Multivariate analysis of genomics data to identify potential pleiotropic genes for type 2 diabetes, obesity and dyslipidemia using Meta-CCA and gene-based approach

Yuan-Cheng Chen, Chao Xu, Ji-Gang Zhang, Chun-Ping Zeng, Xia-Fang Wang, Rou Zhou, Xu Lin, Zeng-Xin Ao, Jun-Min Lu, Jie Shen and Hong-Wen Deng

PLOS ONE, 2018, vol. 13, issue 8, 1-16

Abstract: Previous studies have demonstrated the genetic correlations between type 2 diabetes, obesity and dyslipidemia, and indicated that many genes have pleiotropic effects on them. However, these pleiotropic genes have not been well-defined. It is essential to identify pleiotropic genes using systematic approaches because systematically analyzing correlated traits is an effective way to enhance their statistical power. To identify potential pleiotropic genes for these three disorders, we performed a systematic analysis by incorporating GWAS (genome-wide associated study) datasets of six correlated traits related to type 2 diabetes, obesity and dyslipidemia using Meta-CCA (meta-analysis using canonical correlation analysis). Meta-CCA is an emerging method to systematically identify potential pleiotropic genes using GWAS summary statistics of multiple correlated traits. 2,720 genes were identified as significant genes after multiple testing (Bonferroni corrected p value

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0201173

DOI: 10.1371/journal.pone.0201173

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