%0 Journal Article
%T A web tool for finding gene candidates associated with experimentally induced arthritis in the rat
%A Lars Andersson
%A Greta Petersen
%A Per Johnson
%A Fredrik St£¿hl
%J Arthritis Research & Therapy
%D 2005
%I BioMed Central
%R 10.1186/ar1700
%X Rheumatoid arthritis (RA) is an autoimmune disease characterised by chronic inflammation of the joints. The prevalence of RA is 0.5 to 1% in many populations [1] and is about 2.5 times higher in women [2]. RA has a very complex genetic basis, and the combination of genetic and environmental causative factors makes it hard to study. The genetic contribution to RA susceptibility is estimated to be between 30% and 50%, of which the major histocompatibility complex accounts for about one-third [3].Animal models provide a valuable tool for finding genes contributing to the susceptibility to and severity of RA. Rats are very useful for this purpose because autoimmune experimental arthritis phenotypes can be induced in susceptible strains by several agents, such as collagen, pristane, oil, streptococcal cell wall and even adjuvant alone [4-6]. Intercrosses of such susceptible rat strains with resistant strains are used for establishing linkage between genetic markers and quantitative traits distinguishing the arthritis phenotype. Statistically valid linkage between such genomic regions and measurements of quantitative traits are called quantitative trait loci (QTLs). More than 40 QTLs that regulate experimentally induced arthritis have been identified in different rat crosses [7]. Most of these QTLs are several megabases in size, containing many possible gene candidates. Several experimental strategies are used to narrow these regions, and these attempts almost always are combined with the retrieval of potential candidate genes found in different databases.Information about RA and related genome data is available in several different forms, from raw data to descriptive text. One important difference between raw data and data based on human evaluation is that human evaluation often yields an interpretation that gives meaning to the data. Thus, human considerations bring an added value to genome data, which makes textual description an important source for investigating gene
%U http://arthritis-research.com/content/7/3/R485