A genome-scale genetic interaction map was constructed by examining 5.4 million gene-gene pairs in budding yeast, Saccharomyces cerevisiae, to generate quantitative genetic interaction profiles for ~75% of all genes. The map reveals a functional network where genes with similar biological processes cluster together, and highly correlated profiles delineate specific pathways. The network identifies functional cross-connections between all bioprocesses, mapping a cellular wiring diagram of pleiotropy. Genetic interaction degree correlated with gene attributes, which may be informative about genetic network hubs in other organisms. The study also demonstrates that extensive and unbiased mapping of the genetic landscape provides a key for interpreting chemical-genetic interactions and drug target identification. Genetic interactions were analyzed using a model to estimate fitness defects from double-mutant colony sizes. The study screened 1712 S. cerevisiae query genes, including 334 conditional or hypomorphic alleles of essential genes, for a total of ~5.4 million gene pairs. These interactions were compared to identify ~170,000 interactions, a threefold increase over previously reported data. The data captured ~35% of previously reported negative genetic interactions and showed significant correlation with high-resolution liquid growth profiles. The genetic interaction network was used to construct a functional map of the cell, grouping genes with similar interaction patterns. This network highlights genetic relations between diverse biological processes and the inherent functional organization of the cell. Genes with tightly correlated profiles form discernible clusters corresponding to distinct bioprocesses. The study also predicted gene functions for uncharacterized genes based on network connectivity and identified functional relationships between genes. The genetic interaction network contains functional information at multiple levels of resolution. The study used the network to dissect broad biological processes into distinct, yet interdependent, gene cohorts. The network also revealed network organization reflecting biological pathways and protein complexes. Genetic interactions between different pathways and complexes were often monochromatic, as predicted. Genetic interaction hubs showed a clear association with physiological and evolutionary properties, suggesting they may be predictive of genetic interactions in other organisms. The study found a strong correlation between genetic interaction degree and single-mutant fitness. Genetic interaction hubs also showed a high degree of pleiotropy, indicating their role in the integration and execution of morphogenetic programs. The study also found that genetic interaction hubs are highly conserved and may be evolutionarily constrained. The study identified a significant correlation between genetic and physical interaction degree for any given gene. Genetic interaction hubs tend to be expressed at higher mRNA levels and are more conserved and less prone to duplication. The study also found that genetic interactions are distributed across different cellular processes, with some processes showing more interactions than expected. The study used ANCOVA to analyze the variation in genetic interactions across bioprocesses and found that gene-specific properties explained the variation. The study also found that genetic interactions overlap with 10 to 20% of protein-protein interaction pairsA genome-scale genetic interaction map was constructed by examining 5.4 million gene-gene pairs in budding yeast, Saccharomyces cerevisiae, to generate quantitative genetic interaction profiles for ~75% of all genes. The map reveals a functional network where genes with similar biological processes cluster together, and highly correlated profiles delineate specific pathways. The network identifies functional cross-connections between all bioprocesses, mapping a cellular wiring diagram of pleiotropy. Genetic interaction degree correlated with gene attributes, which may be informative about genetic network hubs in other organisms. The study also demonstrates that extensive and unbiased mapping of the genetic landscape provides a key for interpreting chemical-genetic interactions and drug target identification. Genetic interactions were analyzed using a model to estimate fitness defects from double-mutant colony sizes. The study screened 1712 S. cerevisiae query genes, including 334 conditional or hypomorphic alleles of essential genes, for a total of ~5.4 million gene pairs. These interactions were compared to identify ~170,000 interactions, a threefold increase over previously reported data. The data captured ~35% of previously reported negative genetic interactions and showed significant correlation with high-resolution liquid growth profiles. The genetic interaction network was used to construct a functional map of the cell, grouping genes with similar interaction patterns. This network highlights genetic relations between diverse biological processes and the inherent functional organization of the cell. Genes with tightly correlated profiles form discernible clusters corresponding to distinct bioprocesses. The study also predicted gene functions for uncharacterized genes based on network connectivity and identified functional relationships between genes. The genetic interaction network contains functional information at multiple levels of resolution. The study used the network to dissect broad biological processes into distinct, yet interdependent, gene cohorts. The network also revealed network organization reflecting biological pathways and protein complexes. Genetic interactions between different pathways and complexes were often monochromatic, as predicted. Genetic interaction hubs showed a clear association with physiological and evolutionary properties, suggesting they may be predictive of genetic interactions in other organisms. The study found a strong correlation between genetic interaction degree and single-mutant fitness. Genetic interaction hubs also showed a high degree of pleiotropy, indicating their role in the integration and execution of morphogenetic programs. The study also found that genetic interaction hubs are highly conserved and may be evolutionarily constrained. The study identified a significant correlation between genetic and physical interaction degree for any given gene. Genetic interaction hubs tend to be expressed at higher mRNA levels and are more conserved and less prone to duplication. The study also found that genetic interactions are distributed across different cellular processes, with some processes showing more interactions than expected. The study used ANCOVA to analyze the variation in genetic interactions across bioprocesses and found that gene-specific properties explained the variation. The study also found that genetic interactions overlap with 10 to 20% of protein-protein interaction pairs