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Quantitative analysis of fitness and genetic interactions in yeast on a genome scale

Nature Methods volume 7, pages 1017–1024 (2010)Cite this article

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Abstract

Global quantitative analysis of genetic interactions is a powerful approach for deciphering the roles of genes and mapping functional relationships among pathways. Using colony size as a proxy for fitness, we developed a method for measuring fitness-based genetic interactions from high-density arrays of yeast double mutants generated by synthetic genetic array (SGA) analysis. We identified several experimental sources of systematic variation and developed normalization strategies to obtain accurate single- and double-mutant fitness measurements, which rival the accuracy of other high-resolution studies. We applied the SGA score to examine the relationship between physical and genetic interaction networks, and we found that positive genetic interactions connect across functionally distinct protein complexes revealing a network of genetic suppression among loss-of-function alleles.

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Figure 1: The SGA score for measuring quantitative genetic interactions.
Figure 2: Evaluation of single-mutant fitness measures.
Figure 3: Evaluation of quantitative genetic interactions.
Figure 4: Evaluation of functional information derived from genetic interactions.
Figure 5: Analysis of genetic interactions within and between protein complexes.
Figure 6: Cross-complex genetic suppression network revealed by quantitative genetic interaction analysis.

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Acknowledgements

We thank R. Mani, F. Roth, B. Papp, T. Maeda and T. Hays for helpful discussions and critical comments. This work was supported by Genome Canada through the Ontario Genomics Institute (2004-OGI-3-01), the Canadian Institutes of Health Research (GSP-415-67; C.B. and B.A.) (MOP-79368; G.W.B.), the US National Institutes of Health (1R01HG005084-01A1; C.L.M., Y.K. and S.B.) and the National Science Foundation (DBI 0953881, MCB 0918908; C.L.M. and Y.K.) and partially supported by a seed grant from the Minnesota Supercomputing Institute (C.L.M. and Y.K.).

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Author notes

  1. Anastasia Baryshnikova and Michael Costanzo: These authors contributed equally to this work.

Authors and Affiliations

  1. Banting and Best Department of Medical Research, Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Canada

    Anastasia Baryshnikova, Michael Costanzo, Huiming Ding, Judice Koh, Kiana Toufighi, Ji-Young Youn, Bryan-Joseph San Luis, Gary D Bader, Brenda Andrews & Charles Boone

  2. Department of Molecular Genetics, Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Canada

    Anastasia Baryshnikova, Ji-Young Youn, Gary D Bader, Brenda Andrews & Charles Boone

  3. Department of Computer Science and Engineering, University of Minnesota–Twin Cities, Minneapolis, Minnesota, USA

    Yungil Kim, Sunayan Bandyopadhyay & Chad L Myers

  4. Department of Electrical and Computer Engineering, University of Minnesota–Twin Cities, Minneapolis, Minnesota, USA

    Yungil Kim

  5. Department of Biochemistry, Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Canada

    Jiongwen Ou & Grant W Brown

  6. The Jackson Laboratory, Bar Harbor, Maine, USA

    Matthew Hibbs

  7. Department of Biology, Santa Clara University, Santa Clara, California, USA

    David Hess

  8. Samuel Lunenfeld Research Institute, Mount Sinai Hospital, Toronto, Canada

    Anne-Claude Gingras

  9. Department of Computer Science, Lewis-Sigler Institute for Integrative Genomics, Carl Icahn Laboratory, Princeton University, Princeton, New Jersey, USA

    Olga G Troyanskaya

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  1. Anastasia Baryshnikova
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Contributions

M.C., C.L.M., C.B. and B.A. conceived and coordinated the project. C.L.M. and A.B. designed and implemented the algorithm. A.B., C.L.M., Y.K., J.K. and S.B. performed statistical analysis. J.-Y.Y., B.-J.S.L., J.O., G.W.B. and M.C. validated experiments. H.D. and K.T. analyzed and processed images. M.H., D.H., G.D.B. and O.G.T. provided statistical insight. A.-C.G. provided biological insight. M.C., C.L.M., C.B. and A.B. prepared the manuscript.

Corresponding authors

Correspondence to Brenda Andrews, Charles Boone or Chad L Myers.

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Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–11, Supplementary Tables 1–3 and Supplementary Notes 1–6 (PDF 10244 kb)

Supplementary Data 1

Single-mutant fitness standard. (XLS 514 kb)

Supplementary Data 2

Protein complex standard. (XLS 207 kb)

Supplementary Data 3

List of complex-complex pairs enriched for positive genetic interactions. (XLS 179 kb)

Supplementary Software

Matlab source code for the SGA score algorithm. (ZIP 186 kb)

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Baryshnikova, A., Costanzo, M., Kim, Y. et al. Quantitative analysis of fitness and genetic interactions in yeast on a genome scale. Nat Methods 7, 1017–1024 (2010). https://doi.org/10.1038/nmeth.1534

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