The article presents a comprehensive analysis of genetic interactions in *Saccharomyces cerevisiae* using a genome-scale approach. By examining 5.4 million gene-gene pairs, the authors generated quantitative genetic interaction profiles for approximately 75% of all genes, revealing a functional map of the cell. This map clusters genes with similar biological processes and identifies specific pathways that define gene function. The network highlights cross-connections between different biological processes, providing insights into the cellular wiring diagram of pleiotropy. The degree of genetic interaction is correlated with various gene attributes, suggesting potential hubs in other organisms. The study also demonstrates that unbiased mapping of the genetic landscape can aid in interpreting chemical-genetic interactions and drug target identification. The authors further explore the distribution of genetic interactions across different cellular processes and their overlap with protein-protein interaction networks. Finally, they discuss the implications of these findings for understanding genetic robustness, drug synergy, and the conservation of genetic interaction networks across different organisms.The article presents a comprehensive analysis of genetic interactions in *Saccharomyces cerevisiae* using a genome-scale approach. By examining 5.4 million gene-gene pairs, the authors generated quantitative genetic interaction profiles for approximately 75% of all genes, revealing a functional map of the cell. This map clusters genes with similar biological processes and identifies specific pathways that define gene function. The network highlights cross-connections between different biological processes, providing insights into the cellular wiring diagram of pleiotropy. The degree of genetic interaction is correlated with various gene attributes, suggesting potential hubs in other organisms. The study also demonstrates that unbiased mapping of the genetic landscape can aid in interpreting chemical-genetic interactions and drug target identification. The authors further explore the distribution of genetic interactions across different cellular processes and their overlap with protein-protein interaction networks. Finally, they discuss the implications of these findings for understanding genetic robustness, drug synergy, and the conservation of genetic interaction networks across different organisms.