Integrative Analysis of Genomic Risk Factors in Juvenile Idiopathic Arthritis

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DESCRIPTION (provided by applicant): Juvenile Idiopathic Arthritis (JIA), afflicting ~1 in 10,000 North American Children, encompasses a highly heterogeneous group of chronic, immune-mediated joint disorders. Aside from causing severe pain, tissue damage, and physical immobility, chronic disease activity leads to permanent short stature and limb disfigurement. Similar to a number of other complex polygenic autoimmune diseases, there is a clear heritable component to JIA, but all known genetic risk factors explain less than 20% of disease heritability. The majority of this is attributable to variation across the Major Histocompatibility (MHC) locus, yet definitive high-resolution identification of MHC associations in JIA have not been conclusive. JIA is currently classified into seven clinical subtypes, based on a classification schema that has little research or clinical utility, since 10-50% of cases are classified as "undifferentiated". Biological and molecular evidence based on the presence of serum autoantibodies and other molecular biomarkers of immunological defects suggest that the existing seven JIA subtypes could be grouped into two major classes-subtypes that represent either predominantly seropositive autoimmune (AID) diseases or seronegative autoinflammatory (AIF) diseases. Whether genetic risk factors differ for AID versus AIF-like JIA subtypes is unknown, especially since the traditional approach to genetic association studies is poorly powered to identify loci with subtype- specific effects in the presence of significant phenotypic heterogeneity. To test the hypothesis that distinct genetic risk factors underlie the clinically observed differences in AID versus AIF-like JIA subtypes and identify both shared and subtype-distinct genetic susceptibility loci, I propose to use a subtype-sensitive GWAS to identify JIA disease subtype-specific genetics associations, evaluate evidence for functional HLA associations that are distinct or shared across JIA subtypes, and test if the identified genetic associations can be used to predict disease susceptibility, distinguish patients who belong to AID versus AIF-like JIA classes, and reclassify patients currently defined as having undifferentiated disease. The analysis approach proposed here integrates genomic screening, disease modeling and prediction, and the use of expression and functional data resources to better understand the etiology of JIA. The successful completion of this work will likely yield novel biomolecular or pathway-based targets for the development of therapeutic agents for children with JIA and patients with other related rheumatological or immunological diseases.