Computational Biology 25 chromosomal regions regulating susceptibility or resistance to diseaserelated traits was described ( 65 ). After entry of phenotypic information obtained from inbred mouse strains, the phenotypic and genotypic information is computationally analyzed by this method to predict the chromosomal regions regulating the phenotypic trait. The computational predictions were evaluated using 10 experimentally verified QTL intervals from seven phenotypic traits analyzed as true positives. There was a statistically highly significant level of concordance between the predicted and experimentally verified intervals ( 65 ). Several factors contributed to the quality of the computational predictions. The use of inbred mouse strains housed under the same conditions minimizes environmental variability, and timed experimental intervention and multiple sampling limit the experimental error in phenotypic assessment. The inbred strains are homozygous at all loci, which eliminates confounding effects owing to heterozygosity found in human populations. We do not yet fully understand the limitations of the computational prediction method nor do we know the optimal inbred strains to use for generating the phenotypic data. Clearly, the method may not provide the correct predictions for all traits studied. However, it has the exciting potential of drastically reducing the time required for identifying chromosomal regions with genetic loci regulating complex disease-associated traits.
Recently, we have produced another computational genetic mapping method that utilizes a significantly different methodology ( 84 ). This new method has increased statistical validity and makes predictions that are more precise. As discussed in subsequent chapters, there is a large amount of linkage disequilibrium among the polymorphisms found among the inbred mouse strains ( 26 ). This linkage disequilibrium means that the pattern of genetic variation across regions of the genome can be characterized by knowing the alleles at a relatively few positions within each region. Among the inbred Mus musculus strains, each discrete region contains a relatively small number of distinct genetic patterns. This drastically reduces the number of comparisons required for computational genetic analysis. Instead of comparing a phenotypic pattern with individual SNP alleles, we have developed a new method that compares the observed phenotypic data with different haplotypes that extend across larger genomic regions. Once the haplotypic structure of the mouse genome is understood, it facilitates computational mapping of genetic traits. The haplotype-based method utilizes the same principle of finding patterns of genetic variation that correlate with phenotypic differences among the strains as was used by
the digital disease method. However, the haplotype-based computational mapping method is radically different; it is based on a highly quantitative model. As described, this method has been successfully utilized to map the genetic loci for previously known traits, as well as for discovering a novel functional genomic