ABSTRACT There is limited pathogenic understanding and no cure for autoimmune diseases such as rheumatoid arthritis (RA). Genome wide association studies (GWAS) have identified ~200 RA-associated loci. These loci represent ~4573 genetic variants, which in most cases is a single nucleotide polymorphism (SNP) in linkage disequilibrium (LD). In theory, there is only one functional SNP (fSNP) in each LD that is responsible for the pathogenesis of RA. However, GWAS don't reveal which one is the fSNP in each LD. This technical drawback leaves a gap between GWAS and a specific mechanism that would provide into opportunities for biological insight and therapeutic intervention. To overcome this limitation, we have developed two novel techniques: functional Single Nucleotide Polymorphism-next generation sequencing (fSNP-seq) and DNA competition pulldown-mass spectrometry (DCP-MS). fSNP-seq is a high throughput method to identify experimentally which SNPs are likely to bind regulatory proteins and thus to likely be fSNPs. DCP-MS uses an fSNP sequence as ?bait? to identify associated regulatory proteins in a semi-high throughput way. Using these techniques in a pilot screen, we have identified three fSNPs on a RA-associated CD40 locus that have been confirmed by EMSA and an allele-specific luciferase reporter assay. We have also identified four proteins that regulate CD40 expression via these fSNPs. On the basis of these preliminary data, we propose two aims to apply our new methods to the GWAS data on RA. First, we will use fSNP-seq to screen 1218 SNPs for fSNPs on 101 RA risk loci revealed by a recent study. However, due to the high level of effort involved in this process, we will limit the identification and characterization of fSNPs to only seven RA risk loci involved in the CD40/NF-kB pathway. Second, we will employ DCP-MS to screen for the RA risk gene regulators on the validated fSNPs in these seven RA risk loci in the CD40/NF-kB pathway. This methodology could lead to building a sustainable, long-term research program t o apply this strategy to the entire RA loci. The long-term goal would be to identify the best drug targets for developing personalized drugs in a context of the entire RA-associated risk gene regulation network.