Computational genetic mapping in mice: the ship has sailed.

Computational haplotype-based genetic mapping can be used to discover new biological mechanisms, disease-related pathways, and unexpected uses for existing drugs. Here we discuss the benefits and limitations of this methodology, its impact on translational medicine, and its future course.

In silico and in vitro pharmacogenetic analysis in mice.

Combining the experimental efficiency of a murine hepatic in vitro drug biotransformation system with in silico genetic analysis produces a model system that can rapidly analyze interindividual differences in drug metabolism. This model system was tested by using two clinically important drugs, testosterone and irinotecan, whose metabolism was previously well characterized. The metabolites produced after these drugs were incubated with hepatic in vitro biotransformation systems prepared from the 15 inbred mouse strains were measured. Strain-specific differences in the rate of 16 alpha-hydroxytestosterone generation and irinotecan glucuronidation correlated with the pattern of genetic variation within Cyp2b9 and Ugt1a loci, respectively. These computational predictions were experimentally confirmed using expressed recombinant enzymes. The genetic changes affecting irinotecan metabolism in mice mirrored those in humans that are known to affect the pharmacokinetics and incidence of adverse responses to this medication.

Understanding our drugs and our diseases.

Analysis of mouse genetic models of human disease-associated traits has provided important insight into the pathogenesis of human disease. As one example, analysis of a murine genetic model of osteoporosis demonstrated that genetic variation within the 15-lipoxygenase (Alox15) gene affected peak bone mass, and that treatment with inhibitors of this enzyme improved bone mass and quality in rodent models. However, the method that has been used to analyze mouse genetic models is very time consuming, inefficient, and costly. To overcome these limitations, a computational method for analysis of mouse genetic models was developed that markedly accelerates the pace of genetic discovery. It was used to identify a genetic factor affecting the rate of metabolism of warfarin, an anticoagulant that is commonly used to treat clotting disorders. Computational analysis of a murine genetic model of narcotic drug withdrawal suggested a potential new approach for treatment of narcotic drug addiction. Thus, the results derived from computational mouse genetic analysis can suggest new treatment strategies, and can provide new information about currently available medicines.

In silico pharmacogenetics of warfarin metabolism.

Pharmacogenetic approaches can be instrumental for predicting individual differences in response to a therapeutic intervention. Here we used a recently developed murine haplotype-based computational method to identify a genetic factor regulating the metabolism of warfarin, a commonly prescribed anticoagulant with a narrow therapeutic index and a large variation in individual dosing. After quantification of warfarin and nine of its metabolites in plasma from 13 inbred mouse strains, we correlated strain-specific differences in 7-hydroxywarfarin accumulation with genetic variation within a chromosomal region encoding cytochrome P450 2C (Cyp2c) enzymes. This computational prediction was experimentally confirmed by showing that the rate-limiting step in biotransformation of warfarin to its 7-hydroxylated metabolite was inhibited by tolbutamide, a Cyp2c isoform-specific substrate, and that this transformation was mediated by expressed recombinant Cyp2c29. We show that genetic variants responsible for interindividual pharmacokinetic differences in drug metabolism can be identified by computational genetic analysis in mice.

In silico genetics: identification of a functional element regulating H2-Ealpha gene expression.

Computational tools can markedly accelerate the rate at which murine genetic models can be analyzed. We developed a computational method for mapping phenotypic traits that vary among inbred strains onto haplotypic blocks. This method correctly predicted the genetic basis for strain-specific differences in several biologically important traits. It was also used to identify an allele-specific functional genomic element regulating H2-Ealpha gene expression. This functional element, which contained the binding sites for YY1 and a second transcription factor that is probably serum response factor, is located within the first intron of the H2-Ealpha gene. This computational method will greatly improve our ability to identify the genetic basis for a variety of phenotypic traits, ranging from qualitative trait information to quantitative gene expression data, which vary among inbred mouse strains.