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

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.

Evaluation of Density-Based Spatial Clustering for Identifying Genomic Loci Associated with Ischemic Stroke in Genome-Wide Data.

The genetic architecture of ischemic stroke (IS), which is one of the leading causes of death worldwide, is complex and underexplored. The traditional approach for associative gene mapping is genome-wide association studies (GWASs), testing individual single-nucleotide polymorphisms (SNPs) across the genomes of case and control groups. The purpose of this research is to develop an alternative approach in which groups of SNPs are examined rather than individual ones.

The Study of the Association of Polymorphisms in , , , , , and Genes with the Risk and Outcome of Ischemic Stroke in the Russian Population.

To date, there has been great progress in understanding the genetic basis of ischemic stroke (IS); however, several aspects of the condition remain underexplored, including the influence of genetic factors on post-stroke outcomes and the identification of causative loci. We proposed that an analysis of the results obtained from animal models of brain ischemia could be helpful. To this end, we developed a bioinformatic approach for exploring single-nucleotide polymorphisms (SNPs) in human orthologs of rat genes expressed differentially after induced brain ischemia.

[A study of genetic variants in patients with ischemic stroke in human rat gene orthologues].

Objective: To study the associations of nine genetic variants with the risk and dynamics of recovery (outcome) of ischemic stroke (IS) using the developed protocol for the search for genomic markers based on a bioinformatic approach to the study of single nucleotide polymorphisms (SNPs) in human orthologues of rat genes differentially expressed under conditions of induced cerebral ischemia.

Material And Methods: We identified and analyzed nine SNPs in 553 Russians (331 IS patients and 222 controls). The National Institutes of Health Stroke Scale (NIHSS) was used to assess stroke severity.

Analysis of Common SNPs across Continents Reveals Major Genomic Differences between Human Populations.

Common alleles tend to be more ancient than rare alleles. These common SNPs appeared thousands of years ago and reflect intricate human evolution including various adaptations, admixtures, and migration events. Eighty-four thousand abundant region-specific alleles (ARSAs) that are common in one continent but absent in the rest of the world have been characterized by processing 3100 genomes from 230 populations.