The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Defining genetic interaction
Open accessAbstract
Sometimes mutations in two genes produce a phenotype that is surprising in light of each mutation's individual effects. This phenomenon, which defines genetic interaction, can reveal functional relationships between genes and pathways. For example, double mutants with surprisingly slow growth define synergistic interactions that can identify compensatory pathways or protein complexes. Recent studies have used four mathematically distinct definitions of genetic interaction (here termed Product, Additive, Log, and Min). Whether this choice holds practical consequences has not been clear, because the definitions yield identical results under some conditions. Here, we show that the choice among alternative definitions can have profound consequences. Although 52% of known synergistic genetic interactions in Saccharomyces cerevisiae were inferred according to the Min definition, we find that both Product and Log definitions (shown here to be practically equivalent) are better than Min for identifying functional relationships. Additionally, we show that the Additive and Log definitions, each commonly used in population genetics, lead to differing conclusions related to the selective advantages of sexual reproduction.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Gene Ontology: tool for the unification of biology
Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., et al. (2000) · Nature Genetics · doi:10.1038/75556 · Cited by 43,206
BioGRID: a general repository for interaction datasets
Stark, C. (2006) · Nucleic Acids Research · doi:10.1093/nar/gkj109 · Cited by 3,960
Systematic Genetic Analysis with Ordered Arrays of Yeast Deletion Mutants
Tong, A. H. Y., Evangelista, M., Parsons, A. B., Xu, H., Bader, G. D., Pagé, N., Robinson, M., Raghibizadeh, S., Hogue, C. W. V., Bussey, H., et al. (2001) · Science · doi:10.1126/science.1065810 · Cited by 2,138
Schuldiner, M., Collins, S. R., Thompson, N. J., Denic, V., Bhamidipati, A., Punna, T., Ihmels, J., Andrews, B., Boone, C., Greenblatt, J. F., et al. (2005) · Cell · doi:10.1016/j.cell.2005.08.031 · Cited by 870
Collins, S. R., Miller, K. M., Maas, N. L., Roguev, A., Fillingham, J., Chu, C. S., Schuldiner, M., Gebbia, M., Recht, J., Shales, M., et al. (2007) · Nature · doi:10.1038/nature05649 · Cited by 869
Exploring genetic interactions and networks with yeast
Boone, C., Bussey, H., & Andrews, B. J. (2007) · Nature Reviews Genetics · doi:10.1038/nrg2085 · Cited by 661
Modular epistasis in yeast metabolism
Segrè, D., DeLuna, A., Church, G. M., & Kishony, R. (2005) · Nature Genetics · doi:10.1038/ng1489 · Cited by 575
Cited by
The Genetic Landscape of a Cell
Costanzo, M., Baryshnikova, A., Bellay, J., Kim, Y., Spear, E. D., Sevier, C. S., Ding, H., Koh, J. L. Y., Toufighi, K., Mostafavi, S., et al. (2010) · Science · doi:10.1126/science.1180823 · Cited by 2,162
Self-citationInteractome Networks and Human Disease
Vidal, M., Cusick, M. E., & Barabási, A.-L. (2011) · Cell · doi:10.1016/j.cell.2011.02.016 · Cited by 1,771
A Comprehensive Biophysical Description of Pairwise Epistasis throughout an Entire Protein Domain
Olson, C. A., Wu, N. C., & Sun, R. (2014) · Current Biology · doi:10.1016/j.cub.2014.09.072 · Cited by 408
Combinatorial CRISPR–Cas9 screens for de novo mapping of genetic interactions
Shen, J. P., Zhao, D., Sasik, R., Luebeck, J., Birmingham, A., Bojorquez-Gomez, A., Licon, K., Klepper, K., Pekin, D., Beckett, A. N., et al. (2017) · Nature Methods · doi:10.1038/nmeth.4225 · Cited by 359
“This method [1] detects and removes gRNA constructs with insufficient read coverage; [2] fits growth curves to the measured log2 abundances of each construct over time, the slopes of which reflect fitness; and [3] integrates data from the multiple gRNA constructs to derive a robust fitness value for disruption of each…”
MethodDrug interactions and the evolution of antibiotic resistance
Yeh, P. J., Hegreness, M. J., Aiden, A. P., & Kishony, R. (2009) · Nature Reviews Microbiology · doi:10.1038/nrmicro2133 · Cited by 350
The Causes and Consequences of Genetic Interactions (Epistasis)
Domingo, J., Baeza-Centurion, P., & Lehner, B. (2019) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-083118-014857 · Cited by 242
Global Genetic Networks and the Genotype-to-Phenotype Relationship
Costanzo, M., Kuzmin, E., van Leeuwen, J., Mair, B., Moffat, J., Boone, C., & Andrews, B. (2019) · Cell · doi:10.1016/j.cell.2019.01.033 · Cited by 241
eSGA: E. coli synthetic genetic array analysis
Butland, G., Babu, M., Díaz-Mejía, J. J., Bohdana, F., Phanse, S., Gold, B., Yang, W., Li, J., Gagarinova, A. G., Pogoutse, O., et al. (2008) · Nature Methods · doi:10.1038/nmeth.1239 · Cited by 256
Araya, C. L., Fowler, D. M., Chen, W., Muniez, I., Kelly, J. W., & Fields, S. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1209751109 · Cited by 230
“Guilt by Association” Is the Exception Rather Than the Rule in Gene Networks
Gillis, J., & Pavlidis, P. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002444 · Cited by 214
Dixon, S. J., Fedyshyn, Y., Koh, J. L. Y., Prasad, T. S. K., Chahwan, C., Chua, G., Toufighi, K., Baryshnikova, A., Hayles, J., Hoe, K.-L., et al. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0806261105 · Cited by 205
Mapping of signaling networks through synthetic genetic interaction analysis by RNAi
Horn, T., Sandmann, T., Fischer, B., Axelsson, E., Huber, W., & Boutros, M. (2011) · Nature Methods · doi:10.1038/nmeth.1581 · Cited by 193
Evolution of phosphoregulation: comparison of phosphorylation patterns across yeast species
Beltrao, P., Trinidad, J. C., Fiedler, D., Roguev, A., Lim, W. A., Shokat, K. M., Burlingame, A. L., & Krogan, N. J. (2009) · PLoS Biology · doi:10.1371/journal.pbio.1000134 · Cited by 188
Lehár, J., Stockwell, B. R., Giaever, G., & Nislow, C. (2008) · Nature Chemical Biology · doi:10.1038/nchembio.120 · Cited by 172
Self-citationSystems-level antimicrobial drug and drug synergy discovery
Roemer, T., & Boone, C. (2013) · Nature Chemical Biology · doi:10.1038/nchembio.1205 · Cited by 159
Kampmann, M., Bassik, M. C., & Weissman, J. S. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1307002110 · Cited by 123
Dosage suppression genetic interaction networks enhance functional wiring diagrams of the cell
Magtanong, L., Ho, C. H., Barker, S. L., Jiao, W., Baryshnikova, A., Bahr, S., Smith, A. M., Heisler, L. E., Choy, J. S., Kuzmin, E., et al. (2011) · Nature Biotechnology · doi:10.1038/nbt.1855 · Cited by 108
Self-citationThe Hsp90 chaperone network modulates Candida virulence traits
O’Meara, T. R., Robbins, N., & Cowen, L. E. (2017) · Trends in Microbiology · doi:10.1016/j.tim.2017.05.003 · Cited by 92
A transcription factor collective defines the HSN serotonergic neuron regulatory landscape
Lloret-Fernández, C., Maicas, M., Mora-Martínez, C., Artacho, A., Jimeno-Martín, Á., Chirivella, L., Weinberg, P., & Flames, N. (2018) · eLife · doi:10.7554/elife.32785 · Cited by 67
A Comparative Genomic Approach for Identifying Synthetic Lethal Interactions in Human Cancer
Deshpande, R., Asiedu, M. K., Klebig, M., Sutor, S., Kuzmin, E., Nelson, J., Piotrowski, J., Ho Shin, S., Yoshida, M., Costanzo, M., et al. (2013) · Cancer Research · doi:10.1158/0008-5472.can-12-3956 · Cited by 64
Pervasive antagonistic interactions among hybrid incompatibility loci
Guerrero, R. F., Muir, C. D., Josway, S., & Moyle, L. C. (2017) · PLoS Genetics · doi:10.1371/journal.pgen.1006817 · Cited by 48
Prediction of Genetic Interactions Using Machine Learning and Network Properties
Madhukar, N. S., Elemento, O., & Pandey, G. (2015) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2015.00172 · Cited by 39
Metadata
- Authors
- Mani, R., St.Onge, R. P., Hartman, J. L., Giaever, G., & Roth, F. P.
- Venue
- Proceedings of the National Academy of Sciences
- Published
- 2008-03-04
- Volume
- 105
- Issue
- 9
- Pages
- 3461-3466
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- OpenAlex
- W2130139983
- Cited by
- 473
- References
- 7
- FWCI
- 10.55
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
