Genome-wide association studies of ischemic stroke based on interpretable machine learning

Stefan Nikolić; Dmitry I Ignatov; Gennady V Khvorykh; Svetlana A Limborska; Andrey V Khrunin

doi:10.7717/peerj-cs.2454

Genome-wide association studies of ischemic stroke based on interpretable machine learning

PeerJ Comput Sci. 2024 Nov 6:10:e2454. doi: 10.7717/peerj-cs.2454. eCollection 2024.

Authors

Stefan Nikolić¹, Dmitry I Ignatov¹, Gennady V Khvorykh², Svetlana A Limborska², Andrey V Khrunin²

Affiliations

¹ Laboratory for Models and Methods of Computational Pragmatics; Department of Data Analysis and Artificial Intelligence, HSE University, Moscow, Russia.
² National Research Centre "Kurchatov Institute", Moscow, Russia.

Abstract

Despite the identification of several dozen genetic loci associated with ischemic stroke (IS), the genetic bases of this disease remain largely unexplored. In this research we present the results of genome-wide association studies (GWAS) based on classical statistical testing and machine learning algorithms (logistic regression, gradient boosting on decision trees, and tabular deep learning model TabNet). To build a consensus on the results obtained by different techniques, the Pareto-Optimal solution was proposed and applied. These methods were applied to real genotypic data of sick and healthy individuals of European ancestry obtained from the Database of Genotypes and Phenotypes (5,581 individuals, 883,749 single nucleotide polymorphisms). Finally, 131 genes were identified as candidates for association with the onset of IS. UBQLN1, TRPS1, and MUSK were previously described as associated with the course of IS in model animals. ACOT11 taking part in metabolism of fatty acids was shown for the first time to be associated with IS. The identified genes were compared with genes from the Illuminating Druggable Genome project. The product of GPR26 representing the G-coupled protein receptor can be considered as a therapeutic target for stroke prevention. The approaches presented in this research can be used to reprocess GWAS datasets from other diseases.

Keywords: Genome-wide association studies; Illuminating druggable genome; Interpretable machine learning; Interpretable neural network TabNet; Ischemic stroke; SNP importance; SNP ranking; XGBoost.

Grants and funding

The study was funded by the Russian Science Foundation, grant number 23-14-00131 (training, evaluation and implementation of machine learning models, genome-wide association study, functional interpretation of the results obtained). The work was also an output of a research project implemented as part of the Basic Research Program at the National Research University Higher School of Economics (design of experiments and selection of methods). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.