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Multiomics dissection of molecular regulatory mechanisms underlying autoimmune-associated noncoding SNPs
Xiao-Feng Chen, … , Yan Guo, Tie-Lin Yang
Xiao-Feng Chen, … , Yan Guo, Tie-Lin Yang
Published September 3, 2020
Citation Information: JCI Insight. 2020;5(17):e136477. https://doi.org/10.1172/jci.insight.136477.
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Research Article Genetics

Multiomics dissection of molecular regulatory mechanisms underlying autoimmune-associated noncoding SNPs

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Abstract

More than 90% of autoimmune-associated variants are located in noncoding regions, leading to challenges in deciphering the underlying causal roles of functional variants and genes and biological mechanisms. Therefore, to reduce the gap between traditional genetic findings and mechanistic understanding of disease etiologies and clinical drug development, it is important to translate systematically the regulatory mechanisms underlying noncoding variants. Here, we prioritized functional noncoding SNPs with regulatory gene targets associated with 19 autoimmune diseases by incorporating hundreds of immune cell–specific multiomics data. The prioritized SNPs are associated with transcription factor (TF) binding, histone modification, or chromatin accessibility, indicating their allele-specific regulatory roles. Their target genes are significantly enriched in immunologically related pathways and other known immunologically related functions. We found that 90.1% of target genes are regulated by distal SNPs involving several TFs (e.g., the DNA-binding protein CCCTC-binding factor [CTCF]), suggesting the importance of long-range chromatin interaction in autoimmune diseases. Moreover, we predicted potential drug targets for autoimmune diseases, including 2 genes (NFKB1 and SH2B3) with known drug indications on other diseases, highlighting their potential drug repurposing opportunities. Taken together, these findings may provide useful information for future experimental follow-up and drug applications on autoimmune diseases.

Authors

Xiao-Feng Chen, Ming-Rui Guo, Yuan-Yuan Duan, Feng Jiang, Hao Wu, Shan-Shan Dong, Xiao-Rong Zhou, Hlaing Nwe Thynn, Cong-Cong Liu, Lin Zhang, Yan Guo, Tie-Lin Yang

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Figure 7

Drug implications analysis on predicted target genes.

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Drug implications analysis on predicted target genes.
(A) Pie chart show...
(A) Pie chart showing percentage of predicted target genes for either known drug target genes or predicted druggable genes or others. (B) Venn diagram showing sharing of drug target genes with indications on either autoimmune diseases, other immunologically related diseases or other diseases. See Supplemental Table 14 for detailed indication information and classification of these 3 disease types. (C) Functional enrichment analysis for either known drug target or predicted druggable genes on our predicted target genes compared with all genome genes using Fisher’s exact test. (D) PPI between autoimmune-drug target genes (marked in red) and other drug target or druggable genes. PPI plot was queried online from STRING database (score > 0.9). (E) Functional enrichment analysis showing percentage of genes with strong PPI (score > 0.9) with autoimmune-drug target genes on either predicted druggable genes or known drug target genes. The comparison was performed between predicted target genes (green) and all druggable or drug target genes (orange), as well as between all druggable genes or drug targets and all genome genes (blue) using Fisher’s exact test.

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ISSN 2379-3708

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