Genetic evidence to prioritise intervention

Using genetic data to prioritise treatments for further testing

Theme Translational data science

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.

View all research projects

Using biomarkers and machine learning to predict antidepressant resistance

Around half of patients with depression don’t improve after taking antidepressants. Clinicians need to…

Theme Translational data science

Workstream Omics for prediction and prognosis

Can DNA methylation biomarkers predict whether pleural effusion is caused by cancer?

Pleural effusion, where fluid builds up in the cavity around the lungs, can develop…

Theme Translational data science

Workstream Omics for prediction and prognosis

Using DNA methylation biomarkers to understand Parkinson’s disease severity and progression

The Biogen Tel Aviv Parkinson Project (BeatPD) looks in-depth at clinical and genetic information…

Theme Translational data science

Workstream Omics for prediction and prognosis

Biomarkers for screening and diagnosing lung cancer

In the UK, only 15 per cent of people diagnosed with lung cancer will still…

Theme Translational data science

Workstream Omics for prediction and prognosis

Creating the infrastructure to enable translational data analysis at scale

Our priority is to create the data infrastructure to enable analysis of linked administrative and…

Theme Translational data science

Workstreams Clinical informatics platforms Large, complex datasets

Data driven approaches to drug target prioritisation

Despite more money going towards developing drugs, the success rate of getting new drugs to…

Theme Translational data science

Workstream Genetic evidence to prioritise intervention