Juvenile idiopathic arthritis-associated genetic loci exhibit spatially constrained gene regulatory effects across multiple tissues and immune cell types

Abstract

Juvenile idiopathic arthritis (JIA) is an autoimmune, inflammatory joint disease with complex genetic etiology. Previous GWAS have found many genetic loci associated with JIA. However, the biological mechanism behind JIA remains unknown mainly because most risk loci are located in non-coding genetic regions. Interestingly, increasing evidence has found that regulatory elements in the non-coding regions can regulate the expression of distant target genes through spatial (physical) interactions. Here, we used information on the 3D genome organization (Hi-C data) to identify target genes that physically interact with SNPs within JIA risk loci. Subsequent analysis of these SNP-gene pairs using data from tissue and immune cell type-specific expression quantitative trait loci (eQTL) databases allowed the identification of risk loci that regulate the expression of their target genes. In total, we identified 59 JIA-risk loci that regulate the expression of 210 target genes across diverse tissues and immune cell types. Functional annotation of spatial eQTLs within JIA risk loci identified significant overlap with gene regulatory elements (i.e., enhancers and transcription factor binding sites). We found target genes involved in immune-related pathways such as antigen processing and presentation (e.g., ERAP2, HLA class I and II), the release of pro-inflammatory cytokines (e.g., LTBR, TYK2), proliferation and differentiation of specific immune cell types (e.g., AURKA in Th17 cells), and genes involved in physiological mechanisms related to pathological joint inflammation (e.g., LRG1 in arteries). Notably, many of the tissues where JIA-risk loci act as spatial eQTLs are not classically considered central to JIA pathology. Overall, our findings highlight the potential tissue and immune cell type-specific regulatory changes contributing to JIA pathogenesis. Future integration of our data with clinical studies can contribute to the development of improved JIA therapy.