A genetic interaction network containing approximately 1000 genes and approximately 4000 interactions was mapped by crossing mutations in 132 different query genes into a set of approximately 4700 viable gene yeast deletion mutants and scoring the double mutant progeny for fitness defects. Network connectivity was predictive of function because interactions often occurred among functionally related genes, and similar patterns of interactions tended to identify components of the same pathway. The genetic network exhibited dense local neighborhoods; therefore, the position of a gene on a partially mapped network is predictive of other genetic interactions. Because digenic interactions are common in yeast, similar networks may underlie the complex genetics associated with inherited phenotypes in other organisms.

Many disease states result from gene overexpression, often in a specific genetic context. To explore gene overexpression phenotypes systematically, we assembled an array of 5280 yeast strains, each containing an inducible copy of an S. cerevisiae gene, covering >80% of the genome. Approximately 15% of the overexpressed genes (769) reduced growth rate. This gene set was enriched for cell cycle-regulated genes, signaling molecules, and transcription factors. Overexpression of most toxic genes resulted in phenotypes different from known deletion mutant phenotypes, suggesting that overexpression phenotypes usually reflect a specific regulatory imbalance rather than disruption of protein complex stoichiometry. Global overexpression effects were also assayed in the context of a cyclin-dependent kinase mutant (pho85Delta). The resultant gene set was enriched for Pho85p targets and identified the yeast calcineurin-responsive transcription factor Crz1p as a substrate. Large-scale application of this approach should provide a strategy for identifying target molecules regulated by specific signaling pathways.

Nearly 20% of yeast genes are required for viability, hindering genetic analysis with knockouts. We created promoter-shutoff strains for over two-thirds of all essential yeast genes and subjected them to morphological analysis, size profiling, drug sensitivity screening, and microarray expression profiling. We then used this compendium of data to ask which phenotypic features characterized different functional classes and used these to infer potential functions for uncharacterized genes. We identified genes involved in ribosome biogenesis (HAS1, URB1, and URB2), protein secretion (SEC39), mitochondrial import (MIM1), and tRNA charging (GSN1). In addition, apparent negative feedback transcriptional regulation of both ribosome biogenesis and the proteasome was observed. We furthermore show that these strains are compatible with automated genetic analysis. This study underscores the importance of analyzing mutant phenotypes and provides a resource to complement the yeast knockout collection.