Nature Genetics
37, 471 - 477 (2005)
Published online: 10 April 2005; | doi:10.1038/ng1545
Epistasis analysis with global transcriptional phenotypesNancy Van Driessche1, 2, Janez Demsar3, Ezgi O Booth1, 4, Paul Hill1, Peter Juvan3, Blaz Zupan3, Adam Kuspa1, 2, 5
& Gad Shaulsky1, 2, 41
Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, Houston, Texas 77030, USA. 2
Graduate Program in Developmental Biology, Baylor College of Medicine, One Baylor Plaza, Houston, Texas 77030, USA. 3
Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia. 4
Graduate Program in Structural Computational Biology and Molecular Biophysics, Baylor College of Medicine, Houston, Texas 77030, USA. 5
Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, Texas 77030, USA.
Correspondence should be addressed to Gad Shaulsky gadi@bcm.tmc.eduClassical epistasis analysis can determine the order of function of genes in pathways using morphological, biochemical and other phenotypes. It requires knowledge of the pathway's phenotypic output and a variety of experimental expertise and so is unsuitable for genome-scale analysis. Here we used microarray profiles of mutants as phenotypes for epistasis analysis. Considering genes that regulate activity of protein kinase A in Dictyostelium, we identified known and unknown epistatic relationships and reconstructed a genetic network with microarray phenotypes alone. This work shows that microarray data can provide a uniform, quantitative tool for large-scale genetic network analysis.
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