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Identification of 67 pleiotropic genes for seven autoimmune diseases using multivariate statistical analysis

By Xiaocan Jia, Nian Shi, Zhenhua Xia, Yu Feng, Yifan Li, Jiebing Tan, Fei Xu, Wei Wang, Changqing Sun, Hongwen Deng, Yongli Yang, Xuezhong Shi

Posted 28 Feb 2019
bioRxiv DOI: 10.1101/563973

Although genome-wide association studies (GWAS) have a dramatic impact on susceptibility locus discovery, this univariate approach has limitation in detecting complex genotype-phenotype correlations. It is essential to identify shared genetic risk factors acting through common biological mechanisms of autoimmune diseases with a multivariate analysis. In this study, the GWAS summary statistics including 41,274 single nucleotide polymorphisms (SNPs) located in 11,516 gene regions was analyzed to identify shared variants of seven autoimmune diseases using metaCCA method. Gene-based association analysis was used to refine the pleiotropic genes. In addition, GO term enrichment analysis and protein-protein interaction network analysis were applied to explore the potential biological function of the identified genes. After metaCCA analysis, 4,962 SNPs (P<1.21×10−6) and 1,044 pleotropic genes (P<4.34×10−6) were identified. By screening the results of gene-based p-values, we identified the existence of 27 confirmed pleiotropic genes and highlighted 40 novel pleiotropic genes which achieved significance threshold in metaCCA analysis and were also associated with at least one autoimmune disease in the VEGAS2 analysis. The metaCCA method could identify novel variants associated with complex diseases incorporating different GWAS datasets. Our analysis may provide insights for some common therapeutic approaches of autoimmune diseases based on the pleiotropic genes and common mechanisms identified.

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