Roche researchers have developed a two-pronged computational approach that has the potential to be a “killer app” for predicting regions of the mouse genome responsible for susceptibility to disease traits, according to Gary Peltz, head of genetics in the inflammatory diseases unit at Roche Bioscience in Palo Alto, Calif.
The new approach, described in the June 8 issue of Science, combines a web-accessible mouse SNP database compiled by the researchers by direct sequencing of defined chromosomal locations with a proprietary linkage prediction algorithm to map phenotypic traits onto the database.
The program scans the database, which contains the location of about 3,400 SNPs spread across all 19 mouse chromosomes, and uses known inbred strain phenotypes to predict the chromosomal regions that most likely contribute to complex traits.
Conventional experimental analyses of mouse genetic models of human disease require generating thousands of progeny of mice to characterize their phenotypes, which can take up to two years, Peltz said. Using the new method, which the Roche scientists have dubbed Digital Disease, reduces the time required for performing this type of genetic analysis to milliseconds.
“The idea is we could look at strains of mice that are readily available from commercial suppliers, characterize whatever trait it is we’re interested in, and use that phenotypic information about the trait,” Peltz explained. “The computer maps those phenotypic differences onto our database. It says what are the regions of the genome that likely account for this pattern of phenotype seen in these mouse strains.”
By identifying regions of similarity or difference, the program can predict the likely regions that could contribute to a trait, Peltz said.
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 (MHC), which has been mapped to chromosome 17 on the mouse genome, as well as regions that regulate susceptibility to experimental allergic asthma and regions that define physical traits.
Roche scientists are already using the new methodology in clinical diagnostics research. Peltz said the approach could be used on other organisms down the road but would be limited to the mouse until other SNP databases are compiled. “I do think in the near future we probably will move into other species,” Peltz said.
Digital Disease is not able to identify specific genes candidates, but it can save substantial time by narrowing down an initial search to about 10 percent of the mouse genome. The researchers are currently testing the program to see how it performs under a wide range of conditions and are in the process of continuing to improve it, Peltz said.
“This is a completely different way of looking at genetics,” Peltz said. “When you do a conventional genetic analysis, you’re always comparing one strain to the other and then you come up with the regions. However, in this case, you’re taking advantage of all the available genetic diversity. The computer is running comparisons for whatever mice that you have phenotypic information for.”
The SNP database is freely available (http://mouseSNP.roche.com), while Roche is still determining the best means of distribution for the Digital Disease 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.