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Research Theme

Translational Data Science

You are here: Home > Research Overview > Translational Data Science > Sub-theme 3: Translational genomics

Sub-theme 3: Translational genomics

Led by professor Cecilia Lindgren

Through the major cohorts of patients created in Oxford, including the UK Biobank and the Million Women Study, our theme has access to the incredibly rich data required to better link genotype to phenotype – matching a person’s genetic make-up to their other observable traits – and so better predict risk and stratify cases, which in turn will inform the care a patient receives.

This work – developing the right methodology and approaches, and building effective datasets to do it most effectively – complements the work being done by the BRC’s Genomic Medicine theme, and will play a central role to the development of Polygenic Risk Scores and more personalised medicine.

We are using the genetic information from our cohorts – and international collaborations – to help identify new drug targets, repurpose existing drugs for conditions such as cardiovascular disease and cancer, as well as identifying those patients for whom a drug may be less effective or even harmful. Promising drug targets and repurposed medication will be developed with a number of commercial partners.

All our analyses will take demographic differences – including heritage, sex and age – into account to ensure that we properly identify and address potential health inequities.

We are working with our NHS networks, including the Thames Valley Cancer Alliance and the regional Inherited Cardiac Conditions service on integrating the use of genetic information and risk scores into patient records, delivering additional benefits in terms of care quality and outcomes.

Translational Data Science

  • Translational Data Science
  • Sub-theme 1: Healthcare observation and transformation
  • Sub-theme 2: Deep phenotyping
  • Sub-theme 3: Translational genomics
  • Sub-theme 4: Clinical trials and evaluation
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