Machine Learning Links Two New Genes to Ischemic Stroke
A team of scientists from HSE University and the Kurchatov Institute used machine learning methods to investigate genetic predisposition to stroke. Their analysis of the genomes of over 5,000 people identified 131 genes linked to the risk of ischemic stroke. For two of these genes, the association was found for the first time. The paper has been published in PeerJ Computer Science.
Ischemic stroke is a major cause of death and disability worldwide. This condition occurs when blood supply to a part of the brain is interrupted, causing cell death and impaired brain function. Scientists have long studied the genetic factors influencing stroke risk, but a definitive list of genes linked to stroke predisposition has yet to be established. There are hopes that artificial intelligence methods may provide answers in this regard.
A team of scientists from the HSE Faculty of Computer Science and the Kurchatov Institute proposed using machine learning algorithms to analyse genetic predisposition to stroke. They analysed genomic data from 5,500 unrelated individuals over the age of 55, including ischemic stroke survivors and their healthy counterparts. Samples for the study were collected from 11 laboratories in Europe and 13 in the United States.
The analysis was based on the concept of ranking through learning. First, the researchers developed a predictive model in which the key parameter was the presence or absence of a stroke. Single nucleotide polymorphisms (SNPs), which are variations in the genome at specific sites, were used as markers. The scientists then ranked these markers and selected the most significant ones.
SNPs were analysed and selected using various methods, enabling a new analysis of the data and the identification of genes previously not associated with ischemic stroke. The list of 'suspicious' genetic markers common to two or more methods highlights the reliability of the results.
Working with such a large dataset—nearly 900,000 SNPs per 5,500 participants—required us to move beyond purely statistical analysis methods. Machine learning made it possible to process all of this. As a result, we identified 131 genes, most of which had already been linked to ischemic stroke. However, for two of these genes, this was the first time we discovered the association,' explains Dmitry Ignatov, Head of the Laboratory for Models and Methods of Computational Pragmatics at HSE University.
In particular, the scientists found an association between stroke and ACOT11, a gene involved in fatty acid metabolism and shown in animal experiments to affect inflammatory processes and blood lipid levels. The second gene newly linked to ischemic stroke is UBQLN1, which is involved in the mechanisms that protect cells from oxidative stress. There is evidence that a mutation in this gene is associated with neurodegenerative diseases.
These discoveries could help develop multigenic risk models that predict a person's predisposition to stroke. Information about the newly associated genes could also serve as the foundation for developing drugs and therapies aimed at reducing the risk of ischemic stroke.
Gennady Khvorykh
'Identifying two new stroke-associated genes is an excellent outcome for any method. Our machine learning approach clearly holds strong potential for detecting genes linked to diseases that result from a variety of factors,' comments Gennady Khvorykh, Chief Specialist at the Kurchatov Institute.
The proposed approach to analysing genetic markers demonstrates versatility and can be effectively adapted for a wide range of studies beyond ischemic stroke. This methodology can be applied to any diseases or markers with data available in the 'sample—SNP—class' format.
'Although we initially developed this tool for a specific task, the results reveal its potential in a broader context. The ability to work with a variety of genetic data makes our method valuable to researchers across various fields of biology and medicine,' says Stefan Nikolić, graduate of the Faculty of Computer Science and the Doctoral School of Computer Science at HSE University.
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