Psoriasis is a common, immune-mediated, inflammatory skin disease associated with arthritis and systemic inflammation. Genome-wide association studies of psoriasis have identified over 40 susceptibility loci implicating both innat and adaptive immune pathways. The genetic locus exhibiting the greatest association with psoriasis is the MHC, with HLAC:06:02 showing very strong association in multiple ethnic groups. However, significant residual genetic signal remains in the MHC after accounting for the effects of HLAC:06:02 and it is not clear whether the residual signal resides in other HLA alleles, additional nearby genes, or regulatory variants. There is also evidence to suggest psoriasis susceptibility may be influenced by the interaction of killer cell immunoglobulin-like receptors (KIR) with HLA class I ligands; however, studies to date of HLA-KIR in psoriasis have generally been hampered by the time-consuming nature and high cost of HLA and KIR typing. Finally, although HLAC:06:02 is known to associate significantly with psoriasis and HLAC:06:02-positive psoriasis patients show differences in disease onset, severity, and response to therapy compared to HLAC:06:02-negative psoriasis patients, the mechanism by which HLAC:06:02 contributes to psoriasis has not been well studied. In this application, we utilize a variety of innovative approaches to address these questions. First, we perform high fidelity next-generation sequencing of the extended MHC in a psoriasis reference cohort and impute identified variants into European and Asian GWAS cohorts totaling over 8,500 cases and 17,000 controls. We construct a comprehensive genetic model to best explain the genetic association within the MHC using multiple model inputs including 4-digit resolution calls for 26 HLA/MICA/MICB/TAP genes, SNPs, indels, and functional variables. Second, we optimize a novel KIR imputation method that uses SNP array data to accurately call KIR copy number and apply this method to examine HLA-KIR associations in psoriasis using a large cohort. Finally, we utilize a novel flow cytometry protocol to sort psoriasis-relevant cell populations from the ski of HLAC:06:02-positive and HLAC:06:02- negative psoriasis patients, perform whole transcriptome RNA-sequencing on individual cell populations, and use systems biology analytical approaches to better understand the contribution of HLAC:06:02 to psoriasis pathogenesis. Together, the proposed work will identify causal variants in psoriasis, shed light on functional mechanisms, and potentially identify novel therapeutic pathways. The approaches and tools developed here will greatly facilitate the study of other immune-mediated diseases.
Psoriasis is a debilitating, inflammatory skin disease that affects over 7 million Americans. Although genetic studies of psoriasis have thus far identified important genetic signals within the MHC gene region, identifying the precise MHC gene variants that cause psoriasis has remained elusive. In this proposal, we use large genetic datasets, novel bioinformatics methods, and cutting-edge technologies to identify and characterize psoriasis variants within the MHC, which may lead to new psoriasis therapies.
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