HSE Researchers Teach Neural Network to Distinguish Origins from Genetically Similar Populations
Researchers from the AI and Digital Science Institute, HSE Faculty of Computer Science, have proposed a new approach based on advanced machine learning techniques to determine a person’s genetic origin with high accuracy. This method uses graph neural networks, which make it possible to distinguish even very closely related populations.
Over the past 10–15 years, genetic analysis has become increasingly popular not only as a tool for medical diagnostics, but also as a means of ancestry research. DNA testing allows people to learn more about their ethnic background, identify the places where their ancestors lived, and determine the number of Neanderthal mutations in a person’s genome.
This has become possible thanks to the development of modern technologies—such as genotyping, data storage and processing systems, and machine learning—and the significant reduction in their cost. However, current testing methods are unable to differentiate between genetically similar populations that have lived in adjacent regions for extended periods.
Researchers from the AI and Digital Science Institute have developed a method for distinguishing between individuals from closely related populations. At the heart of this technology are graph neural networks, which do not rely on DNA sequences but instead use graphs to represent genetic links between individuals with shared genome segments. These shared segments indicate the degree of kinship between people, revealing how many generations back their common ancestors lived. The more overlaps there are, the closer their ancestral connection is. In the model, each person is represented by a vertex in the graph, and the strength of the connection between them is indicated by the edges in the graph.
The method was tested on data from various regions. The results were particularly insightful for the population of the East European Plain, as a large dataset had already been compiled there. The graph neural network was able to accurately determine the population affiliation of individuals from genetically similar ethnic groups.
Aleksei Shmelev
‘Existing methods of genetic analysis address a different task: they identify affiliation with large, isolated groups, such as determining whether someone has French, German, or English ancestry. Our method enables the analysis of closely related populations, which is particularly relevant for Russia, a country with a diverse ethnic background,’ said Aleksei Shmelev, one of the study's authors and Research Assistant at the HSE International Laboratory of Statistical and Computational Genomics, AI and Digital Science Institute.
In their future work, the researchers aim to train the neural network to predict the proportion of different populations within a genome.
They have named their development AncestryGNN, which stands for 'Neural Network-Based Prediction of Population Affiliation via Shared Genome Segments.’
Vladimir Shchur
As noted by Vladimir Shchur, Head of the International Laboratory of Statistical and Computational Genomics at the AI and Digital Science Institute, HSE University, the proposed method holds great potential for more accurate understanding of human history and can be applied in genealogy and anthropology research.
This research was supported by a grant from the Government of the Russian Federation as part of the federal program ‘Artificial Intelligence.’
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