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Human Molecular Genetics Advance Access published online on March 16, 2005
Human Molecular Genetics, doi:10.1093/hmg/ddi124
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© The Author 2005. Published by Oxford University Press. All rights reserved

Article

Inferring Gene Transcriptional Modulatory Relations: A Genetical Genomics Approach

Hongqiang Li 1, Lu Lu 2, Kenneth F. Manly 3, Elissa J. Chesler 4, Lei Bao 1, Jintao Wang 5, Mi Zhou 1, Robert W. Williams 6, and Yan Cui 1*

1 Department of Molecular Sciences; Center of Genomics and Bioinformatics
2 Center of Genomics and Bioinformatics; Department of Anatomy and Neurobiology
3 Center of Genomics and Bioinformatics; Department of Anatomy and Neurobiology; Department of Pathology and Laboratory Medicine, University of Tennessee Health Science Center
4 Department of Anatomy and Neurobiology
5 Center of Genomics and Bioinformatics
6 Center of Genomics and Bioinformatics; Department of Anatomy and Neurobiology; Department of Pediatrics

* To whom correspondence should be addressed.
Yan Cui, E-mail: ycui2{at}utmem.edu


  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 approximately 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 segregates 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 explain the microarray data best.


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