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. We proposed, validated and applied to real data a new workflow consisting of three key stages: grouping SNPs in clusters, inferring the haplotypes in the clusters and testing haplotypes for the association with phenotype. To group SNPs, we applied the clustering algorithms DBSCAN and HDBSCAN to linkage disequilibrium (LD) matrices, representing pairwise r2 values between all genotyped SNPs. These clustering algorithms have never before been applied to genotype data as part of the workflow of associative studies. In total, 883,908 SNPs and insertion/deletion polymorphisms from people of European ancestry (4929 cases and 652 controls) were processed. The subsequent testing for frequencies of haplotypes restored in the clusters of SNPs revealed dozens of genes associated with IS and suggested the complex role that protocadherin molecules play in IS. The developed workflow was validated with the use of a simulated dataset of similar ancestry and the same sample sizes. The results of classic GWASs are also provided and discussed. The considered clustering algorithms can be applied to genotypic data to identify the genomic loci associated with different qualitative traits, using the workflow presented in this research.

The Study of the Association of Polymorphisms in LSP1, GPNMB, PDPN, TAGLN, TSPO, and TUBB6 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. Using this approach, we identified and analyzed 11 SNPs from 6 genes in 553 Russian individuals (331 patients with IS and 222 controls). We assessed the association of SNPs with the risk of IS and IS outcomes. We found that the SNPs rs858239 (GPNMB), rs907611 (LSP1), and rs494356 (TAGLN) were associated with different parameters of IS functional outcomes. In addition, the SNP rs1261025 (PDPN) was associated significantly with IS itself (p = 0.0188, recessive model). All these associations were demonstrated for the first time. Analysis of the literature suggests that they should be characterized as being inflammation related. This supports the pivotal role of inflammation in both the incidence of stroke and post-stroke outcomes. We believe the findings reported here will help with stroke prognosis in the future.

Examination of Genetic Variants Revealed from a Rat Model of Brain Ischemia in Patients with Ischemic Stroke: A Pilot Study.

Although there has been great progress in understanding the genetic bases of ischemic stroke (IS), many of its aspects remain underexplored. These include the genetics of outcomes, as well as problems with the identification of real causative loci and their functional annotations. Therefore, analysis of the results obtained from animal models of brain ischemia could be helpful. We have developed a bioinformatic approach exploring single nucleotide polymorphisms (SNPs) in human orthologues of rat genes expressed differentially under conditions of induced brain ischemia. Using this approach, we identified and analyzed nine SNPs in 553 Russian individuals (331 patients with IS and 222 controls). We explored the association of SNPs with both IS outcomes and with the risk of IS. SNP rs66782529 (LGALS3) was associated with negative IS outcomes (p = 0.048). SNPs rs62278647 and rs2316710 (PTX3) were associated significantly with IS (p = 0.000029 and p = 0.0025, respectively). These correlations for rs62278647 and rs2316710 were found only in women, which suggests a sex-specific association of the PTX3 polymorphism. Thus, this research not only reveals some new genetic associations with IS and its outcomes but also shows how exploring variations in genes from a rat model of brain ischemia can be of use in searching for human genetic markers of this disorder.

Global Picture of Genetic Relatedness and the Evolution of Humankind.

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 (<20 kb) that trace common ancestors who lived up to 200,000 years ago. We detected nine distinct geographical regions within which individuals had strong genetic relatedness, but with negligible relatedness between the populations of these regions. The regions, comprising nine unique genetic components for mankind, are the following: East and West Africa, Northern Europe, Arctica, East Asia, Oceania, South Asia, Middle East, and South America. The level of admixture in every studied population has been apportioned among these nine genetic components. Genetically, long-term neighboring populations are strikingly similar to each other in spite of any political, religious, and cultural differences. The topmost admixture has been observed at the center of Eurasia. These admixed populations (including Uyghurs, Azerbaijanis, Uzbeks, and Iranians) have roughly equal genetic contributions from the Middle East, Europe, China, and India, with additional significant traces from Africa and Arctic. The entire picture of relatedness of all the studied populations unfolds and presents itself in the form of shared number/size of IBDs.

imputeqc: an R package for assessing imputation quality of genotypes and optimizing imputation parameters.

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.

Genomic landscape of the signals of positive natural selection in populations of Northern Eurasia: A view from Northern Russia.

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.