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Advancing structural variation exploitation in aquaculture genomics


College of Medicine and Veterinary Medicine

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Prof Dan Macqueen , Dr J Prendergast No more applications being accepted Competition Funded PhD Project (Students Worldwide)

About the Project

Modern genetic analyses typically use single nucleotide polymorphism (SNP) data, for instance to discover regions in genomes associated with trait variation. However, genomes contain additional classes of abundant genetic variation that are influential to phenotypic variation. Structural variants (SVs) include inversions, duplications, deletions, copy number variations and mobile element insertions, which alter the structure, presence or absence, number, and expression of many genes. Despite representing a major source of genetic and phenotypic variation, SVs are more challenging to accurately type than SNPs. Consequently, they remain comparatively poorly characterized in most species, including farmed fish and shellfish species of global importance for food and economic security. Modern technological and computational advances provide a pressing opportunity to characterize and exploit SVs in aquaculture species, supporting genetics research applied to breeding and conservation.

The objective of this PhD project is to advance the discovery and exploitation of SVs in several key aquaculture species, including salmonids, tilapia and oysters. The project will take advantage of leading-edge methods developed for robust SV detection at the Roslin Institute, which have been successfully applied in fishes (Bertolotti et al. 2020) and livestock. The student will apply such methods using publicly available and unpublished whole genome sequencing datasets to catalogue novel SVs, improve SV detection accuracy, and develop imputation methods to type SVs at reduced cost.
We seek a student with a strong interest in genome biology and a first or minimum upper second-class degree in a relevant topic (e.g. genetics/genomics bioinformatics, computational biology), with preference given to candidates with a master’s degree. The student will receive training in advanced genomics analyses using a range of bioinformatics tools. There will be opportunities to present findings at national and international conferences, and to visit international collaborators for training and networking.

Funding Notes

3.5 year PhD

This opportunity is open to UK and international students and provides funding to cover stipend, tuition fees and consumable/travel costs. Applications including a statement of interest and full CV with names and addresses (including email addresses) of two academic referees, should be emailed to [Email Address Removed].

When applying for the studentship please state clearly the project title/s and the supervisor/s in your covering letter.

Other projects available:
We would encourage applicants to list up to three projects of interest (ranked 1st, 2nd and 3rd choice) from those listed with a closing date of 10th January 2021 at https://www.ed.ac.uk/roslin/work-study/postgraduate/studentships

References

Bertolotti AC, Layer RM, Gundappa MK, Gallagher MD, Pehlivanoglu E, Nome T, Robledo D, Kent MP, Røsæg LL, Holen MM, Mulugeta TD, Ashton TJ, Hindar K, Sægrov H, Florø-Larsen B, Erkinaro J, Primmer CR, Bernatchez L, Martin SAM, Johnston IA, Sandve SR, Lien S, Macqueen DJ. The structural variation landscape in 492 Atlantic salmon genomes. Nat Commun. 2020 Oct 14;11(1):5176. doi: 10.1038/s41467-020-18972-x.
Dutta P, Talenti A, Young R, Jayaraman S, Callaby R, Jadhav SK, Dhanikachalam V, Manikandan M, Biswa BB, Low WY, Williams JL, Cook E, Toye P, Wall E, Djikeng A, Marshall K, Archibald AL, Gokhale S, Kumar S, Hume DA, Prendergast JGD. Whole genome analysis of water buffalo and global cattle breeds highlights convergent signatures of domestication. Nat Commun. 2020 Sep 21;11(1):4739. doi: 10.1038/s41467-020-18550-1. PMID: 32958756
Ho SS, Urban AE, Mills RE. Structural variation in the sequencing era. Nat Rev Genet. 2020 Mar;21(3):171-189. doi: 10.1038/s41576-019-0180-9. Epub 2019 Nov 15. PMID: 31729472; PMCID: PMC7402362.
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