Rat models are frequently used for finding genes contributing to the arthritis phenotype. In most studies, however, limitations in the number of animals result in a low resolution. As a result, the linkage between the autoimmune experimental arthritis phenotype and the genomic region, that is, the quantitative trait locus, can cover several hundred genes. The purpose of this work was to facilitate the search for candidate genes in such regions by introducing a web tool called Candidate Gene Capture (CGC) that takes advantage of free text data on gene function. The CGC tool was developed by combining genomic regions in the rat, associated with the autoimmune experimental arthritis phenotype, with rat/human gene homology data, and with descriptions of phenotypic gene effects and selected keywords. Each keyword was assigned a value, which was used for ranking genes based on their description of phenotypic gene effects. The application was implemented as a web-based tool and made public at http://ratmap.org/cgc . The CGC application ranks gene candidates for 37 rat genomic regions associated with autoimmune experimental arthritis phenotypes. To evaluate the CGC tool, the gene ranking in four regions was compared with an independent manual evaluation. In these sample tests, there was a full agreement between the manual ranking and the CGC ranking for the four highest-ranked genes in each test, except for one single gene. This indicates that the CGC tool creates a ranking very similar to that made by human inspection. The exceptional gene, which was ranked as a gene candidate by the CGC tool but not in the manual evaluation, was found to be closely associated with rheumatoid arthritis in additional literature studies. Genes ranked by the CGC tools as less likely gene candidates, as well as genes ranked low, were generally rated in a similar manner to those done manually. Thus, to find genes contributing to experimentally induced arthritis, we consider the CGC application to be a helpful tool in facilitating the evaluation of large amounts of textual information.

BACKGROUND:Rat models are frequently used to find genomic regions that contribute to complex diseases, so called quantitative trait loci (QTLs). In general, the genomic regions found to be associated with a quantitative trait are rather large, covering hundreds of genes. To help selecting appropriate candidate genes from QTLs associated with type 2 diabetes models in rat, we have developed a web tool called Candidate Gene Capture (CGC), specifically adopted for this disorder.METHODS:CGC combines diabetes-related genomic regions in rat with rat/human homology data, textual descriptions of gene effects and an array of 789 keywords. Each keyword is assigned values that reflect its co-occurrence with 24 different reference terms describing sub-phenotypes of type 2 diabetes (for example "insulin resistance"). The genes are then ranked based on the occurrences of keywords in the describing texts.RESULTS:CGC includes QTLs from type 2 diabetes models in rat. When comparing gene rankings from CGC based on one sub-phenotype, with manual gene ratings for four QTLs, very similar results were obtained. In total, 24 different sub-phenotypes are available as reference terms in the application and based on differences in gene ranking, they fall into separate clusters.CONCLUSION:The very good agreement between the CGC gene ranking and the manual rating confirms that CGC is as a reliable tool for interpreting textual information. This, together with the possibility to select many different sub-phenotypes, makes CGC a versatile tool for finding candidate genes. CGC is publicly available at http://ratmap.org/CGC.

The inbred BDII rat is a valuable experimental model for the genetic analysis of endometrial adenocarcinoma (EAC). One common aberration detected by comparative genomic hybridization in rat EAC was gain/amplification affecting the proximal part of rat chromosome 6 (RNO6). We applied rat and mouse chromosome painting probes onto tumor cell metaphase preparations in order to detect and characterize gross RNO6 aberrations. In addition, the RNO6q11–q16 segment was analyzed by fluorescence in situ hybridization with probes representing 12 cancer‐related genes in the region. The analysis revealed that seven tumors contained large RNO6‐derived homogeneously staining regions (HSRs) in addition to several normal or near‐normal RNO6 chromosomes. Five tumors (two of which also had HSRs) exhibited a selective increase of the RNO6q11–q16 segment, sometimes in conjunction with moderate amplification of one or a few genes. Most commonly, the amplification affected the region centered around band 6q16 and included the Mycn, Ddx1, and Rrm2 genes. A second region, centering around Slc8a1 and Xdh, also was affected by gene amplification but to a lesser extent. The aberrations in the proximal part of RNO6 were further analyzed using allelotyping of microsatellite markers in all tumors from animals that were heterozygous in the proximal RNO6 region. We could detect allelic imbalance (AI) in 12 of 20 informative tumors, 6 of which were in addition to those already analyzed by molecular cytogenetic methods as described. Our findings suggest that increase/amplification of genes in this chromosome region contribute to the development of this hormone‐dependent tumor. © 2005 Wiley‐Liss, Inc.

The rat genome database RatMap (http://ratmap.org or http://ratmap.gen.gu.se) has been one of the main resources for rat genome information since 1994. The database is maintained by CMB-Genetics at Goteborg University in Sweden and provides information on rat genes, polymorphic rat DNA-markers and rat quantitative trait loci (QTLs), all curated at RatMap. The database is under the supervision of the Rat Gene and Nomenclature Committee (RGNC); thus much attention is paid to rat gene nomenclature. RatMap presents information on rat idiograms, karyotypes and provides a unified presentation of the rat genome sequence and integrated rat linkage maps. A set of tools is also available to facilitate the identification and characterization of rat QTLs, as well as the estimation of exon/intron number and sizes in individual rat genes. Furthermore, comparative gene maps of rat in regard to mouse and human are provided.

Published quantitative trait locus (QTL) data, as well as all known rat genes and DNA markers, have since 1993 been collected and made easily accessible at the rat genome database, RatMap. The objective of the present study is to fully integrate available data concerning rat models with human genome information. The final goal of this process is to make results from any rat model experiment directly applicable to humans. The overall goal of this work is to create an automatic system which, for any given rat chromosomal region associated with a QTL, will characterize both mapped rat genes and all putative homologous human genes in that region. This article reports the use of the web application to find human gene candidates contributing to an arthritis phenotype.