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System network analysis of genomics and transcriptomics data identified type 1 diabetes-associated pathway and genes

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Abstract

Genome-wide association studies (GWASs) have discovered >50 risk loci for type 1 diabetes (T1D). However, those variations only have modest effects on the genetic risk of T1D. In recent years, accumulated studies have suggested that gene–gene interactions might explain part of the missing heritability. The purpose of our research was to identify potential and novel risk genes for T1D by systematically considering the gene–gene interactions through network analyses. We carried out a novel system network analysis of summary GWAS statistics jointly with transcriptomic gene expression data to identify some of the missing heritability for T1D using weighted gene co-expression network analysis (WGCNA). Using WGCNA, seven modules for 1852 nominally significant (P ≤ 0.05) GWAS genes were identified by analyzing microarray data for gene expression profile. One module (tagged as green module) showed significant association (P ≤ 0.05) between the module eigengenes and the trait. This module also displayed a high correlation (r = 0.45, P ≤ 0.05) between module membership (MM) and gene significant (GS), which indicated that the green module of co-expressed genes is of significant biological importance for T1D status. By further describing the module content and topology, the green module revealed a significant enrichment in the “regulation of immune response” (GO:0050776), which is a crucially important pathway in T1D development. Our findings demonstrated a module and several core genes that act as essential components in the etiology of T1D possibly via the regulation of immune response, which may enhance our fundamental knowledge of the underlying molecular mechanisms for T1D.

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Acknowledgements

This work was partially supported or benefited by the National Institutes of Health Grants [R01 315 AR069055, U19 AG055373, R01 MH104680, R01AR059781, and P20 GM109036]; the Edward G. Schlieder Endowment fund from Tulane University; the National Natural Science Foundation of China [81302228]; the Foundation for P Pearl River Nova program of Guangzhou [2014J2200034] and the Technological Innovation Project of Foshan [2017AG100102].

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Authors and Affiliations

  1. Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou, 510630, Guangdong, PR China

    Jun-Min Lu, Yuan-Cheng Chen, Zeng-Xin Ao, Jie Shen, Chun-Ping Zeng, Xu Lin, Lin-Ping Peng, Rou Zhou, Xia-Fang Wang & Cheng Peng

  2. School of Basic Medical Sciences, Central South University, Changsha, 410000, Hunan, PR China

    Hong-Mei Xiao & Hong-Wen Deng

  3. Department of Computer Science, Bioinformatics Facility of Xavier NIH RCMI Cancer Research Center, Xavier University of Louisiana, New Orleans, LA, 70125, USA

    Kun Zhang

  4. Southern Medical University, Guangzhou, 510515, Guangdong, PR China

    Hong-Wen Deng

  5. Center for Bioinformatics and Genomics, Department of Biostatistics and Data Science, Tulane University, New Orleans, LA, 70112, USA

    Hong-Wen Deng

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  1. Jun-Min Lu
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Correspondence to Hong-Wen Deng.

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Lu, JM., Chen, YC., Ao, ZX. et al. System network analysis of genomics and transcriptomics data identified type 1 diabetes-associated pathway and genes. Genes Immun 20, 500–508 (2019). https://doi.org/10.1038/s41435-018-0045-9

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