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Jia X, Shi N, Feng Y, Li Y, Tan J, Xu F, Wang W, Sun C, Deng H, Yang Y, Shi X. Identification of 67 Pleiotropic Genes Associated With Seven Autoimmune/Autoinflammatory Diseases Using Multivariate Statistical Analysis. Front Immunol 2020; 11:30. [PMID: 32117227 PMCID: PMC7008725 DOI: 10.3389/fimmu.2020.00030] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2019] [Accepted: 01/08/2020] [Indexed: 12/19/2022] Open
Abstract
Although genome-wide association studies (GWAS) have a dramatic impact on susceptibility locus discovery, this univariate approach has limitations in detecting complex genotype-phenotype correlations. Multivariate analysis is essential to identify shared genetic risk factors acting through common biological mechanisms of autoimmune/autoinflammatory diseases. In this study, GWAS summary statistics, including 41,274 single nucleotide polymorphisms (SNPs) located in 11,516 gene regions, were analyzed to identify shared variants of seven autoimmune/autoinflammatory diseases using the 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 functions of the identified genes. A total of 4,962 SNPs (P < 1.21 × 10-6) and 1,044 pleotropic genes (P < 4.34 × 10-6) were identified by metaCCA analysis. By screening the results of gene-based P-values, we identified the existence of 27 confirmed pleiotropic genes and highlighted 40 novel pleiotropic genes that achieved statistical significance in the metaCCA analysis and were also associated with at least one autoimmune/autoinflammatory in the VEGAS2 analysis. Using the metaCCA method, we identified novel variants associated with complex diseases incorporating different GWAS datasets. Our analysis may provide insights for the development of common therapeutic approaches for autoimmune/autoinflammatory diseases based on the pleiotropic genes and common mechanisms identified.
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Affiliation(s)
- Xiaocan Jia
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, China
| | - Nian Shi
- Department of Physical Diagnosis, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yu Feng
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, China
| | - Yifan Li
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, China
| | - Jiebing Tan
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, China
| | - Fei Xu
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, China
| | - Wei Wang
- Department of Occupational and Environmental Health, College of Public Health, Zhengzhou University, Zhengzhou, China
| | - Changqing Sun
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, China
| | - Hongwen Deng
- Center for Bioinformatics and Genomics, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, United States
| | - Yongli Yang
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, China
| | - Xuezhong Shi
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, China
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