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

Improving decisions on what to focus on in research using large datasets

Research using de-personalised data from electronic health records is increasingly common.  Electronic…

Theme Translational data science

Workstream Large, complex datasets

Exploring inflammation as a driver for post-operative complications

One in seven patients develop a serious medical problem after surgery. These types of complications…

Theme Translational data science

Workstream Omics for prediction and prognosis

Investigating new approaches to drug development using human genetics

Developing new drugs is an important part of improving our ability to treat disease. To…

Theme Translational data science

Workstream Genetic evidence to prioritise intervention

Exploring the link between obesity and heart failure using genetics

Heart failure is a condition that develops when the heart unable to pump blood around…

Theme Translational data science

Workstream Genetic evidence to prioritise intervention

Lung development in early life and respiratory diagnosis and treatment

More than 17 per cent of deaths around the world can be attributed to respiratory…

Themes Respiratory disease Translational data science

Workstream Exacerbation prediction and aerosol emissions

Can we use DNA methylation to predict disease in diverse populations?

DNA methylation is a process during which methyl groups become attached to parts of our…

Theme Translational data science

Workstream Omics for prediction and prognosis

South West Secure Data Environment

Secure Data Environments (SDEs) are online platforms for analysing health and social care data for…

Theme Translational data science

Workstream Clinical informatics platforms

Preventing cardiovascular events in stroke patients

Having a stroke means you are more likely to experience a subsequent cardiovascular event. Cardiovascular…

Theme Translational data science

Workstream Genetic evidence to prioritise intervention

Exploring the link between genes and cognitive decline

Cognitive decline in older adults refers to the difficulties someone may experience later in life…

Theme Translational data science

Workstream Genetic evidence to prioritise intervention

Do ethnicity and coexisting health conditions impact high-risk diabetes?

About a third of people diagnosed with type 2 diabetes have very high blood sugar…

Theme Translational data science

Workstreams Clinical informatics platforms Large, complex datasets

Handling missing data in large electronic healthcare record datasets

Electronic healthcare records (EHRs) are created when healthcare professionals record information about the health of…

Theme Translational data science

Workstream Large, complex datasets

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

Treatment resistance and drug side effects in schizophrenia

Schizophrenia is a mental health condition where people may see, hear or believe things that…

Theme Translational data science

Workstream Genetic evidence to prioritise intervention

Exploring how obesity influences cancer survival

Evidence from different studies suggests that obesity or body mass index (BMI) might play a…

Theme Translational data science

Workstream Genetic evidence to prioritise intervention

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

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