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Untangling the Genetic Basis of Drug Response

  • Title: Untangling the Genetic Basis of Drug Response
  • Funded by: The Lundbeck Foundation
  • AU project manager: Postdoc Palle Duun Rohde, Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics.
  • Collaboration partners: Big Data Institute (BDI), University of Oxford
  • Project period: 26 February 2019 – 25 February 2022
  • Funding: 1,850,000 DKK

Project description

Providing personalised treatment with precise medication matched to a patient’s genetic profile to maximise treatment effect, and minimising adverse drug reactions is currently a major focus in health and biomedicine research. This requires detailed knowledge of the genetic basis for why patients with the same diagnosis respond differently to the same drug treatment. Currently, the knowledge on the genetic basis of drug response variability is too limited for a general implementation of personalised medicine. The objective of the project is to leverage large-scale, heterogeneous data on medical history, prior and current drug usage and lifestyle parameters to identify common patterns of drug usage and utilise these signatures to obtain novel knowledge of the genetic basis underlying drug response.

Patients with the same diagnosis may respond differently to the same medical treatment, and variation in genetic profiles is proposedly a contributing factor. We propose three genetic mechanisms that contributes to drug response variability; 1) the disease and the response to treatment are two independent traits with separate genetic basis; 2) one disease diagnosis, but variation in lifestyle and environmental exposures leads to sub-categories of the disease; 3) several genetic signatures can lead to the same disease, which are the fundament for variation in drug response. We aim to develop a novel analytical framework for utilising and exploiting large-scale heterogeneous data available in the UK Biobank to untangle the genetic basis of drug response of three major disease categories: metabolic diseases, cancer types and mental health.

Personalised and targeted therapies have enormous perspectives; however, currently the usage of genetic profiles in personalised treatment is limited. The outcome of this project has the perspective to enhance our knowledge of drug response variability, and move the field of personalised medicine further towards a general implementation.