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Identification of pleiotropic genes between risk factors of stroke by multivariate metaCCA analysis.

ABSTRACT: Genome-wide association studies (GWASs) have identified more than 20 genetic loci as risk predictors associated with stroke. However, these studies were generally performed for single-trait and failed to consider the pleiotropic effects of these risk genes among the multiple risk factors for stroke. In this study, we applied a novel metaCCA method followed by gene-based VEGAS2 analysis to identify the risk genes for stroke that may overlap between seven correlated risk factors (including atrial fibrillation, hypertension, coronary artery disease, heart failure, diabetes, body mass index, and total cholesterol level) by integrating seven corresponding GWAS data. We detected 20 potential pleiotropic genes that may be associated with multiple risk factors of stroke. Furthermore, using gene-to-trait pathway analysis, we suggested six potential risk genes (FUT8, GMIP, PLA2G6, PDE3A, SMARCA4, SKAPT) that may affect ischemic or hemorrhage stroke through multiple intermediate factors such as MAPK family. These findings provide novel insight into the genetic determinants contributing to the concurrent development of biological conditions that may influence stroke susceptibility, and also indicate some potential therapeutic targets that can be further studied for the prevention of cerebrovascular disease.

SUBMITTER: Wang Z

PROVIDER: S-EPMC7394724 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Identification of pleiotropic genes between risk factors of stroke by multivariate metaCCA analysis.

Wang Zun Z Greenbaum Jonathan J Qiu Chuan C Li Kelvin K Wang Qian Q Tang Si-Yuan SY Deng Hong-Wen HW

Molecular genetics and genomics : MGG 20200530 5

Genome-wide association studies (GWASs) have identified more than 20 genetic loci as risk predictors associated with stroke. However, these studies were generally performed for single-trait and failed to consider the pleiotropic effects of these risk genes among the multiple risk factors for stroke. In this study, we applied a novel metaCCA method followed by gene-based VEGAS2 analysis to identify the risk genes for stroke that may overlap between seven correlated risk factors (including atrial ...[more]

PMID: 32474671

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Project description:BackgroundStroke ranks first among disease fatalities, and those who do survive stroke are prone to cognitive impairment. The aim of this study was to explore the clinical characteristics of post stroke cognitive impairment (PSCI) and the risk factors of PSCI using multivariate logistic regression.MethodsJanuary 2018 to January 2021, the clinical data of 120 patients treated for cerebral ischemic stroke (CIS) at Chengde Central Hospital were retrospectively analyzed. In this study, patients were divided into 2 groups: a control group and a cognitive impairment group. The clinical characteristics of cognitive impairment following CIS were determined using multivariate logistic regression analysis to examine the risk factors and identify clinical implications.ResultsThis study included the assessment of overall cognitive function and daily living activities of 120 participants, 68 of whom experienced cognitive impairment, representing an incidence of 57%, while 43% patients represented no cognitive impairment after CIS. After the careful analysis of the data, there were remarkable differences in age, sex, education level, stroke history, infarction area, and infarction location (P<0.05). There was no remarkable difference in the history of hypertension, diabetes, atrial fibrillation, carotid intima thickness, smoking, or drinking (P>0.05). The degree of white matter degeneration, brain atrophy, and dominant hemisphere involvement was higher in the cognitive impairment group (P<0.05). The results of multivariate logistic regression analysis indicated that sex, age, education level, stroke history, infarction size, and infarction location were the main risk factors for cognitive impairment after CIS (P<0.05).ConclusionsPatients with cognitive impairment after CIS have imaging features of white matter degeneration, brain atrophy, and involvement of dominant hemispheres. The results of multivariate logistic regression analysis indicated that sex, age, education level, stroke history, infarct size, and infarct location were main risk factors of cognitive impairment after CIS.

Dataset Information

Identification of pleiotropic genes between risk factors of stroke by multivariate metaCCA analysis.

Publications

Identification of pleiotropic genes between risk factors of stroke by multivariate metaCCA analysis.

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