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

Anastasia Baryshnikova, Michael Costanzo, Yungil Kim, Huiming Ding, Judice Koh, Kiana Toufighi, Ji Young Youn, Jiongwen Ou, Bryan Joseph San Luis, Sunayan Bandyopadhyay, Matthew Hibbs, David Hess, Anne Claude Gingras, Gary D. Bader, Olga G. Troyanskaya, Grant W. Brown, Brenda Andrews, Charles Boone, Chad L. Myers

Research output: Contribution to journalArticlepeer-review

261 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)1017-1024
Number of pages8
JournalNature Methods
Issue number12
StatePublished - Dec 2010

Bibliographical note

Funding Information:
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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