The fundamental aim of genetics is to understand how an organism's phenotype is determined by its genotype, and implicit in this is predicting how changes in DNA sequence alter phenotypes. A single network covering all the genes of an organism might guide such predictions down to the level of individual cells and tissues. To validate this approach, we computationally generated a network covering most C. elegans genes and tested its predictive capacity. Connectivity within this network predicts essentiality, identifying this relationship as an evolutionarily conserved biological principle. Critically, the network makes tissue-specific predictions - we accurately identify genes for most systematically assayed loss-of-function phenotypes, which span diverse cellular and developmental processes. Using the network, we identify 16 genes whose inactivation suppresses defects in the retinoblastoma tumor suppressor pathway, and we successfully predict that the dystrophin complex modulates EGF signaling. We conclude that an analogous network for human genes might be similarly predictive and thus facilitate identification of disease genes and rational therapeutic targets.
Bibliographical noteFunding Information:
Worm strains used in this work were provided by the Caenorhabditis Genetics Center, which is funded by the US National Institutes of Health National Center for Research Resources (NCRR). SAGE data were obtained from the Genome BC C. elegans Gene Expression Consortium and were produced at the Michael Smith Genome Sciences Centre with funding from Genome Canada. This work was supported by grants from the N.S.F. (IIS-0325116, EIA-0219061), N.I.H. (GM06779-01), Welch (F1515), and a Packard Fellowship (E.M.M.). B.L. was supported by a Sanger Institute Postdoctoral Fellowship and the EMBL-CRG Systems Biology Program, and the Institució Catalana de Recerca i Estudis Avanc¸ats (ICREA). A.G.F., W.W. and C.C. are supported by the Wellcome Trust. We thank A. Enright for help with constructing the web interface for analyzing this data.
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