Inferring gene transcriptional modulatory relations: a genetical genomics approach

• Published in: Human molecular genetics 2005-05, Vol.14 (9), p.1119-1125
• Main Authors: Li, Hongqiang, Lu, Lu, Manly, Kenneth F., Chesler, Elissa J., Bao, Lei, Wang, Jintao, Zhou, Mi, Williams, Robert W., Cui, Yan
• Format: Article
• Language: English
• Subjects: Algorithms; Animals; Bayes Theorem; Biological and medical sciences; Chromosome Mapping; Computational Biology; Crosses, Genetic; Female; Fundamental and applied biological sciences. Psychology; Gene Expression Profiling; Gene Targeting; Genetic Markers; Genetics of eukaryotes. Biological and molecular evolution; Genetics, Population; Genomics - methods; Male; Mice; Mice, Inbred C57BL; Mice, Inbred DBA; Models, Genetic; Molecular and cellular biology; Molecular genetics; Oligonucleotide Array Sequence Analysis; Polymorphism, Single Nucleotide; Quantitative Trait Loci; Quantitative Trait, Heritable; Transcription, Genetic; Transcription. Transcription factor. Splicing. Rna processing
• ISSN: 0964-6906; 1460-2083
• DOI: 10.1093/hmg/ddi124

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.