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Predicting efficacy and safety of drugs using genetic and population data


   Bristol Medical School

  , Dr Venexia Walker  Applications accepted all year round  Self-Funded PhD Students Only

About the Project

A major factor influencing the cost of drugs is the high rate of failure during clinical trials due to inefficacy (failure to have the desired effect) or safety issues (adverse effects). Once a drug has been approved, a second challenge is identifying additional diseases for which it could be beneficial (i.e. repurposing). We have demonstrated that genetic data can predict the beneficial (and adverse) effects of drugs and provide valuable information for drug target prioritization [1,2]. In separate work we have also demonstrated that observational data (based on health outcomes of people prescribed an existing drug) can be used to inform drug repurposing [3].

In this project you will apply and compare genetic and observational epidemiology methods to identify potential drug repurposing opportunities, and help develop an understanding of factors that might affect genetic predictions of efficacy and safety to help improve the methods used for this purpose. Depending on your interests the project will include a mixture of methodological and applied research.

Aims and objectives

The overarching aim is to compare the use of genetic and observational data to predict efficacy and safety of drugs. The objectives include:

  1. Observational analysis of the effects of established drugs on a wide range of disease outcomes in UK Biobank to identify potential beneficial and adverse effects
  2. Genetic (Mendelian randomization + colocalization) analysis of the molecular targets for the drugs in (1) against the same disease outcomes to identify potential beneficial and adverse effects
  3. Comparison of genetic and observational predictions to identify and investigate disagreements, using these to inform new methodological developments

Methodology

The project will involve statistical and genetic epidemiological approaches, including regression, Mendelian randomization, genetic colocalization and genetic fine mapping. You will develop analytical code (using R) to ensure your analyses are reproducible, and will potentially develop R packages for others to use (sharing these via GitHub).

Data will include the UK Biobank, with data on up to half a million people from the UK population (https://www.ukbiobank.ac.uk/) and genetic data from our IEU OpenGWAS database, containing over 240 billion genetic associations (https://gwas.mrcieu.ac.uk/).

Training is provided in year 1 of the PhD, with short courses on statistics and genetic epidemiology (https://www.bristol.ac.uk/medical-school/study/short-courses/) and workshops on coding, data analysis and high performance computing (http://www.bris.ac.uk/acrc/acrc-training/).

Apply for this project

This project will be based in Bristol Medical School - Population Health Sciences.

Please contact for further details on how to apply.

Apply now!


References

[1] V.M. Walker et al., Int J Epidemiol 49 (2020) 1132–1140.
[2] J. Zheng et al., Nat Genet 52 (2020) 1122–1131.
[3] V.M. Walker et al., Epidemiology 31 (2020) 852–859.

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