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.
Treatment resistance and drug side effects in schizophrenia
Theme Translational data science
Workstream Genetic evidence to prioritise intervention
Great Western Secure Data Environment
Theme Translational data science
Workstream Clinical informatics platforms
Preventing cardiovascular events in stroke patients
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
Using biomarkers and machine learning to predict antidepressant resistance
Theme Translational data science
Workstream Omics for prediction and prognosis
Can DNA methylation biomarkers predict whether pleural effusion is caused by cancer?
Theme Translational data science
Workstream Omics for prediction and prognosis
Using DNA methylation biomarkers to understand Parkinson’s disease severity and progression
Theme Translational data science
Workstream Omics for prediction and prognosis
Biomarkers for screening and diagnosing lung cancer
Theme Translational data science
Workstream Omics for prediction and prognosis
Creating the infrastructure to enable translational data analysis at scale
Theme Translational data science
Workstreams Clinical informatics platforms Large, complex datasets
Data driven approaches to drug target prioritisation
Theme Translational data science
Workstream Genetic evidence to prioritise intervention