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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.

Complex diseases that affect the functioning of the central nervous system pose a major problem for modern society. Among these, ischemic stroke (IS) holds a special place as one of the most common causes of disability and mortality worldwide. Furthermore, Alzheimer’s disease (AD) ranks first among neurodegenerative diseases, drastically reducing brain activity and overall life quality and duration. Recent studies have shown that AD and IS share several common risk and pathogenic factors, such as an overlapping genomic architecture and molecular signature. In this review, we will summarize the genomics and RNA biology studies of IS and AD, discussing the interconnected nature of these pathologies. Additionally, we highlight specific genomic points and RNA molecules that can serve as potential tools in predicting the risks of diseases and developing effective therapies in the future.

Abstract Background The imputation of genotypes increases the power of genome-wide association studies. However, the imputation quality should be assessed in each particular case. Nevertheless, not all imputation softwares control the error of output, e.g., the last release of fastPHASE program (1.4.8) lacks such an option. In this particular software there is also an uncertainty in choosing the model parameters. fastPHASE is based on haplotype clusters, which size should be set a priori. The parameter influences the results of imputation and downstream analysis. Results We present a software toolkit imputeqc to assess the imputation quality and/or to choose the model parameters for imputation. We demonstrate the efficacy of toolkit for evaluation of imputations made with both fastPHASE and BEAGLE software for HapMap and 1000 Genomes data. The discordance of genotypes received correlated well in both methods. Using imputeqc, we also shown how to choose the optimal number of haplotype clusters and expectation-maximization cycles for fastPHASE program. The found number of haplotype clusters of 25 was further applied for hapFLK testing that revealed signatures of selection at LCT region on chromosome 2. We also demonstrated how to decrease the computational time in the case of hapFLK testing from 3 days to 20 h. Conclusions The toolkit is implemented as an R package imputeqc and command line scripts. The code is freely available at https://github.com/inzilico/imputeqc under the MIT license.

We performed an exhaustive pairwise comparison of whole-genome sequences of 3120 individuals, representing 232 populations from all continents and seven prehistoric people including archaic and modern humans. In order to reveal an intricate picture of worldwide human genetic relatedness, 65 million very rare single nucleotide polymorphic (SNP) alleles have been bioinformatically processed. The number and size of shared identical-by-descent (IBD) genomic fragments for every pair of 3127 individuals have been revealed. Over 17 million shared IBD fragments have been described. Our approach allowed detection of very short IBD fragments (

Natural selection of beneficial genetic variants played a critical role in human adaptation to a wide range of environmental conditions. Northern Eurasia, despite its severe climate, is home to lots of ethnically diverse populations. The genetic variants associated with the survival of these populations have hardly been analyzed. We searched for the genomic signatures of positive selection in (1) the genome-wide microarray data of 432 people from eight different northern Russian populations and (2) the whole-genome sequences of 250 people from Northern Eurasia from a public repository through testing the extended haplotype homozigosity (EHH) and direct comparison of allele frequency, respectively. The 20 loci with the strongest selection signals were characterized in detail. Among the top EHH hits were the NRG3 and NBEA genes, which are involved in the development and functioning of the neural system, the PTPRM gene, which mediates cell-cell interactions and adhesion, and a region on chromosome 4 (chr4:28.7-28.9 Mb) that contained several loci affiliated with different classes of non-coding RNAs (RN7SL101P, MIR4275, MESTP3, and LINC02364). NBEA and the region on chromosome 4 were novel selection targets that were identified for the first time in Western Siberian populations. Cross-population comparisons of EHH profiles suggested a particular role for the chr4:28.7-28.9 Mb region in the local adaptation of Western Siberians. The strongest selection signal identified in Siberian sequenced genomes was formed by six SNPs on chromosome 11 (chr11:124.9-125.2 Mb). This region included well-known genes SLC37A2 and PKNOX2. SLC37A2 is most-highly expressed in the gut. Its expression is regulated by vitamin D, which is often deficient in northern regions. The PKNOX2 gene is a transcription factor of the homeobox family that is expressed in the brain and many other tissues. This gene is associated with alcohol addiction, which is widespread in many Northern Eurasian populations.

Several studies examined the fine-scale structure of human genetic variation in Europe. However, the European sets analyzed represent mainly northern, western, central, and southern Europe. Here, we report an analysis of approximately 166,000 single nucleotide polymorphisms in populations from eastern (northeastern) Europe: four Russian populations from European Russia, and three populations from the northernmost Finno-Ugric ethnicities (Veps and two contrast groups of Komi people). These were compared with several reference European samples, including Finns, Estonians, Latvians, Poles, Czechs, Germans, and Italians. The results obtained demonstrated genetic heterogeneity of populations living in the region studied. Russians from the central part of European Russia (Tver, Murom, and Kursk) exhibited similarities with populations from central-eastern Europe, and were distant from Russian sample from the northern Russia (Mezen district, Archangelsk region). Komi samples, especially Izhemski Komi, were significantly different from all other populations studied. These can be considered as a second pole of genetic diversity in northern Europe (in addition to the pole, occupied by Finns), as they had a distinct ancestry component. Russians from Mezen and the Finnic-speaking Veps were positioned between the two poles, but differed from each other in the proportions of Komi and Finnic ancestries. In general, our data provides a more complete genetic map of Europe accounting for the diversity in its most eastern (northeastern) populations.