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Sudman, M.

Publications and source records attributed to Sudman, M..

2 recordsLinked to original sources

Using Functional Genomic Data in Monocytes/Macrophages and Genotyping to Nominate Disease-Driving Single Nucleotide Polymorphisms and Target Genes in Juvenile Idiopathic Arthritis

IntroductionGWAS have identified multiple regions that confer risk for juvenile idiopathic arthritis (JIA). However, identifying the single nucleotide polymorphisms (SNPs) that drive disease risk is impeded by the SNPs that identify risk loci being 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. We 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. MethodsWe 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). We used 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 intersected the chosen SNPs (n=846) with regions of bi-directional transcription initiation characteristic of non-coding regulatory regions detected using dREG to analyze GRO-seq data. Finally, we used MicroC data to identify gene promoters interacting with the regulatory regions harboring the candidate causal SNPs. ResultsWe 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 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. ConclusionSNPs in JIA risk regions that are candidate causal variants can be further screened using functional data such as GRO-seq. This 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 genes likely to be influenced by these SNPs. These studies demonstrate the importance of investigations into the role of innate immunity in JIA.

genetics↗

Identification of a regulatory pathway governing TRAF1 via an arthritis-associated non-coding variant

TRAF1/C5 was among the first loci shown to confer risk for inflammatory arthritis in the absence of an associated coding variant, but its genetic mechanism remains undefined. Using ImmunoChip data from 3,939 juvenile idiopathic arthritis (JIA) patients and 14,412 controls, we identified 132 plausible common non-coding variants, reduced serially by SNP-seq, electrophoretic mobility shift, and luciferase studies to the single variant rs7034653 in the third intron of TRAF1. Genetically manipulated experimental cells and primary monocytes from genotyped donors establish that the risk G allele reduces binding of Fos-Related Antigen 2 (FRA2), resulting in reduced TRAF1 expression and enhanced TNF production. Conditioning on this variant eliminates attributable risk for rheumatoid arthritis, implicating a mechanism shared across the arthritis spectrum. These findings reveal that rs7034653, FRA2, and TRAF1 mediate a pathway through which a non-coding causal variant drives risk of inflammatory arthritis in children and adults.

genetics↗