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Joint China Scholarship Council: Using imaging genomics to unlock the door to better treatments for osteoarthritis


Department of Oncology and Metabolism

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Prof M Wilkinson , Prof A Frangi No more applications being accepted Competition Funded PhD Project (Students Worldwide)
Sheffield United Kingdom Artificial Intelligence Bioinformatics Data Analysis Epidemiology Genetics Machine Learning Statistics

About the Project

What is the problem?

Three hundred million people suffer pain and disability from osteoarthritis (OA) worldwide, a disease for which there is currently no disease-modifying treatment. Joint shape is a critical determinant of osteoarthritis risk, accounting for ~80% of idiopathic hip OA susceptibility. The heritable component of hip OA accounts for ~60% of the susceptible risk, although the underlying mechanisms are poorly understood. Studies linking 2-dimensional (2D) imaging with genome-wide variant information have demonstrated a genetic component to joint shape. However, to date, they have yielded only limited insights into joint shape heritability and OA risk, in part because of the intrinsic limitations of 2D imaging of a complex 3D structure and the small cohort sizes studied

How can we solve it?

Computational imaging exploits image sequence data to provide high-dimensional phenotypic information. When combined with genome-wide variation data, this “imaging genomics” approach promises to transform our understanding of the structural biology of health and disease. Wilkinson, Frangi and Zeggini have been at the forefront of exploring the imaging and genetics of bone and arthritis using the UK Biobank dataset (PMID: 31647421, 30664745, 32457287). Here, we will combine these strengths and our practical experience with the dataset to develop the first 3D imaging genomics map of the human hip and osteoarthritis. 

Using our established research imaging platform MULTI-X (www.multi-x.org) for data analytics and our UK Biobank OA genetics dataset (study #9979) the student will combine 3D MRI hip morphology and genotype data  architecture/variant relationships in up to 100,000 UK Biobank participants. The student will integrate these multiscale/multisource datasets using machine/deep learning, maximum-likelihood and Mendelian randomisation models to construct causal quantitative trait analysis relationships between the genetic, clinical and imaging-derived phenotypes. The work is entirely computational using existing data allowing COVID-independent remote working using established cloud-computing servers.

What training will you receive?

We feel passionately about the opportunities that Imaging Genomics will bring to our field of human ageing and want to transfer our enthusiasm to a student who also sees the development opportunities in this area. As well as specific training in machine learning and statistical genetics, you will be immersed in the biology of a common, complex disease. You will gain expertise in all these fields through the inter-institutional nature of this project. You will have the opportunity to participate in the seminar and lecture series available at Sheffield and Leeds to maximize the learning resource available. This will including a period of complex trait genomics training at the Helmholtz Institute in Munich, as well as that received at the home institutions.

See these links for more details of the supervisors and their work:

Prof Mark Wilkinson, Faculty of Medicine & Health, University of Sheffield,   https://mellanbycentre.org/mark-wilkinson/

Prof Alejandro Frangi, Faculty of Engineering & Physical Sciences, University of Leeds, http://www.cistib.org/afrangi/

Prof Ele Zeggini, Helmholtz Munich, https://www.helmholtz-muenchen.de/itg/index.html

ENTRY REQUIREMENTS

Candidates must have a first or upper second class honors degree or significant research experience.

HOW TO APPLY

Prospective candidates will need to apply for postgraduate research here: https://www.sheffield.ac.uk/postgraduate/phd/apply/applying where they will also be asked to fill in a google form specifically for the Joint China Scholarship Council scheme.

Please clearly state the prospective main supervisor in the respective box and select Neuroscience as the department. 

You must be a national of, and reside in, mainland China (not including Hong Kong or Macau). You must also be intending to return to China once your programme is completed. You will require an unconditional offer from the University of Sheffield including meeting both academic and English language criteria.

Applications close at 5pm on Friday 26th February 2021.


Funding Notes

This scheme provides academically excellent Chinese students with the opportunity to study for a PhD at the University of Sheffield. The scholarships are supported jointly by the China Scholarship Council and the University of Sheffield.
The University of Sheffield will fund tuition fees for 3.5 years. Students are expected to submit their thesis within this period. CSC will fund a grant for living costs for up to 4 years, and one return flight from China to the UK.

References

PMID 32393986. Wilkinson JM, Zeggini E. The Genetic epidemiology of joint shape and the development of osteoarthritis. Calcif Tissue Int. 2020 May 11. doi: 10.1007/s00223-020-00702-6.
PMID 32457287. Littlejohns TJ, Holliday J, Gibson LM, Garratt S, Oesingmann N, Alfaro-Almagro F, Bell JD, Boultwood C, Collins R, Conroy MC, Crabtree N, Doherty N, Frangi AF, Harvey NC, Leeson P, MillerKL, Neubauer S, Petersen SE, Sellors J, Sheard S, Smith SM, Sudlow CLM, Matthews PM, Allen NE. The UK Biobank imaging enhancement of 100,000 participants: rationale, data collection, management and future directions. Nat Commun. 2020 May 26;11(1):2624. doi: 10.1038/s41467-020-15948-9.

PMID: 30664745. Tachmazidou I, Hatzikotoulas K, Southam L, Esparza-Gordillo J, Haberland V, Zheng J, Johnson T, Koprulu M, Zengini E, Steinberg J, Wilkinson JM, Bhatnagar S, Hoffman JD, Buchan N, Süveges D, arcOGEN Consortium, Yerges-Armstrong L, Davey Smith G, Gaunt TR, Scott RA, McCarthy LC, Zeggini E. Identification of new therapeutic targets for osteoarthritis through genome-wide analyses of UK Biobank data. Nat Genet. 2019 Feb;51(2):230-236. doi: 10.1038/s41588-018-0327-1. Epub 2019 Jan 21.
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