Background/Purpose: GWAS have identified multiple genetic regions that confer risk for juvenile idiopathic arthritis (JIA).However, identifying the single nucleotide polymorphisms (SNPs) that drive disease risk has been impeded by the fact that the SNPs that identify risk loci are in linkage disequilibrium (LD) with hundreds of other SNPs. Since the causal SNPs remain unknown, it is difficult to identify target genes, and use genetic information to inform patient care. GWAS are also unable to identify the cells impacted by disease-driving variants. We have shown that disease-driving variants on JIA haplotypes are likely to impact monocytes and macrophages. We therefore used genotyping and functional data in primary human monocytes/macrophages to nominate disease-driving SNPs on JIA risk haplotypes and identify their likely target genes.

Methods: We identified JIA risk haplotypes using Immunochip data from Hinks et al (Nature Gen 2013) and the meta-analysis from McIntosh et al (Arthritis Rheum 2017), setting r² at 0.80.We used existing genotyping data from 3,939 children with JIA and 14,412 healthy controls to identify SNPs that: (1) were situated within open chromatin in multiple immune cell types and (2) were more common in children with JIA than the controls (p< 0.05).We next identified those SNPs that occured within regulatory regions. We intersected the chosen SNPs (n=846) with regions of bi-directional transcription initiation characteristic of non-coding regulatory regions detected using dREG to analyze GROseq data. Finally, we used MicroC data to identify gene promoters interacting with the regulatory regions harboring the candidate causal SNPs.

Results: From the list of n=846 SNPs, we identified 190 SNPs that overlap with dREG peaks in monocytes and126 SNPs that overlap with dREG peaks in macrophages. Of these SNPs, 101 were situated within dREG peaks in both monocytes and macrophages, suggesting that these SNPs exert their biological effects independent of the cellular activation state. MicroC data in monocytes identified 20 genes/transcripts whose promoters interact with the enhancers harboring the SNPs of interest and therefore are the likely target genes. These included genes known to regulate interferon responses (IRF1, ERAP2) and well as genes broadly associated with leukocyte activation (JAK1, PIK3CG).

Conclusion: We demonstrate that SNPs on JIA risk haplotypes that are candidate causal variants can be further screened using functional data such as GROseq. This screening process identifies a finite number of candidate causal SNPs, the majority of which are likely to exert their biological effects independent of cellular activation state in monocytes. Three dimensional chromatin data generated with MicroC identifies the genes likely to be influenced by these SNPs. These studies demonstrate the importance of investigations into the role of innate immunity in JIA.

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ACR Meeting Abstracts - https://acrabstracts.org/abstract/using-genotyping-and-functional-data-from-monocytes-to-identify-risk-driving-snps-on-jia-risk-haplotypes/