Juvenile Idiopathic Arthritis (JIA) is a childhood onset autoimmune arthropathy characterized by inflammation of joints and other tissues. It is a debilitating disorder in which progressive joint damage and resulting growth disorders may occur. It is the most common chronic childhood rheumatic disease in the Western world. There are at least seven subtypes that comprise JIA and these subtypes clearly differ by age of onset and gender. JIA has a strong genetic influence with sibling risk ratios of approximately 20 (S=20). However, genetic influences vary by subtypes, age of onset and gender. We propose to analyze existing genome-wide association (GWA) data on 800 JIA cases and greater than 3,000 controls of European ancestry and genotyped on the Affymetrix 500K and Affymetrix 6.0 platforms for targeted hypotheses beyond the current case:control study. We hypothesize that loci contributing to the risk of JIA can be identified through contrasting and partitioning into more homogeneous subtypes of JIA and testing for the modifying effects on these loci's risks through interactions with HLA polymorphisms, age of onset and gender. We propose to identify genetic factors of JIA via 1) the use of imputation methods to test for association with JIA and subtypes for all HapMap SNPs with minor allele frequency greater than 2%;2) computing GWA analysis for age of onset, SNP-by-gender interaction, SNP-by-HLA interaction and SNP-by-SNP interactions;3) identifying regions of loss of heterozygosity and copy number variation predictive of JIA and its subtypes;and 4) computing GWA for expression quantitative trait loci (eQTL) in individuals with both Affymetrix 6.0 and existing global gene expression data. The proposal includes well powered GWA analyses for JIA, novel methods such as statistical machine learning for interaction analyses, and integration of expression and genotype data. The proposed series of analyses will assist in our long- term goal of understanding JIA, while recognizing the diversity of conditions known as JIA and making the discoveries of greater relevance for diagnosis, prognosis and treatment.
Juvenile Idiopathic Arthritis (JIA) is a childhood onset autoimmune disease characterized by inflammation of joints and other tissues. JIA is a debilitating disorder in which progressive joint damage and resulting growth disorders may occur and it is the most common chronic childhood rheumatic disease in the Western world. We will compute a series of genome-wide association analyses for JIA and its subtypes that will assist in our long-term goal of understanding JIA, while recognizing the diversity of conditions known as JIA and making the discoveries of greater relevance for diagnosis, prognosis and treatment.
