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.’
See also:
Larger Groups of Students Use AI More Effectively in Learning
Researchers at the Institute of Education and the Faculty of Economic Sciences at HSE University have studied what factors determine the success of student group projects when they are completed with the help of artificial intelligence (AI). Their findings suggest that, in addition to the knowledge level of the team members, the size of the group also plays a significant role—the larger it is, the more efficient the process becomes. The study was published in Innovations in Education and Teaching International.
New Models for Studying Diseases: From Petri Dishes to Organs-on-a-Chip
Biologists from HSE University, in collaboration with researchers from the Kulakov National Medical Research Centre for Obstetrics, Gynecology, and Perinatology, have used advanced microfluidic technologies to study preeclampsia—one of the most dangerous pregnancy complications, posing serious risks to the life and health of both mother and child. In a paper published in BioChip Journal, the researchers review modern cellular models—including advanced placenta-on-a-chip technologies—that offer deeper insights into the mechanisms of the disorder and support the development of effective treatments.
Using Two Cryptocurrencies Enhances Volatility Forecasting
Researchers from the HSE Faculty of Economic Sciences have found that Bitcoin price volatility can be effectively predicted using Ethereum, the second-most popular cryptocurrency. Incorporating Ethereum into a predictive model reduces the forecast error to 23%, outperforming neural networks and other complex algorithms. The article has been published in Applied Econometrics.
Administrative Staff Are Crucial to University Efficiency—But Only in Teaching-Oriented Institutions
An international team of researchers, including scholars from HSE University, has analysed how the number of non-academic staff affects a university’s performance. The study found that the outcome depends on the institution’s profile: in research universities, the share of administrative and support staff has no effect on efficiency, whereas in teaching-oriented universities, there is a positive correlation. The findings have been published in Applied Economics.
Physicists at HSE University Reveal How Vortices Behave in Two-Dimensional Turbulence
Researchers from the Landau Institute for Theoretical Physics of the Russian Academy of Sciences and the HSE University's Faculty of Physics have discovered how external forces affect the behaviour of turbulent flows. The scientists showed that even a small external torque can stabilise the system and extend the lifetime of large vortices. These findings may improve the accuracy of models of atmospheric and oceanic circulation. The paper has been published in Physics of Fluids.
Solvent Instead of Toxic Reagents: Chemists Develop Environmentally Friendly Method for Synthesising Aniline Derivatives
An international team of researchers, including chemists from HSE University and the A.N. Nesmeyanov Institute of Organoelement Compounds of the Russian Academy of Sciences (INEOS RAS), has developed a new method for synthesising aniline derivatives—compounds widely used in the production of medicines, dyes, and electronic materials. Instead of relying on toxic and expensive reagents, they proposed using tetrahydrofuran, which can be derived from renewable raw materials. The reaction was carried out in the presence of readily available cobalt salts and syngas. This approach reduces hazardous waste and simplifies the production process, making it more environmentally friendly. The study has been published in ChemSusChem.
How Colour Affects Pricing: Why Art Collectors Pay More for Blue
Economists from HSE University, St Petersburg State University, and the University of Florida have found which colours in abstract paintings increase their market value. An analysis of thousands of canvases sold at auctions revealed that buyers place a higher value on blue and favour bright, saturated palettes, while showing less appreciation for traditional colour schemes. The article has been published in Information Systems Frontiers.
New Method for Describing Graphene Simplifies Analysis of Nanomaterials
An international team, including scientists from HSE University, has proposed a new mathematical method to analyse the structure of graphene. The scientists demonstrated that the characteristics of a graphene lattice can be represented using a three-step random walk model of a particle. This approach allows the lattice to be described more quickly and without cumbersome calculations. The study has been published in Journal of Physics A: Mathematical and Theoretical.
Scientists Have Modelled Supercapacitor Operation at Molecular and Ionic Level
HSE scientists used supercomputer simulations to study the behaviour of ions and water molecules inside the nanopores of a supercapacitor. The results showed that even a very small amount of water alters the charge distribution inside the nanopores and influences the device’s energy storage capacity. This approach makes it possible to predict how supercapacitors behave under different electrolyte compositions and humidity conditions. The paper has been published in Electrochimica Acta. The study was supported by a grant from the Russian Science Foundation (RSF).
Designing an Accurate Reading Skills Test: Why Parallel Texts are Important in Dyslexia Diagnosis
Researchers from the HSE Centre for Language and Brain have developed a tool for accurately assessing reading skills in adults with reading impairments. It can be used, for instance, before and after sessions with a language therapist. The tool includes two texts that differ in content but are equal in complexity: participants were observed to read them at the same speed, make a similar number of errors, and understand the content to the same degree. Such parallel texts will enable more accurate diagnosis of dyslexia and better monitoring of the effectiveness of interventions aimed at addressing it. The paper has been published in Educational Studies.