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Arthritis Models: Using CIA and CAIA to Study RA GWAS Risk Loci

The development of rheumatoid arthritis (RA) may rely on a complex, and not fully understood, network of environmental and genetic factors that collectively contribute to autoimmunity directed against collagen. Several environmental factors, most notably smoking, have been associated with modulating RA progression (1). Twin studies have resulted in vastly different estimates of the influence of genetic factors on RA susceptibility, with estimates of heritability ranging from 12% (2) to 60% (3). Of these genetics factors, the HLA-DRB1 gene is perhaps the most important and well-known link. The HLA-DRB1 gene is part of the major histocompatibility complex gene family, which codes for proteins responsible for antigen-presentation to T cells during adaptive immune responses. Certain HLA-DRB1 subtypes, namely the DR4 subtype, have been associated with RA and anti-collagen autoantibody production (4,5). Since this linkage was first established in the 1970s (6), a plethora of other genes have been associated with RA through the use of Genome-Wide Association Studies (GWASs).

GWASs compare the genomes of two populations (disease vs. non-disease) by screening individuals in each population for 500,000 - 1 million single nucleotide polymorphisms (SNPs) throughout the entire genome. The SNPs identified in each population are then compared to determine which SNPs are enriched in the disease population as compared to the control population. SNPs identified this way are termed risk loci and are candidates for further research as to their potential roles in disease pathogenesis. In the case of RA, numerous GWASs have been performed (see list of RA GWASs), identifying over 100 risk loci (7). 

Interestingly, a significant proportion of the disease-associated SNPs identified through GWASs are not located within protein-coding regions of DNA. Rather these genetic variants, called expression quantitative trait loci (eQTLs), are clustered in regions associated with changes in gene expression (8). In the case of RA, 44% of risk loci are identified as eQTLs (9). Many RA associated eQTLs have also been identified in GWASs of other autoimmune diseases, indicating that autoimmune diseases have similar basic genetic contributions (9). The future direction of autoimmune disease research relies on the formulation of a mechanism based on genomic data and validation of proposed mechanisms using a well-characterized disease model. 

Discerning the roles of genetic factors in humans is virtually impossible, therefore models using genetically modified mice could provide key insight into how eQTLs and other risk loci contribute to disease pathology. For studying RA pathologies, the mouse Collagen-Induced Arthritis (CIA) model and the Collagen Antibody-Induced Arthritis (CAIA) model are valuable tools. The CAIA model allows for arthritis induction in a wide variety of mouse strains, including Balb/c, C57BL/6, and mixed genetic background mice. The CAIA model bypasses the antigen recognition and antibody production process of the adaptive immune response by injecting a cocktail of anti-type II collagen antibodies, leading to complement activation and severe arthritis. Alternatively, if a candidate gene functions during antigen recognition and antibody generation stages, the CIA model is a more suitable model for studying these genes in the context of immune responses. In fact, using both models concurrently could allow researchers to parse the genetic mechanisms of rheumatoid arthritis development as one would act as a negative control for the other. 

Both the CAIA and CIA models well characterized and highly reproducible. If you would like to learn more about these models, please see our product page comparing these two models. If you have any questions about how the CAIA and CIA model could be utilized to study how RA risk loci affect disease phenotype, please contact us



  1. K. Liao, L. Alfredsson, E. Karlson, Environmental influences on risk for rheumatoid arthritis. Curr Opin Rheumatol. 21, 279-283 (2009).
  2. A. Svendsen et al., On the origin of rheumatoid arthritis: the impact of environment and genes - a population based twin study. PLoS ONE 8,  (2013).
  3. A. MacGregor et al., Characterizing the quantitative genetic contribution to rheumatoid arthritis using data from twins. Arthritis & Rheumatism 43, 30-37 (2001).
  4. L. Fugger, A. Svejgaard, Association of MHC and rheumatoid arthritis: HLA-DR4 and rheumatoid arthritis - studies in mice and men. Arthritis Research 2, 208-211 (2000).
  5. M. Rowley, B. Tait, I. Mackay, T. Cunningham, B. Phillips, Collagen antibodies in rheumatoid arthritis. Significance of antibodies to denatured collagen and their associated with HLA-DR4. Arthritis & Rheumatology 29, 174-184 (1986).
  6. K. McAllister, S. Eyre, G. Orozco, Genetics of rheumatoid arthritis: GWAS and beyond. Open Access Rheumatology 3, 31-46 (2011).
  7. Y. Okada et al., Genetics of rheumatoid arthritis contributes to biology and drug discovery. Nature 506, 376-381 (2014).
  8. A. Walsh et al., Integrative genomic deconvoluton of rheumatoid arthritis GWAS loci into gene and cell type associations. Genome Biology 17,  (2016).
  9. Y. Kochi et al., Genetics of autoimmune diseases: perspectives from genome-wise association studies. International Immunology 28, 155-161 (2016).