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

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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.

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Data availability

GWAS summary statistics for AF are available on the Cardiovascular Disease Knowledge Portal (https://www.broadcvdi.org/). GWAS summary statistics for CAD can be accessed at CARDIoGRAMplusC4D Consortium (https://www.cardiogramplusc4d.org/). HBP and HF GWAS summary statistics were obtained from UK Biobank and can be accessed at Neale lab (https://www.nealelab.is/uk-biobank). GWAS summary statistics for BMI can be accessed at GIANT consortium (https://portals.broadinstitute.org/collaboration/giant/index.php/GIANT_consortium_data_files). TC GWAS summary statistics can be accessed at Global Lipids Genetics Consortium (https://csg.sph.umich.edu/willer/public/lipids2013/). GWAS summary statistics for T2D is available online at DIAGRAM (https://diagram-consortium.org/downloads.html).

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Funding

This research was benefited by grants from the National Institutes of Health (R01AR069055, U19AG055373, P20GM109036, R01AG061917), the Franklin D. Dickson/Missouri Endowment, and the Edward G. Schlieder Endowment and the Drs. W. C. Tsai and P. T. Kung Professorship in Biostatistics from Tulane University, China Oceanwide Holding Group Project Fund (Contract No.143010100), Special Funding for the Construction of Innovative Provinces in Hunan (Grant No. 2019SK2141), Hunan women’s research association (Grant No. 18ZDB03), and Innovation-Driven Project of Central South University (Grant No. 2018zzts886).

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Authors and Affiliations

  1. Xiangya Nursing School, Central South University, Changsha, 410013, China

    Zun Wang, Qian Wang & Si-Yuan Tang

  2. Department of Global Biostatistics and Data Science, Tulane Center for Bioinformatics and Genomics, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, 70112, USA

    Zun Wang, Jonathan Greenbaum, Chuan Qiu, Kelvin Li & Hong-Wen Deng

  3. School of Basic Medical Science, Central South University, Changsha, 410013, China

    Hong-Wen Deng

  4. Hunan Women’s Research Association, Changsha, 410011, China

    Si-Yuan Tang

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Wang, Z., Greenbaum, J., Qiu, C. et al. Identification of pleiotropic genes between risk factors of stroke by multivariate metaCCA analysis. Mol Genet Genomics 295, 1173–1185 (2020). https://doi.org/10.1007/s00438-020-01692-8

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