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
This project is part of the Discovery Medicine North Doctoral Training Partnership (DiMeN DTP), a diverse community of PhD students across the North of England researching the major health problems facing the world today. Our partner institutions (Universities of Leeds, Liverpool, Newcastle and Sheffield) are internationally recognised as centres of research excellence and can offer you access to state-of the-art facilities to deliver high impact research.
We are very proud of our student-centred ethos and committed to supporting you throughout your PhD. As part of the DTP, we offer bespoke training in key skills sought after in early career researchers, as well as opportunities to broaden your career horizons in a range of non-academic sectors.
Being funded by the MRC means you can access additional funding for research placements, international training opportunities or internships in science policy, science communication and beyond. See how our current DiMeN students have benefited from this funding here: http://www.dimen.org.uk/overview/student-profiles/flexible-supplement-awards
Further information on the programme and how to apply can be found on our website:
Studentships commence: 1st October 2021
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.
Why not add a message here
Based on your current searches we recommend the following search filters.
Based on your current search criteria we thought you might be interested in these.
MRC DiMeN Doctoral Training Partnership: Using artificial intelligence to optimise treatment decisions by analysis of retinal images for patients with blinding diabetic eye disease