COMPUTATIONAL APPROACHES
TO COMPLEX DISEASE BIOLOGY
Genetic analysis of murine models requires generation, phenotypic
screening, and genotyping of a large number of intercross progeny.
Even with improved genotyping tools, this laborious, expensive, and
time-consuming process has greatly limited the rate at which genetic
loci can be identified in experimental mouse or rat models. It usually
requires at least 2 yr to generate and characterize the 200–1000
intercross progeny required for genetic analysis. To accelerate this
process, a computational method that can predict linkage regions by
analysis of phenotypic data generated from inbred mouse strains was
developed (65). The computational prediction method can be used in
conjunction with databases of gene expression and phenotypic
information to markedly accelerate complex trait analysis. Although it
is at an early stage, the computational approach can eliminate many
months to years of laboratory work and reduce the time required for
QTL interval identification to milliseconds. Five years ago, at least five
scientists working for a 5-yr period would be required to carry out the
analysis of a complex trait in an experimental mouse model. Using the
computational method described in this book, one scientist could
complete the initial steps in analysis of a complex trait in an
experimental mouse model in 1 d. Experimental murine genetic models
can be analyzed using currently available genetic and genomic tools.
However, the rate can be exponentially accelerated through application
of recently developed computational tools. Databases of gene expression
information and DNA sequence polymorphisms among inbred mouse
strains enable genetically controlled, diseaserelated traits to be
computationally analyzed. Although computational methods may not
generate a complete “solution to the riddle” posed by a complex trait,
they can identify candidate genes and pathways that serve as starting
points for subsequent biological and genetic analysis. If the databases
and methods are sufficiently developed, the computationally identified
gene candidates will have a reasonable probability of contributing to
the disease-related phenotype. Almost all human complex diseases and
disease-related phenotypes can be experimentally modeled in rats and
mice. Genetic and genomic tools that enable computational analysis of
complex traits in mice are currently available (Fig. 5). Web-accessible
databases of DNA sequence polymorphisms across 21 murine strains
(see ref. 65 and http://mouseSNP.roche.com)andphenotypicinformation
(http://www.jax.org) across inbred mouse strains are enabling tools for
computational analysis of complex traits. Of course, aninvestigator can
also experimentally obtain phenotypic information among inbred
mouse strains for any trait of interest. One algorithm that utilizes
information within the mouse SNP database to computationally identify