearing completion we have explored how prediction accuracy within and across lines varies when we allow gene flow (“mixing”) between the independent lines. We have found that, under random selection, mixing increases similarity between the lines and genomic prediction accuracy. Under directional selection, a small percentage of mixing increases prediction accuracy and genetic gain but decreases diversity (genetic variance).
Objectives: To achieve our aim, we will investigate by simulation the effect on genomic prediction accuracy, genetic gain and diversity of:
- mixing scenarios relevant to the fish breeding industry, such as use of cryopreserved gametes and introduction of individuals from populations outside the current breeding programme
-the degree of genetic distance between the lines that are mixed
-strategies to control loss of diversity
-strategies that take advantage of both common and rare variation in genomic prediction, by modelling close and distant relationships
Our research will inform on the optimal strategies that will contribute to increase the sustainability of the fish farming industry through increased production efficiency and decreased environmental impact.
This project offers training in generic transferable and professional skills, animal breeding, genetics and genomics, statistical and computational skills. These will prove the foundation for a career in industry or research in the life sciences. Basic training is available through our MSc in Quantitative Genetics and Genome Analysis (http://qgen.bio.ed.ac.uk
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.