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  Using Big Data to identify precision medicine targets for asthma

   College of Medicine and Veterinary Medicine

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  Dr C Simpson, Prof A Sheikh  No more applications being accepted  Funded PhD Project (European/UK Students Only)

About the Project

The UK has the highest prevalence of asthma and some of the poorest health outcomes from asthma in the world. This results in a substantially high healthcare, personal and societal burden for the UK [1]. It is now widely recognised that asthma is in fact a heterogeneous group of conditions [2] and greater appreciation of asthma phenotypes and endotypes has the potential to allow improved tailoring of treatments. This understanding is as yet not translated into primary care where over 90% of asthma patients are exclusively managed. General practice databases are a rich source of phenotypic/endotypic information [3] and are further enriched through linkage to other routinely collected sources of data or by machine learning (ML) and natural language processing (NLP) of electronic free text recorded in the clinical record. These databases are also increasingly being used in the support of clinical trials. For instance, the increasing amount of electronic health data being collected for patient care can be used to support clinical research by identifying potential study participants and following up for outcomes [4].

The primary aim of this PhD will be to investigate approaches to deeper characterisation of asthma phenotypes/endotypes using electronic health records in primary care with a view to identifying those who have the potential to benefit from precision medicine treatment approaches.

• The PhD will involve exploration and interrogation of very large GP databases (SIVE II, OPCRD and SAIL) using a range of epidemiological, machine learning and NLP techniques and then validating the potential signals in a second independent database;
• Building on this database infrastructure, a computerised decision support algorithm will be constructed that will help frontline clinicians to identify those who may benefit from further investigation/specific interventions;
• The extent to which routine electronic medical record systems can support the identification of potential study participants (e.g. by characterising the allergic asthma phenotype) and follow them for longer-term outcomes via a pilot clinical trial will be explored.

Based within the Usher Institute of Population Health Sciences and Informatics, the PhD will capitalise on The Farr Institute’s and the Asthma UK Centre for Applied Research’s (AUKCAR) extensive informatics and disease-specific infrastructures and the synergistic relationship between these UK-wide endeavours. These organisations have embedded patient and public involvement and knowledge exchange, in addition to strong links with industry which will be encouraged to develop through placements or workshops. AUKCAR members also sit on National Institute for Health and Care Excellence (NICE) and British Thoracic Society/ Scottish Intercollegiate Guidelines Network (BTS/SIGN) asthma guidelines and they will, therefore, be ideally placed to translate the findings from this work into national asthma guidelines.

The PhD student will be trained in advanced epidemiology statistical techniques, natural language processing, machine learning and informatics. The PhD student will also gain knowledge regarding current Randomised Clinical Trials (RCT) for people with asthma, trial and use of data in RCT design methodology and the use of linked electronic health records and emerge as a major leader in the field of population-based informatics and precision medicine of asthma.

This MRC DTP programme is joint between the Universities of Edinburgh and Glasgow. You will be registered at the host institution of the primary supervisor detailed in your project selection.

You can apply here via the University of Glasgow:
Within the application, at the programme of study search field option, please select ‘MRC DTP in Precision Medicine’.

Please note that, in step 6 within the online application process, you are asked to detail supervisor/project title information. Please ensure that you clearly detail this information from the information provided within this abstract advert. Within the research area text box area, you can also add further details if necessary.

Please ensure that all of the following supporting documents are uploaded at point of application:
• CV/Resume
• Degree certificate (if you have graduated prior to 1 July 2016)
• Language test (if relevant)
• Passport
• Personal statement
• Reference 1 (should be from an academic who has a knowledge of your academic ability from your most recent study/programme)
• Reference 2 (should be from an academic who has a knowledge of your academic ability)
• Transcript

For more information about Precision Medicine at the University of Edinburgh, visit

Funding Notes

Start date:
September/October 2016

Qualifications criteria:
Applicants applying for a MRC DTP in Precision Medicine studentship must have obtained, or soon will obtain, a first or upper-second class UK honours degree or equivalent non-UK qualifications, in an appropriate science/technology area.

Residence criteria:
The MRC DTP in Precision Medicine grant provides tuition fees and stipend of £14,296 (RCUK rate 2016/17) for UK and *EU nationals that meet all required eligibility criteria.

(*must have been resident in the UK for three years prior to commencing studentship)

Full qualifications and residence eligibility details are available here:

General enquiries regarding programme/application procedure: [Email Address Removed]


1. Gupta R, Sheikh A, Strachan DP, et al. Burden of allergic disease in the UK: secondary analyses of national databases. Clin Exp Allergy 2004; 34, 520-6
2. Bush A, Kleinert S, Pavord ID. The asthmas in 2015 and beyond: a Lancet Commission. Lancet 2015;385:1273-5
3. Simpson CR, et al. Seasonal Influenza Vaccination Effectiveness II (SIVE II): Use of a large national primary care and laboratory-linked dataset. NIHR-HTA.
4. Wallace P, Delaney B, Sullivan F. Unlocking the research potential of the GP electronic care record. Br J Gen Pract. 2013;63:284-5

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