Genetic evidence to prioritise intervention
Using genetic data to prioritise treatments for further testing
In this workstream we use genetic data to predict the effects of medicines and identify modifiable risk factors, such as drinking alcohol or smoking.
Mendelian randomization (MR) is a ground-breaking gene-based approach pioneered in Bristol by our Medical Director George Davey Smith. This approach doesn’t involve giving people a particular treatment. Instead, it uses natural variation in our genes to test the effects of a modifiable factor to estimate the effect of that factor on disease outcomes. It also allows us to explore how different populations are affected using existing datasets from around the world.
MR is now routinely used to decide which targets to focus on for medical and public health intervention. However, it has mainly been used for disease prevention rather than treatment. To address this, we will apply our new MR methods to genetic datasets to identify potential treatment targets.
The use of MR has also mainly focused on white European populations. We will work with our large population-based study collaborators, including Global Biobank Meta-analysis Initiative and Born in Bradford, to address this. This will allow us to predict ancestry-specific effects for existing and new drugs, and to prioritise interventions for a range of ethnic groups.
We are working with our other themes, including mental health and diet and physical activity, to apply our MR approaches in their research.
Understanding the link between eczema and acne and mental health
Theme Translational data science
Workstream Genetic evidence to prioritise intervention
Using Mendelian randomization to improve how drugs are tested for different populations
Theme Translational data science
Workstream Genetic evidence to prioritise intervention
Developing a genetic data platform for use in pharmaceutical testing
Theme Translational data science
Workstream Genetic evidence to prioritise intervention
Using genetics to improve how drug targets are identified
Theme Translational data science
Workstream Genetic evidence to prioritise intervention
Understanding how adverse childhood experiences impact anxiety-related disorders in the UK and Brazil
Themes Mental health Translational data science
Workstream Genetic evidence to prioritise intervention
Investigating new approaches to drug development using human genetics
Theme Translational data science
Workstream Genetic evidence to prioritise intervention
Exploring the link between obesity and heart failure using genetics
Theme Translational data science
Workstream Genetic evidence to prioritise intervention
Preventing cardiovascular events in stroke patients
Theme Translational data science
Workstream Genetic evidence to prioritise intervention
Exploring the link between genes and cognitive decline
Theme Translational data science
Workstream Genetic evidence to prioritise intervention
Treatment resistance and drug side effects in schizophrenia
Theme Translational data science
Workstream Genetic evidence to prioritise intervention
Exploring how obesity influences cancer survival
Theme Translational data science
Workstream Genetic evidence to prioritise intervention
Data driven approaches to drug target prioritisation
Theme Translational data science
Workstream Genetic evidence to prioritise intervention