Introduction
Around 130 regions of the human genome are now known to influence an individual’s risk of heart attacks or strokes, but current methods lack the ability to show how certain genes are involved. This project involves the development of new fine-mapping learning tools that can deal with the complexities of today’s genetic datasets. This work will aid the understanding of what causes cardiovascular diseases and will help drug-makers identify potential targets to make new treatments, which are more likely to be effective.
One of six British Heart Foundation funded projects.
Explaining the science
Around 130 regions of the human genome are now known to influence an individual’s risk of heart attacks or strokes. However, we often don’t know how the genes involved exert their effects on the cardiovascular system. One way to tackle this question is to look at what effect these regions have on related human traits (e.g., blood pressure) and blood-based parameters (e.g., protein levels). However, even when a region of DNA is associated with disease risk and multiple traits, it is currently difficult to assess whether the genetic signal is the same across disease and traits (implying that the traits are causing the disease), or distinct signals that happen to be close together.
Existing methods that try to address this problem are not sufficiently scalable and can’t deal with the complexities of today’s genetic datasets without making simplifying assumptions.
This project counters these problems by producing novel probabilistic models for analysing multiple correlated traits, whether continuous or discrete, or a combination of both. The method will also deal effectively with missing data, allowing for the analysis of multiple traits measured in partially-overlapping participants.
Project aims
To develop new fine-mapping learning tools that can overcome the limitations of existing methods to assess how certain genes exert their effects on the cardiovascular system.
The tools will be applied to the 130 genetic regions known to influence an individual’s risk of heart attacks or strokes, in order to identify new risk factors for cardiovascular disease. The proposed methods will provide a set of candidate aetiological pathways (how a disease is caused) for cardiovascular disease.
This work will aid the understanding of what causes these diseases and will help drug-makers identify potential targets to make new treatments, which are more likely to be effective.
Applications
The application of the proposed methods to known cardiovascular genetic regions will help to identify causal pathways, as well as finding erroneous overlaps with traits that have been previously suggested.
These findings will suggest causal, intermediate factors for cardiovascular diseases that can be further validated through wet-lab experiments. They will also suggest possible therapeutic targets by identifying genes, proteins and other molecules that could be therapeutic targets. Lastly the findings will provide a basis for detailed insight about the disease-associated biological networks that are targeted by genetic variation.
Organisers
Researchers and collaborators
