Optimising Genomic Breeding of Farmed Salmon

Funding: BBSRC KTN Industry CASE Studentship

This project provides the opportunity for a PhD student to develop cutting edge genomic technology for the aquaculture industry. Aquaculture is the fastest growing agricultural sector world-wide, with salmon being its most important farmed species in the UK.

The relatively recent domestication of salmon produces both challenges and opportunities for the use of breeding to improve both production and welfare. The recent development of a single-nucleotide polymorphism (SNP) genotyping array for salmon facilitates the use of genomic selection to increase rates of genetic improvement. Genomic selection utilises computational analysis to combine genome-wide SNP data with trait information to identify and select fish that are carrying the best of the naturally occurring genetic variation for characteristics such as growth rate, product quality and disease resistance.

The project is a collaboration with the salmon breeding company Landcatch Natural Selection (LNS), which is part of the Hendrix Genetics breeding company. LNS has used genomic breeding approaches for several years and was the first company to identify (in collaboration with Roslin Institute and others) and utilise a gene associated with increased resistance to disease in its breeding programme. The company will provide data from several different populations of salmon that have been recorded for traits including parasite resistance and have been typed using a genome-wide SNP array.

The objective of the project is to explore the efficiency of genomic selection for parasite resistance and production traits within and between populations using real data and data simulated according to alternative genetic models. The main task will be to optimise the accuracy of selection by adjusting the emphasis put on within and between population genomic information and to suggest additional information that might be collected to further enhance accuracy. There will be the opportunity to work with company geneticists to implement approaches developed in their populations.

The project would suit a student with excellent numerical or computational skills (or who has the aptitude to develop such skills) who is interested in working in the biosciences area.

This PhD project is supported by an Industrial KTN BBSRC CASE award studentship (for more information on these awards see http://www.bbsrc.ac.uk/skills/investing-doctoral-training/case-partnerships/). As such the studentship benefits from an enhanced stipend and the opportunity to work with both Academic and Industry supervisors and to gain training and experience by working within LNS for at least three months during the project. The training and experience will provide the student with an excellent foundation for a future career in academia, the breeding industry or in many other areas of the booming biotech industry.

The project would suit an enthusiastic and innovative student who has or expects to get an excellent degree qualification at BSc or MSc level and who is enthusiastic about applying computational or statistical analyses in the biosciences. This would include students with training in bioinformatics or quantitative or statistical genetics or related disciplines. The project may also be well suited to students with numerical training in areas such as statistics, mathematics, actuarial science, informatics, etc., and such students are encouraged to apply. Additional training in all relevant areas of biology and analysis is available if required, and the student will also receive training in both project specific and widely transferrable skills.

All candidates should have or expect to have a minimum of an appropriate upper 2nd class degree or equivalent. 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.