Computer model to detect prevalent diseases
New research aims to make it possible to assess people’s genetic risk for a variety of widespread diseases using a computer model.
Although we know that genes play a major role in the development of many widespread diseases, doctors are currently only able to carry out reliable genetic risk assessments of a few of them.
That some of these diseases that can be tested for is often due to the presence of an important gene, which dramatically increases the risk of developing a given disease – for instance the BRCA1 gene, which dramatically increases the risk of breast cancer (BRCA1 was the reason why actress Angelina Jolie had both of her breasts removed).
However, the vast majority of common hereditary diseases cannot currently be tested for. These diseases are the result of a complex interaction between many different genes which, together with environmental factors, increase an individual’s risk of developing a given disease.
These include many of the most prevalent diseases today, such as obesity, cardiovascular disease, type 2 diabetes, Alzheimer’s, cancer along with mental disorders such as ADHD, autism and depression, to name just a few.
However, a Danish research project now aims to remedy this shortage of adequate testing.
Bjarni Johann Vilhjalmsson, of Aarhus University, who is also affiliated with the Harvard School of Public Health, has just received a grant to develop a statistical computer tool, which he hopes will make it easier for doctors to test for far more diseases than is currently possible.
“The statistical computer model will enable us to assess the risk of a long series of diseases simply by studying a person’s sequenced genome. This will make it possible to start treatment much earlier than we can today, or suggest genetically-based lifestyle changes,” he says.
Massive genetic datasets
In his study, titled ‘Efficient Bayesian polygenic Risk Prediction’, he will be making use of the massive Danish genetic studies that have been conducted on ADHD, autism and depression.
The statistical computer model will enable us to assess the risk of a long series of diseases simply by studying a person’s sequenced genome. This will make it possible to start treatment much earlier than we can today, or suggest genetically-based lifestyle changes.
Bjarni Johann Vilhjalmsson
In these studies, tens of thousands of people suffering from these disorders had their genomes sequenced in order for the researchers to determine which genes contribute to an increased risk of developing the disorders.
“This large amount of data allows us to compare a person’s genome with tens of thousands of people who have one of these hereditary disorders. This enables the statistical model to calculate a person’s genetic risk of developing the disorder,” says Vilhjalmsson.
The results of the risk assessment are converted into a score corresponding to the increased risk: for example, a score of 4 for type 2 diabetes would indicate that, genetically speaking, the person has a four-fold risk of developing the disease, compared to the population average.
A step towards personalised medicine
The new statistical model has a number of possible applications:
- Firstly, it will make it easy for people with a hereditary disease in the family to find out if they are genetically predisposed to the disease. This not only applies to diseases caused by a single gene, but also those caused by the interaction of many different genes.
- Secondly, an analysis may also help promote lifestyle changes. An analysis can e.g. tell people that they are predisposed to cardiovascular disease and should pay more attention to diet and exercise.
- Thirdly, the statistical model is also a step towards more personalised medicine, as it can eventually become capable of telling people whether they would benefit from preventive medicine, or whether they might experience side effects from it.
”It is clear that the individual person’s genetic background will play a key role in future public health,” says Vilhjalmsson.
“In many cases, doctors will choose treatment and medication based on the individual’s genetics. My research aims to develop a tool that hopefully will promote the genetic revolution in medicine. The ultimate goal is to prevent diseases and improve patient treatment.”