Abstract We aim to move the field of genetics as applied to juvenile idiopathic arthritis (JIA) away from the identification of genetic associations and toward a mechanistic understanding of how genetic variants exert risk-conferring effects. We will accomplish two major tasks now facing the field: (1) identification of the variants that exert the biological effects that confer risk (the “causal variants”); (2) identification of the genes whose expression levels are altered by those variants (the “target genes”). In accomplishing these aims, we will also elucidate mechanisms through which those variants alter gene expression and cellular functions. One of the striking findings from GWAS for many complex traits, including rheumatic diseases such as JIA, is the frequency with which disease-associated genetic variants appear in the non-coding genome. As in other complex traits, the JIA genetic risk loci are highly enriched for H3K4me1/H3K27histone marks, epigenetic signatures frequently associated with enhancer function. This finding has led to the hypothesis that genetic risk in JIA impinges on enhancer function, leading to transcriptional abnormalities that can be observed in peripheral blood cells. In this application, we focus on CD4+ T cells, which our preliminary data suggest are among the cells likely to be impacted by causal genetic variants in JIA. In Aim 1, we will identify causal variants based on distinct biological properties. We will identify histone quantitative trait loci (hQTLs) in CD4+ T cells of children with JIA, i.e., regions where genetic variants are associated with differences in read depth on H3K4me1/H3K27ac Cut-and-Run sequencing. We will use the same approach as that previously used by our co-investigator, Dr. Gaffney, in his investigations into the genetics of systemic lupus. We will then identify the variants within the hQTLs that alter DNA topology, a critical determinant of regulatory function. Finally, from variants that pass both screens, we will use a massively parallel reporter assay (MPRA) to identify those variants within the hQTLs that have a significant influence on gene expression. In Aim 2, we will identify the target genes within the JIA risk haplotypes. The underlying premise of these studies is that, although the causal variants may not impact the nearest gene, the majority of relevant interactions will occur within the same topologically associated domains (TADs). Using Cut-and-Run data that we generate in Aim 1 as well as H3K27ac HiChIP data and supplemented by our published CTCF ChIPseq/HiChIP data, we will identify interactions between H3K27ac-marked regions on the risk haplotypes and gene promoters, focusing on those within CTCF-anchored TADs. Knowledge of the 3D chromatin structure, patient genotype, and RNAseq data will then allow us to identify the likely target genes of variants on the risk haplotypes.