The reconstruction of genetic networks in mammalian systems is one of the primary goals in biological research, especially as such reconstructions relate to elucidating not only common, polygenic human diseases, but living systems more generally. Here we propose a novel gene network reconstruction algorithm, derived from classic Bayesian network methods, that utilizes naturally occurring genetic variations as a source of perturbations to elucidate the network. This algorithm incorporates relative transcript abundance and genotypic data from segregating populations by employing a generalized scoring function of maximum likelihood commonly used in Bayesian network reconstruction problems. The utility of this novel algorithm is demonstrated via application to liver gene expression data from a segregating mouse population. We demonstrate that the network derived from these data using our novel network reconstruction algorithm is able to capture causal associations between genes that result in increased predictive power, compared to more classically reconstructed networks derived from the same data.
An integrative genomics approach to the reconstruction of gene networks in segregating populations
Zhu, J., Lum, P. Y., Lamb, J., GuhaThakurta, D., Edwards, S. W., Thieringer, R., Berger, J. P., Wu, M. S., Thompson, J., Sachs, A. B., & Schadt, E. E. (2004). An integrative genomics approach to the reconstruction of gene networks in segregating populations. Cytogenetic and Genome Research, 105(2-4), 363-374. https://doi.org/10.1159/000078209
Abstract
Publications Info
To contact an RTI author, request a report, or for additional information about publications by our experts, send us your request.
Recent Publications
OCCASIONAL PAPER
OCCASIONAL PAPER
Culturally informed community engagement
Article
Does the relationship between alcohol retail environment and alcohol outcomes vary by depressive symptoms? Findings from a US Survey of Black, Hispanic and White drinkers
Article