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Roche Scientists Develop Predictive Method to Identify Disease Regions of Mouse Genome

NEW YORK, June 7 - Roche scientists said Thursday that a new computational approach they have developed was applied successfully to identify regions of the mouse genome responsible for susceptibility to disease traits. 

The new approach, described in the June 8 issue of Science , combines a web-accessible mouse SNP database compiled by the researchers ( ) with a proprietary linkage prediction algorithm to map phenotypic traits onto the database. 

The program scans the database, which contains allele information across 15 inbred mouse strains and genotyping assays for over 500 SNPs, and uses known inbred strain phenotypes to predict the chromosomal regions that most likely contribute to complex traits. 

Current experimental analyses of mouse genetic models of human disease can take up to two years, according to Gary Peltz, head of genetics in the inflammatory diseases unit at Roche Bioscience in Palo Alto, Calif., and an author of the paper. Peltz said the Roche method reduces the time required for performing this type of genetic analysis to milliseconds. 

The accuracy of the predictive technique was verified against experimentally verified quantitative trait regions for 10 phenotypic traits. The method was able to correctly predict 19 of 26 experimentally verified regions, including the chromosomal location of the major histocompatibility complex, which has been mapped to chromosome 17 on the mouse genome, as well as chromosomal regions that regulate susceptibility to experimental allergic asthma. 

This new methodology is already being applied by Roche scientists in the area of clinical diagnostics research. Peltz said the SNP database is freely available, while Roche is still determining the best means of distribution for the computer program. 

The research was supported in part by a $1.2 million, three-year grant from the National Human Genome Research Institute of the National Institutes of Health.

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