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MRC DiMeN Doctoral Training Partnership: Metadata Integration in Biological Networks to Identify New Therapeutic Targets in Giant Cell Arteritis


Project Description

This PhD’s primary focus is to use genotypic data to elucidate the pathogenesis of giant cell arteritis (GCA) and related clinical phenotypes to identify new treatment targets. GCA is the commonest primary systemic vasculitis, occurring exclusively after 50yrs. Most patients commence glucocorticoid monotherapy. GCA has a high relapse rate with 50% remaining glucocorticoid dependent 2-3 years later. There is a significant unmet clinical need to identify new therapeutic targets in GCA.

The PhD will explore the use of novel mathematical approaches to target validation in GCA. Genotypic data will be enriched with additional multi-omic data from disease cohorts and public databases. Many Network clustering algorithms can automatically infer clusters of similar nodes. A range of validation measures is used to improve the understanding of networks’ structure. However additional information about each node, i.e. metadata, is often available but unexplored. Examples:
• In gene networks, proteins or cellular functions are expected to correlate to the gene clusters.
• In patients’ networks, we expect specific treatment, illness or age to correlate to clusters of people.

Main objectives:
• Enable fast integration of partial information on biological samples and class prediction.
• Provide an automatic model to identify the mismatches between metadata, predicted edges and ground truth.
• Combine genetic, plasma protein, metabolomic and RNASeq data to identify clinical subtypes and examine associations with outcome in GCA and PMR cohorts.

The automatic embedding of metadata alongside class label inference will be investigated using a hierarchical Bayesian modeling approach. Interdependent signals from the nodes, when available, will be used to infer the missing relationships. 
This will give insight into GCA pathogenesis, other diseases, and related clinical phenotypes to identify new treatment targets.
The output of this research will have high impact in the field of biomedical science, where there is urgent need for fast integration of partial information on biological samples. It will also address the well-recognised methodological gap of combining multi’omics datasets with high levels of missing data. Nevertheless, this PhD will address important skills gaps in biostatistics and mathematics and will be used to attract mathematicians into working with biological and clinical data.
Large well-phenotyped GCA cohorts with genome-wide genotypic data and associated proteomic and RNASeq will be used, combined with UK Biobank and publicly accessible data.

This project will require heavy computational resources and will be focused on the methodological development.

Training. You will join existing MRC and EU-funded international programmes of research.

Dr Luisa Cutillo is a lecturer at the School of Mathematics, expert in statistics, networks and data analysis; she runs an MSc module in genetic epidemiology at the University of Leeds, which is frequently taken as a training module by PhD students. She is also an active member of the Local Leeds Royal Statistical Society group. https://eps.leeds.ac.uk/maths/staff/5526/dr-luisa-cutillo

Professor Ann Morgan is a practicing clinician and the PI of the MRC TARGET (Treatment According to Response in Giant cEll arteritis) Partnership. https://medicinehealth.leeds.ac.uk/medicine/staff/2614/professor-ann-morgan. This TARGET research network holds regular project meetings and an annual scientific meeting where you would be invited to present. https://lida.leeds.ac.uk/target

Benefits of being in the DiMeN DTP:
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 can be found on our website:
http://www.dimen.org.uk/

Funding Notes

To qualify, you must be a UK or EU citizen who has been resident in the UK/EU for 3 years prior to commencement. Applicants must have obtained, or be about to obtain, at least a 2.1 honours degree (or equivalent) in a relevant subject. All applications are scored blindly based on merit. Please read additional guidance here: View Website

Good luck!

References

1-Carissimo A, Cutillo L, Feis ID. 2018. Validation of community robustness. Computational Statistics & Data Analysis. 120, pp. 1-24

2-Peel, Larremore, Clauset, The ground truth about metadata and community detection in networks Science Advances 2017

3-Carmona F,….. Barrett, J.H. Morgan AW*, Martin J*. A large-scale genetic analysis reveals a study contribution of the HLA class II region to GCA susceptibility. Am J Hum Genet 2015;96:565.

How good is research at University of Leeds in Mathematical Sciences?

FTE Category A staff submitted: 53.00

Research output data provided by the Research Excellence Framework (REF)

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