A genetic interaction occurs when the combination of two mutations leads to an unexpected phenotype. Screens for synthetic genetic interactions have been used extensively to identify genes whose products are functionally related. In particular, synthetic lethal genetic interactions often identify genes that buffer one another or impinge on the same essential pathway. For the yeast Saccharomyces cerevisiae, we developed a method termed synthetic genetic array (SGA) analysis, which offers an efficient approach for the systematic construction of double mutants and enables a global analysis of synthetic genetic interactions. In a typical SGA screen, a query mutation is crossed to an ordered array of ~5000 viable gene deletion mutants (representing ~80% of all yeast genes) such that meiotic progeny harboring both mutations can be scored for fitness defects. This approach can be extended to all ~6000 genes through the use of yeast arrays containing mutants carrying conditional or hypomorphic alleles of essential genes. Estimating the fitness for the two single mutants and their corresponding double mutant enables a quantitative measurement of genetic interactions, distinguishing negative (synthetic lethal) and positive (within pathway and suppression) interactions. The profile of genetic interactions represents a rich phenotypic signature for each gene and clustering genetic interaction profiles group genes into functionally relevant pathways and complexes. This array-based approach automates yeast genetic analysis in general and can be easily adapted for a number of different genetic screens or combined with high-content screening systems to quantify the activity of specific reporters in genome-wide sets of single or more complex multiple mutant backgrounds. Comparison of genetic and chemical-genetic interaction profiles offers the potential to link bioactive compounds to their targets. Finally, we also developed an SGA system for the fission yeast Schizosaccharomyces pombe, providing another model system for comparative analysis of genetic networks and testing the conservation of genetic networks over millions of years of evolution.

• PMID: 20946810
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Journal Article

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Baryshnikova A, Costanzo M, Dixon S, Vizeacoumar FJ, Myers CL, Andrews B, Boone C

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