Genome-wide association studies (GWAS) have led to an increased understanding of the genetic basis of a common autoimmune disease, rheumatoid arthritis (RA). We recently conducted a meta- analysis of three published GWAS totaling ~15,000 case-control samples of European ancestry, and found evidence for 6 additional RA risk loci - bringing the total number of validated loci to 11. Statistical modeling of the GWAS meta-analysis demonstrated that (a) less than half the genetic burden of RA can be explained by all known risk loci and (b) dozens of additional common variants of modest effect size should be identified with more powerful GWAS. We propose the following Specific Aims to increase power of existing GWAS datasets in order to identify novel RA risk loci. Specific Aim 1: Conduct the largest GWAS performed to date to search for RA susceptibility loci - in total, more than 5,000 CCP+ RA cases and 17,000 controls. Maximizing the total number of case-control samples and increasing genomic coverage of common variants (through imputation) increase power in GWAS. Through existing collaborations, we have access to published GWA data on approximately 3,000 CCP+ cases and 12,000 controls;we also have access to unpublished GWA data on an additional 2,300 CCP+ cases and more than 5,000 controls. Our study will focus on RA patients of European ancestry seropositive for cyclic citrullinated peptide antibodies (CCP+ RA) to minimize heterogeneity across sample collections. Specific Aim 2: Search for shared biological pathways implicated by our GWAS meta- analysis using novel literature mining methods developed in our laboratory. We hypothesize that true RA risk genes share common biological pathways. We will test this hypothesis by assessing the degree of similarity between genes in regions implicated by GWAS, where we define similarity using a text-based approach based on PubMed abstracts. This approach can also help prioritize SNPs of intermediate significance for future replication. Specific Aim 3: Make all GWAS results available to the public to foster research by other investigators. Results from our meta-analysis will allow other groups to explore related areas of research: replicate best results in additional RA samples;explore commonality of autoimmune disease;create genetic prediction models of RA risk;and test RA risk alleles for outcome of treatment response. Completing these Aims will provide substantial progress towards our ultimate goal of a complete understanding of all RA genetic mutations - a necessary step before genetics can be translated to clinical care. PUBLIC HEALTH RELEVANCE: A long-term goal of understanding the genetic basis of rheumatoid arthritis is to improve care of patients with this common and debilitating disease. In theory, identifying specific pieces of DNA ("alleles") should aid in diagnosing a treatable condition either prior to onset of symptoms, or early in the course of disease before bone destruction occurs. In addition, genetics should provide insight into important steps of the disease pathway, allowing for the development of new therapies that target these pathways in at-risk individuals.