Abstract
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.
Highlights
Stroke, including ischemic stroke (IS), is a multifactorial disease and one of the main causes of morbidity and mortality worldwide [1]
Of 331 IS patients analyzed in Study 1, 116 were classified as the functional recovery group and 214 were nonfunctional recovery group
One hundred and fifty-five patients demonstrated positive dynamics in changes to their modified Rankin scale (mRS) scores (∆mRS > 0); negative changes were detected in 67 patients (∆mRS < 0); and 108 patients were stable (∆mRS = 0) (Table 2)
Summary
Stroke, including ischemic stroke (IS), is a multifactorial disease and one of the main causes of morbidity and mortality worldwide [1]. They have poor precision in identifying real causal variants or genes caused by the linkage disequilibrium-based architecture of DNA arrays used, which has hindered the development of biological insights and clinical translation of GWA findings, and requires functional validation of each gene association discovered [8]. This deficiency highlights the need for the development of new approaches that can delineate causal genetic variants and biological mechanisms underlying the associations observed with IS. They can be realized in enhanced downstream characterization of identified loci through the involvement of different ‘omics’ data and advanced analytic techniques, or in direct functional dissection of particular variants and genes [8,9,10]
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