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(MRC DTP) Improved classification of disease in myositis for stratified medicine using statistical genetics approaches

Faculty of Biology, Medicine and Health

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Dr J Lamb , Dr H Chinoy No more applications being accepted Competition Funded PhD Project (Students Worldwide)

About the Project

Keywords: idiopathic inflammatory myopathy, myositis, autoimmune, stratified medicine, statistical genetics, disease classification

Idiopathic inflammatory myopathies (IIM) are heterogeneous autoimmune diseases characterized by inflammation of skeletal muscle (myositis). Other organ involvement due to lung, heart and skin disease and cancers can lead to increased mortality. IIM are difficult to diagnose, classify and treat; many patients are permanently disabled due to irreversible muscle damage. Improved disease classification in IIM is vital to stratify patients to improve disease management and enable earlier, more targeted treatment.

In this PhD project, we aim to improve disease classification and characterise disease subgroups in IIM. We aim to identify genetic risk factors that differentiate disease subgroups; to investigate similarities and differences in genetic risk factors between IIM and related immune-mediated inflammatory disorders, and to use publically available genetic and genomic data to increase understanding of different types of myositis.

We will use a number of statistical genetics approaches to exploit our wealth of clinical, genetic, genomic and autoantibody data to improve patient stratification. These approaches could include polygenic risk score analysis, conditional false discovery leveraging other autoimmune diseases, meta-analyses, Mendelian randomization, machine learning and integrated multi-omics approaches.

We expect these analyses to improve disease classification and our ability to stratify patients in IIM. This may improve our understanding of the pathogenesis and disease mechanisms in different forms of IIM, and how IIM relate to other immune-mediated inflammatory disorders. This research also will improve the evidence-base for stratified medicine, leading to more focussed disease management and treatment.

The student will join an active research group within the Versus Arthritis Genetics and Genomics Centre of Excellence. Training will be provided in quantitative statistical genetics skills, and interdisciplinary skills in translational and stratified medicine. There will be opportunities to attend and present results at national and international meetings and conferences, and public and patient engagement events.

Entry Requirements:
Applicants must have obtained, or be about to obtain, at least an upper second class honours degree (or equivalent) in a relevant subject.

UK applicants interested in this project should make direct contact with the Primary Supervisor to arrange to discuss the project further as soon as possible. International applicants (including EU nationals) must ensure they meet the academic eligibility criteria (including English Language) as outlined before contacting potential supervisors to express an interest in their project. Eligibility can be checked via the University Country Specific information page (

If your country is not listed you must contact the Doctoral Academy Admissions Team providing a detailed CV (to include academic qualifications – stating degree classification(s) and dates awarded) and relevant transcripts.

Following the review of your qualifications and with support from potential supervisor(s), you will be informed whether you can submit a formal online application.

To be considered for this project you MUST submit a formal online application form - full details on how to apply can be found on the MRC Doctoral Training Partnership (DTP) website

Funding Notes

Funding will cover UK tuition fees/stipend only. The University of Manchester aims to support the most outstanding applicants from outside the UK. We are able to offer a limited number of bursaries that will enable full studentships to be awarded to international applicants. These full studentships will only be awarded to exceptional quality candidates, due to the competitive nature of this scheme.

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