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Multiomics dissection of molecular regulatory mechanisms underlying autoimmune-associated noncoding SNPs
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
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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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 2

Comparing the integrative functional SNP prioritization method with other methods.

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Comparing the integrative functional SNP prioritization method with othe...
(A) Comparison of experimentally validated functional SNPs between our method and 5 other methods from a high-throughput screen assay in mononuclear cells (35). (B–E) Comparison of percentage of annotated SNPs with different regulatory evidence between our method and 5 other methods, including (B) potential regulatory SNPs with predicted target genes by combining cis-QTL, chromatin interaction, and colocalization analysis (see Methods for detailed information), (C) potential functional SNPs with significant molecular QTL association on multiple blood immune cell types (Supplemental Table 3), (D) causal autoimmune- associated SNPs identified by PICS approach, and (E) SNPs annotated with enhancer RNA (eRNA). Fisher’s exact test was performed in C–E with the asterisk representing significant higher enrichment on our method (FC > 1). *P < 0.05, **P < 0.005. NS, not significant; PICS, Probabilistic Identification of Causal SNPs.

Copyright © 2026 American Society for Clinical Investigation
ISSN 2379-3708

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