Ranking candidate genes in rat models of type 2 diabetes

• Main Authors: Ståhl Fredrik, Petersen Greta, Andersson Lars
• Format: Article
• Language: English
• Published: BMC 2009-07-01
• Series: Theoretical Biology and Medical Modelling
• Online Access: http://www.tbiomed.com/content/6/1/12

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Methods</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusion</p> <p>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 <url>http://ratmap.org/CGC</url>.</p>