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Using multi-omics to understand autoimmune diseases

  • Full or part time
  • Application Deadline
    Friday, November 08, 2019
  • Funded PhD Project (European/UK Students Only)
    Funded PhD Project (European/UK Students Only)

Project Description

Project description:
We invite applications for a Department of Immunology and Inflammation-funded PhD studentship in the research group of Dr James Peters in the Centre for Inflammatory Disease. We are seeking a dynamic individual with an interest in applying quantitative approaches to biomedical data to join a team of researchers working on autoimmune diseases.

The project will focus on integrating high-dimensional genomic, transcriptomic and proteomic data to better understand autoimmune diseases, particularly systemic lupus erythematous (SLE, or ‘lupus’) and giant cell arteritis (GCA). These diseases exhibit considerable heterogeneity in their clinical manifestations, response to drug treatments and long-term outcomes.

The aims of the project are (i) understanding the molecular basis of this clinical heterogeneity (e.g. with approaches such as clustering); (ii) using –omics to identify biomarkers that predict clinical outcomes; and (iii) identifying potential new drug targets.

The project will also involve collaborating with colleagues at other institutes including the Wellcome Trust Sanger Institute and the University of Leeds. The project will provide training in computational biology, programming, applied statistics, genomics, transcriptomics and proteomics. Additional training will be provided where necessary. Dr Peters is part of the Health Data Research UK (HDR UK) London network.

An undergraduate degree in a quantitative discipline would be helpful, but we also welcome candidates with a biology background who are enthusiastic about learning data science approaches and applying these to biomedical problems.

Essential qualifications, skills and experience:
- Applicants must have/or expect to gain a first class or upper second class honours degree or equivalent in a relevant subject area (e.g. biology, statistics, computer science or similar).
- Basic knowledge of applied statistics.
- Good interpersonal and oral communication skills.

Desirable qualifications, skills and experience:
- A Master’s degree in Statistical Genetics, Genetic Epidemiology, Biostatistics, Computational Biology, or equivalent experience is desirable but not essential.
- Be able to use unix command line tools
- Be able to programme in R and/or Python.
- Experience of parallelisation using computing clusters.
- Experience of analysing genomic, transcriptomic, proteomic or similar high-dimensional data.

How to apply:
Applicants are requested to send a full CV (including the names and email addresses of two academic referees), and personal statement detailing why you are interested in the research project (maximum 1 side A4, font size 12 Arial). The successful candidate will be then asked to complete an electronic application form at Imperial College London in order for their qualifications to be addressed by College Registry.

Please submit your application to Annie-Rose Nicholas (). Informal enquiries can be directed to Dr James Peters ()

Funding Notes

A tax-free stipend and home UK/EU fees for 3 years will be awarded. Overseas students should be able to demonstrate adequate financial support to cover the difference between the home/EU fee and the overseas fee. Applicants are also required to meet Imperial College’s English language requirements. Please see the following link: View Website

References

Lyons PA*, Peters JE*, Alberici F*, Liley J*, Coulson RMR, et al. Genome-wide association study of eosinophilic granulomatosis with polyangiitis reveals genomic loci stratified by ANCA status. Nature Communications: in press. Preprint on BioRxiv: https://www.biorxiv.org/content/early/2018/12/10/491837

Sun BB*, Maranville JC*, Peters JE*, Stacey D, Staley JR, et al. Genomic atlas of the human plasma proteome. Nature 2018; 558:73-79.

Peters JE, Lyons PA, Lee JC, Richard AC, Newcombe PJ, Richardson S, Smith KGC. Insight into genotype-phenotype associations through eQTL mapping in multiple cell types in health and immune- mediated disease. PLoS Genetics 2016; 12:e1005908.

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