Abstract
Bayesian network modeling is a promising approach to define and evaluate gene expression circuits in
diverse tissues and cell types under different experimental conditions. The power and practicality of this
approach can be improved by restricting the number of potential interactions among genes and by defining
causal relations before evaluating posterior probabilities for billions of networks. A newly developed genetical
genomics method that combines transcriptome profiling with complex trait analysis now provides
strong constraints on network architecture. This method detects those chromosomal intervals responsible
for differences in mRNA expression using quantitative trait locus (QTL) mapping. We have developed an
efficient Bayesian approach that exploits the genetical genomics method to focus computational effort on
the most plausible gene modulatory networks. We exploit a dense marker map for a genetic reference
population (GRP) that consists of 32 BXD strains of mice made by intercrossing two progenitor strains-
C57BL/6J and DBA/2J. These progenitors differ at 1.3 million known single nucleotide polymorphisms
(SNPs), all of which can be exploited to estimate the probability that a gene contains functional polymorphisms
that segregate within the GRP. We constructed 66 candidate networks that include all the candidate
modulator genes located in the 209 statistically significant trans-acting QTL regions. SNPs that distinguish
between the two progenitor strains were used to further winnow the list of candidate modulators. Bayesian
network was then used to identify the genetic modulatory relations that best explain the microarray data.