Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Review Article
  • Published:

Exploring genetic interactions and networks with yeast

Key Points

  • Large-scale gene-deletion analyses reveal that mutations in most eukaryotic genes have little discernable effect.

  • Rarely, pairwise combinations of mutant alleles can be inviable even though the single mutants themselves are viable, a phenomenon that is termed synthetic lethality. Synthetic lethality identifies a functional relationship between genes, indicating that they work together and impinge on the same essential function.

  • Two different methods, synthetic genetic array (SGA) analysis and diploid synthetic-lethal analysis by microarray (dSLAM), enable large-scale mapping of synthetic-lethal genetic interactions in yeast.

  • Collections of yeast genes that have been cloned into vectors to enable their overexpression allow systematic analysis of synthetic dosage suppression, whereby the overexpression of a gene compensates for the defect in another gene, or of synthetic dosage lethality, whereby the overexpression of a gene exaggerates the defect that is associated with another gene.

  • Quantification of fitness phenotypes enables genetic interactions to be scored as alleviating or aggravating when the double-mutant fitness is compared with the expected value, derived from a multiplicative model for combining the single-mutant fitnesses.

  • There are 1,000 essential genes in yeast and, 200,000 synthetic-lethal/sick double-mutant combinations, indicating that there are 200-fold more ways of creating the same mutant phenotype through a digenic interaction.

  • Large-scale mapping of synthetic-genetic interactions has showed that, for both non-essential and essential genes, genetic interactions tend to occur among functionally related genes.

  • The yeast synthetic-lethal genetic network shows 'small world' properties with a short characteristic path length and dense local neighbourhoods, in which genes tend to interact with their immediate neighbours. The dense neighbourhood characteristic of small-world networks is of particular interest because it can be exploited to predict interactions from a sparsely mapped network.

  • Although synthetic-lethal genetic interactions overlap with protein–protein interactions more often than expected by chance, such overlap is relatively rare, occurring at a frequency of less than 1%, and largely confined to genes within pathways that contain an essential gene. Most synthetic-lethal genetic interactions occur between different pathways and are, therefore. orthogonal to protein–protein interactions.

  • Genes whose products function within the same pathway or complex often show a similar pattern of genetic interactions; therefore, clustering can be used to identify genes encoding pathway or complex components.

  • RNAi libraries that target all predicted genes in metazoan models and in human cell lines offers the potential for genome-wide analysis in complex systems and have already been applied successfully to Caenorhabditis elegans.

  • Deletion of a gene encoding the target of an inhibitory compound should cause cellular effects similar to inhibition of the target by drug treatment. So, the complete matrix of synthetic-lethal interactions for yeast should serve as a key for deciphering the chemical–genetic profile of a specific compound, for example the set of yeast mutants that are hypersensitive to the compound, thereby linking the compound to a target pathway.

Abstract

The development and application of genetic tools and resources has enabled a partial genetic-interaction network for the yeast Saccharomyces cerevisiae to be compiled. Analysis of the network, which is ongoing, has already provided a clear picture of the nature and scale of the genetic interactions that robustly sustain biological systems, and how cellular buffering is achieved at the molecular level. Recent studies in yeast have begun to define general principles of genetic networks, and also pave the way for similar studies in metazoan model systems. A comparative understanding of genetic-interaction networks promises insights into some long-standing genetic problems, such as the nature of quantitative traits and the basis of complex inherited disease.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Figure 1: The yeast deletion collection and parallel analysis.
Figure 2: The synthetic genetic array (SGA) methodology.
Figure 3: Diploid-based synthetic lethality analysis with microarrays (dSLAM).
Figure 4: A yeast genetic-interaction network, as determined by synthetic genetic array (SGA) analysis.
Figure 5: Relationships between genetic and protein interactions for complexes.
Figure 6: Hierarchical clustering of genetic and chemical-genetic interactions.

Similar content being viewed by others

References

  1. Davierwala, A. P. et al. The synthetic genetic interaction spectrum of essential genes. Nature Genet. 37, 1147–1152 (2005). This paper describes the first major application of SGA analysis to mapping of genetic-interaction networks among essential genes, and reveals that they seem to act as highly connected hubs on the network.

    Article  CAS  PubMed  Google Scholar 

  2. Tong, A. H. Y. et al. Global mapping of the yeast genetic interaction network. Science 303, 808–813 (2004). This study describes large-scale mapping of synthetic-lethal genetic interactions in yeast by SGA analysis. The results highlight the utility of genetic networks for discovering gene function and define the topology and general properties of genetic networks.

    Article  CAS  PubMed  Google Scholar 

  3. Hughes, T. R., Robinson, M. D., Mitsakakis, N. & Johnston, M. The promise of functional genomics: completing the encyclopedia of a cell. Curr. Opin. Microbiol. 7, 546–554 (2004).

    Article  CAS  PubMed  Google Scholar 

  4. Dolinski, K. & Botstein, D. Changing perspective in yeast research nearly a decade after the genome sequence. Genome Res. 15, 1611–1619 (2006).

    Article  CAS  Google Scholar 

  5. Giaever, G. et al. Functional profiling of the Saccharomyces cerevisiae genome. Nature 418, 387–391 (2002). A landmark paper that describes the construction and use of the yeast deletion-mutant collection.

    CAS  PubMed  Google Scholar 

  6. Winzeler, E. A. et al. Functional characterization of the S. cerevisiae genome by gene deletion and parallel analysis. Science 285, 901–906 (1999).

    Article  CAS  PubMed  Google Scholar 

  7. Hillenmeyer, M. E. et al. The chemical genomic portrait of the cell reveals a phenotype for all genes. (Submitted).

  8. Deutschbauer, A. et al. Mechanisms of haploinsufficiency revealed by genome-wide profiling in yeast. Genetics 169, 1915–1925 (2005).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  9. Hartman, J. L., Garvik, B. & Hartwell, L. Principles for the buffering of genetic variation. Science 291, 1001–1004 (2001). An excellent opinion piece that explores how eukaryotic genomes are buffered against genetic and environmental insults and outlines how synthetic-lethal interaction maps can be used to understand the relationship between genotype and phenotype.

    Article  CAS  PubMed  Google Scholar 

  10. Hartwell, L. H. Yeast and cancer. Biosci. Rep. 22, 373–394 (2002).

    Article  CAS  PubMed  Google Scholar 

  11. Dobzhansky, T. Genetics of natural populations, XIII: recombination and variability in populations of Drosophila pseudoobscura. Genetics 31, 269–290 (1946).

    CAS  PubMed Central  PubMed  Google Scholar 

  12. Sturtevant, A. H. A highly specific complementary lethal system in Drosophila melanogaster. Genetics 41, 118–123 (1956).

    CAS  PubMed Central  PubMed  Google Scholar 

  13. Novick, P., Osmond, B. C. & Botstein, D. Suppressors of yeast actin mutants. Genetics 121, 659–674 (1989). One of the first yeast papers to describe synthetic-lethal genetic interactions, with useful references to the early D. melanogaster literature.

    CAS  PubMed Central  PubMed  Google Scholar 

  14. Guarente, L. Synthetic enhancement in gene interaction: a genetic tool come of age. Trends Genet. 9, 362–366 (1993).

    Article  CAS  PubMed  Google Scholar 

  15. Bender, A. & Pringle, J. R. Use of a screen for synthetic lethal and multicopy suppressor mutants to identify two new genes involved in morphogenesis in Saccharomyces cerevisiae. Mol. Cell. Biol. 11, 1295–1305 (1991). This manuscript describes the first use of a yeast colony sectoring assay as screen for synthetic lethal genetic interactions.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  16. Basson, M. E., Moore, R. L., O'Rear, J. & Rine, J. Identifying mutations in duplicated functions in Saccharomyces cerevisiae: recessive mutations in HMG-CoA reductase genes. Genetics 117, 645–655 (1987).

    CAS  PubMed Central  PubMed  Google Scholar 

  17. Suter, B., Auerbach, D. & Stagljar, I. Yeast-based functional genomics and proteomics technologies: the first 15 years and beyond. Biotechniques 40, 625–644 (2006).

    Article  CAS  PubMed  Google Scholar 

  18. Wach, A., Brachat, A., Pohlmann, R. & Philippsen, P. New heterologous modules for classical or PCR-based gene disruptions in Saccharomyces cerevisiae. Yeast 10, 1793–1808 (1994).

    Article  CAS  PubMed  Google Scholar 

  19. Kellis, M., Patterson, N., Endrizzi, M., Birren, B. & Lander, E. S. Sequencing and comparison of yeast species to identify genes and regulatory elements. Nature 423, 241–254 (2003).

    Article  CAS  PubMed  Google Scholar 

  20. Cliften, P. et al. Finding functional features in Saccharomyces genomes by phylogenetic footprinting. Science 301, 71–76 (2003).

    Article  CAS  PubMed  Google Scholar 

  21. Kastenmayer, J. P. et al. Functional genomics of genes with small open reading frames (sORFs) in S. cerevisiae. Genome Res. 16, 365–373 (2006).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  22. Mnainmeh, S. et al. Exploration of essential gene functions via titrable promoter alleles. Cell 118, 31–44 (2004).

    Article  Google Scholar 

  23. Dohmen, R. J. & Varshavsky, A. Heat-inducible degron and the making of conditional mutants. Methods Enzymol. 399, 799–822 (2005).

    Article  CAS  PubMed  Google Scholar 

  24. Kanemaki, M., Sanchez-Diaz, A., Gambus, A. & Labib, K. Functional proteomic identification of DNA replication proteins by induced proteolysis in vivo. Nature 423, 720–724 (2003).

    Article  CAS  PubMed  Google Scholar 

  25. Schuldiner, M. et al. Exploration of the function and organization of the yeast early secretory pathway through an epistatic miniarray profile. Cell 123, 507–519 (2005).

    Article  CAS  PubMed  Google Scholar 

  26. Ho, Y. et al. Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry. Nature 415, 180–183 (2002).

    Article  CAS  PubMed  Google Scholar 

  27. Butcher, R. A. et al. Microarray-based method for monitoring yeast overexpression strains reveals small-molecular targets in the TOR pathway. Nature Chem. Biol. 2, 103–109 (2006).

    Article  CAS  Google Scholar 

  28. Zhu, H. et al. Global analysis of protein activities using proteome chips. Science 293, 2101–2105 (2001).

    Article  CAS  PubMed  Google Scholar 

  29. Gelperin, D. M. et al. Biochemical and genetic analysis of the yeast proteome with a movable ORF collection. Genes Dev. 19, 2816–2826 (2005).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  30. Tong, A. H. Y. et al. Systematic genetic analysis with ordered arrays of yeast deletion mutants. Science 294, 2364–2368 (2001). This paper describes development of the SGA method and its application to synthetic-lethal genetic-interaction mapping. The study also provides the first glimpse of a genetic-interaction network.

    Article  CAS  PubMed  Google Scholar 

  31. Pan, X. et al. A robust toolkit for functional profiling of the yeast genome. Mol. Cell 16, 487–496 (2004). A study that describes the development of dSLAM, a transformation-based method of creating double mutants that provides a barcode microarray read-out for synthetic-lethal genetic interactions.

    Article  CAS  PubMed  Google Scholar 

  32. Pan, X. et al. A DNA integrity network in the yeast Saccharomyces cerevisiae. Cell 124, 1069–1081 (2006). This work describes the application of dSLAM analysis to the study of genes involved in DNA synthesis and repair, and genome integrity.

    Article  CAS  PubMed  Google Scholar 

  33. Surana, U. et al. The role of CDC28 and cyclins during mitosis in the budding yeast S. cerevisiae. Cell 65, 145–161 (1991).

    Article  CAS  PubMed  Google Scholar 

  34. Reguly, T. et al. Comprehensive curation and analysis of global interaction networks in Saccharomyces cerevisiae. J. Biol. 5, 11 (2006).

    Article  PubMed Central  PubMed  Google Scholar 

  35. Kroll, E. S., Hyland, K. M., Hieter, P. & Li, J. J. Establishing genetic interactions by a synthetic dosage lethality phenotype. Genetics 143, 95–102 (1996).

    CAS  PubMed Central  PubMed  Google Scholar 

  36. Measday, V. & Hieter, P. Synthetic dosage lethality. Methods Enzymol. 350, 316–326 (2002).

    Article  CAS  PubMed  Google Scholar 

  37. Measday, V. et al. Systematic yeast synthetic lethal and synthetic dosage lethal screens identify genes required for chromosome segregation. Proc. Natl Acad. Sci. USA 102, 13956–13961 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Sopko, R. et al. Mapping pathways and phenotypes by systematic gene overexpression. Mol. Cell 21, 319–330 (2006).

    Article  CAS  PubMed  Google Scholar 

  39. Veitia, R. A. Exploring the etiology of haploinsufficiency. Bioessays 24, 175–184 (2002).

    Article  CAS  PubMed  Google Scholar 

  40. Lum, P. Y. et al. Discovering novel modes of action for therapeutic compounds unsing a genome-wide screen of yeast heterozygotes. Cell 116, 121–137 (2004).

    Article  CAS  PubMed  Google Scholar 

  41. Giaever, G. et al. Chemogenomic profiling: identifying the functional interactions of small molecules in yeast. Proc. Natl Acad. Sci. USA 101, 793–798 (2005).

    Article  CAS  Google Scholar 

  42. Haarer, B., Viggiano, S., Hibbs, M. A., Troyanskya, O. G. & Amberg, D. C. Modeling complex genetic interactions in a simple eukaryotic genome: actin displays a rich spectrum of complex haploinsufficiencies. Genes Dev. 21, 148–159 (2007).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  43. Krogan, N. et al. Global landscape of protein complexes in the yeast Saccharomyces cerevisiae. Nature 440, 637–643 (2006).

    Article  CAS  PubMed  Google Scholar 

  44. Collins, S. R., Schuldiner, M., Krogan, N. & Weissman, J. S. A strategy for extracting and analyzing large-scale quantitative epistatic interaction data. Genome Biol. 7, R63 (2006). This manuscript describes a method for generating quantitative genetic-interaction data sets using SGA analysis.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  45. Drees, B. L. et al. Derivation of genetic interaction networks from quanitative phenotype data. Genome Biol. 6, R38 (2005).

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  46. Segre, D., Deluna, A., Church, G. M. & Kishony, R. Modular epistasis in yeast metabolism. Nature Genet. 37, 77–83 (2005). This study outlines the general concept that the expected phenotype of a double mutant is a multiplicative combination of two single mutants, and that scoring of deviations from this expected value generates genetic networks to describe functional relationships among metabolic pathways.

    Article  CAS  PubMed  Google Scholar 

  47. Evangelista, M. et al. Bni1p, a yeast formin linking Cdc42p and the actin cytoskeleton during polarized morphogenesis. Science 276, 118–122 (1997).

    Article  CAS  PubMed  Google Scholar 

  48. Barabasi, A. L. & Bonabeau, E. Scale-free networks. Sci. Am. 288, 60–69 (2003).

    Article  PubMed  Google Scholar 

  49. Kamb, A. Mutation load, functional overlap, and synthetic lethality in the evolution and treatment of cancer. J. Theor. Biol. 223, 205–213 (2003).

    Article  CAS  PubMed  Google Scholar 

  50. Goldberg, D. S. & Roth, F. P. Assessing experimentally derived interactions in a small word. Proc. Natl Acad. Sci. USA 100, 4372–4376 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Ito, T. et al. A comprehensive two-hybrid analysis to explore the yeast protein interactome. Proc. Natl Acad. Sci. USA 98, 4569–4574 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Uetz, P. et al. A comprehensive analysis of protein–protein interactions in Saccharomyces cerevisiae. Nature 403, 623–627 (2000).

    Article  CAS  PubMed  Google Scholar 

  53. Gavin, A. C. et al. Functional organization of the yeast proteome by systematic analysis of protein complexes. Nature 415, 141–147 (2002).

    Article  CAS  PubMed  Google Scholar 

  54. Gavin, A. C. et al. Proteome survey reveals modularity of the yeast cell machinery. Nature 440, 631–636 (2006).

    Article  CAS  PubMed  Google Scholar 

  55. Kelley, R. & Ideker, T. Systematic interpretation of genetic interactions using protein networks. Nature Biotech. 23, 561–566 (2005).

    Article  CAS  Google Scholar 

  56. Bader, G. D. et al. Functional genomics and proteomics: charting a multidimensional map of the yeast cell. Trends Cell Biol. 13, 344–356 (2003).

    Article  CAS  PubMed  Google Scholar 

  57. Ye, P. et al. Gene function prediction from congruent synthetic lethal interactions in yeast. Mol. Syst. Biol. 1, 2005.0026 (2005).

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  58. Zhang, L. V. et al. Motifs, themes and thematic maps of an integrated Saccharomyces cerevisiae interaction network. J. Biol. 4, 6 (2005).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  59. Goehring, A. S. et al. Synthetic lethal analysis implicates Ste20p, a p21-activated protein kinase, in polarisome activation. Mol. Biol. Cell 14, 1501–1516 (2003).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  60. Brem, R. B. & Kruglyak, L. The landscape of genetic complexity across 5,700 gene expression traits in yeast. Proc. Natl Acad. Sci. USA 102, 1572–1577 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Brem, R. B., Storey, J. D., Whittle, J. & Kruglyak, L. Genetic interactions between polymorphisms that affect gene expression in yeast. Nature 436, 701–703 (2005).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  62. Consortium, I. H. A haplotype map of the human genome. Nature 437, 1299–1320 (2005).

    Article  CAS  Google Scholar 

  63. Gresham, D. et al. Genome-wide detection of polymorphisms at nucleotide resolution with a single DNA microarray. Science 311, 1932–1936 (2006).

    Article  CAS  PubMed  Google Scholar 

  64. Perstein, E. O., Ruderfer, D. M., Roberts, D. C., Schreiber, S. L. & Kruglyak, L. Genetic basis of individual differences in response to small-molecule drugs in yeast. Nature Genet. 39, 496–502 (2007).

    Article  CAS  Google Scholar 

  65. Moffat, J. & Sabatini, D. M. Building mammalian signalling pathways with RNAi screens. Nature Rev. Mol. Cell Biol. 7, 177–187 (2006).

    Article  CAS  Google Scholar 

  66. Echeverri, C. J. & Perrimon, N. High-throughput RNAi screening in cultured cells: a user's guide. Nature Rev. Genet. 7, 373–384 (2006).

    Article  CAS  PubMed  Google Scholar 

  67. Baugh, L. R. et al. Synthetic lethal analysis of Caenorhabditis elegans posterior embryonic patterning genes identified conserved genetic interactions. Genome Biol. 6, R45 (2005).

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  68. van Haaften, G., Vastenhouw, N. L., Nollen, E. A., Plasterk, R. H. & Tijsterman, M. Gene interactions in the DNA damage-response pathway identified by genome-wide RNA-interference analysis of synthetic lethality. Proc. Natl Acad. Sci. USA 101, 12992–12996 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Lehner, B., Crombie, C., Tischler, J., Fortunato, A. & Fraser, A. G. Systematic mapping of genetic interactions in C. elegans. Nature Genet. 38, 896–903 (2006). This study describes the first large-scale mapping of synthetic genetic networks in a metazoan, generated by feeding hypomorphic C. elegans mutants arrays of bacteria that expressed dsRNAi molecules targeting specific signalling pathways.

    Article  CAS  PubMed  Google Scholar 

  70. Badano, J. L., Teslovich, T. M. & Katsanis, N. The centrosome in human disease. Nature Rev. Genet. 6, 194–205 (2005).

    Article  CAS  PubMed  Google Scholar 

  71. Wong, S. L. et al. Combining biological networks to predict genetic interactions. Proc. Natl Acad. Sci. USA 101, 15682–25687 (2004). The first paper to show that functional genomics data sets can be used to predict genetic interactions.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Zhong, W. & Sternberg, P. Genome-wide prediction of C. elegans genetic interactions. Science 311, 1481–1484 (2006).

    Article  CAS  PubMed  Google Scholar 

  73. Parsons, A. B. et al. Integration of chemical-genetic and genetic interaction data links bioactive compounds to cellular targets and pathways. Nature Biotech. 22, 62–69 (2004). This work describes how synthetic-lethal genetic-interaction maps function as a key for deciphering chemical-genetic maps, providing a means of linking compounds to their target pathways.

    Article  CAS  Google Scholar 

  74. Sharom, J. R., Bellows, D. S. & Tyers, M. From large networks to small molecules. Curr. Opin. Chem. Biol. 8, 81–90 (2004).

    Article  CAS  PubMed  Google Scholar 

  75. Keith, C. T., Borisy, A. A. & Stockwell, B. R. The identification of combinations of molecules can result in highly effective drug regimens. Nature Rev. Drug Discov. 4, 71–78 (2003).

    Article  CAS  Google Scholar 

  76. Borisy, A. A. et al. Systematic discovery of multicomponent therapeutics. Proc. Natl Acad. Sci. USA 100, 7977–7982 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Phillips, P. C. The language of gene interaction. Genetics 149, 1167–1171 (1998). A wonderful review of the language that is used to describe genetic interactions, including terms like epistasis, with historical context.

    CAS  PubMed Central  PubMed  Google Scholar 

  78. Fisher, R. A. The correlations between relatives on the supposition of Mendelian inheritance. Trans. R. Soc. Edinb. 52, 399–433 (1918).

    Article  Google Scholar 

  79. Sternberg, P., Stern, M. J., Clark, I. & Herskowitz, I. Activation of the yeast HO gene by release from muliple negative controls. Cell 48, 567–577 (1987).

    Article  CAS  PubMed  Google Scholar 

  80. Hartwell, L. H., Culotti, J., Pringle, J. R. & Reid, B. J. Genetic control of cell division cycle in yeast. Science 183, 46–51 (1974).

    Article  CAS  PubMed  Google Scholar 

  81. Sprague, G. F. Jr & Thorner, J. W. in The Molecular and Cellular Biology of the Yeast Saccharomyces: Gene Expression Vol. 2 (eds Jones, E. W., Pringle, J. R. & Broach, J. R.) 657–744 (Cold Spring Harbor Laboratory Press, Cold Spring Harbor, 1992).

    Google Scholar 

  82. Avery, L. & Wasserman, S. Ordering gene function: the interpretation of epistasis in regulatory hierarchies. Trends Genet. 8, 312–316 (1992).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  83. Baker, B. S. & Ridge, K. A. Sex and the single cell. I. On the action of major loci affecting sex determination in Drosophila melanogaster. Genetics 94, 383–423 (1980).

    CAS  PubMed Central  PubMed  Google Scholar 

  84. Ihmels, J., Collins, S. R., Schuldiner, M., Krogan, N. & Weissman, J. S. Backup without redundancy: genetic interactions reveal the cost of duplicate gene loss. Mol. Syst. Biol. 3, 86 (2007).

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  85. Harrison, R., Papp, B., Pal, C., Oliver, S. G. & Delneri, D. Plasticity of genetic interactions in metabolic networks of yeast. Proc. Natl Acad. Sci. USA 104, 2307–2312 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Forsburg, S. L. The art and design of genetic screens: yeast. Nature Rev. Genet. 2, 659–668 (2001).

    Article  CAS  PubMed  Google Scholar 

  87. Kaiser, C. A. & Schekman, R. Distinct sets of SEC genes govern transport vesicle formation and fusion early in the secretory pathway. Cell 61, 723–733 (1990).

    Article  CAS  PubMed  Google Scholar 

  88. Finger, F. & Novick, P. Synthetic interactions of the post-golgi sec mutations of Saccharomyces cerevisiae. Genetics 156, 943–951 (2000).

    CAS  PubMed Central  PubMed  Google Scholar 

Download references

Acknowledgements

Genetic interaction network projects in the Andrews and Boone laboratories are supported by grants from the Canadian Institutes of Health Research (CIHR) and Genome Canada through the Ontario Genomics Institute. C.B. is an International Scholar of the Howard Hughes Medical Institute and holds a Canada Research Chair in Functional Genomics. We would like to thank Amy Hin Yan Tong and Michael Costanzo for help with the figures and comments on the manuscript.

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Charles Boone or Brenda J. Andrews.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Related links

Related links

DATABASES

OMIM

Bardet–Beidl syndrome

FURTHER INFORMATION

Andrews lab

Boone lab

BIOBASE

International HapMap Project

Saccharomyces genome database

The general repository for interaction datasets

Glossary

Synthetic enhancement

The situation in which a mutation in one gene exacerbates the phenotypic severity of a mutation in a second gene.

Synthetic lethality

The situation in which two genes that are non-essential when individually mutated cause lethality when they are combined as a double mutant.

Haploinsufficiency

The situation in diploid cells in which heterozygous mutants that produce a reduced amount of functional gene product can be less robust than the wild type to perturbations that affect essential functions.

Tetrad analysis

The four haploid cells that are produced by an individual meiosis in budding yeast are referred to as a tetrad. The tetrad is enclosed in a sac called an ascus. Tetrad analysis involves the isolation and analysis of the haploid meiotic spores of individual asci for the segregation of genetic markers.

N-end rule

Relates the in vivo half-life of a protein to the identity of its N-terminal residue. In eukaryotes, the N-end rule pathway is part of the ubiquitin system.

Hypomorphic

Describes an allele that carries a mutation that causes a partial loss of gene function.

Synthetic genetic array analysis

A robotic procedure that is used to create, select and systematically examine the growth phenotypes of yeast double-mutant haploid strains.

Pinning

The use of hand-held or robotic tools, which are composed of small floating pinheads, to replicate yeast colonies to different media for genetic tests (typical formats include 96, 384, 768 and 1,536 pinheads per replica tool).

Suppression

The situation in which a mutation in one gene counteracts the effects of a mutation in another, so that the phenotype of the double mutant is more like that of the wild type.

Nodes

In typical network diagrams, genes or proteins are represented as nodes, whereas the connections between the nodes are termed edges.

Clustering algorithms

Algorithms that group together objects that are 'similar'; objects belonging to other clusters are 'dissimilar'. Clustering algorithms have been used extensively to view large collections of biological data, such as microarray expression profiles and genetic-interaction data.

Congruency score

A numerical ranking of the degree of partner sharing in a network.

Isogenic

Strains or organisms that share identical genotypes.

Gene association studies

Studies that assess whether genotype frequencies are different between two groups that differ in phenotype.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Boone, C., Bussey, H. & Andrews, B. Exploring genetic interactions and networks with yeast. Nat Rev Genet 8, 437–449 (2007). https://doi.org/10.1038/nrg2085

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1038/nrg2085

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing