An integrative network approach to map the transcriptome to the phenome.
Although many studies have been successful in the discovery of cooperating groups of genes, mapping these groups to phenotypes has proved a much more challenging task. In this article, we present the first genome-wide mapping of gene coexpression modules onto the phenome. We annotated coexpression networks from 136 microarray datasets with phenotypes from the Unified Medical Language System (UMLS). We then designed an efficient graph-based simulated annealing approach to identify coexpression modules frequently and specifically occurring in datasets related to individual phenotypes. By requiring phenotype-specific recurrence, we ensure the robustness of our findings. We discovered 118,772 modules specific to 42 phenotypes, and developed validation tests combining Gene Ontology, GeneRIF and UMLS. Our method is generally applicable to any kind of abundant network data with defined phenotype association, and thus paves the way for genome-wide, gene network-phenotype maps.
Mehan
Michael R
MR
Program in Computational Biology, Department of Biological Sciences, University of Southern California , Los Angeles, CA 90089, USA.
Nunez-Iglesias
Juan
J
Kalakrishnan
Mrinal
M
Waterman
Michael S
MS
Zhou
Xianghong Jasmine
XJ
D000465:Algorithms; D001185:Artificial Intelligence; D020869:Gene Expression Profiling; D023281:Genomics / Q000379:methods*; D006801:Humans; D010641:Phenotype