Data from: A mathematical framework for the quantitative analysis of genetic buffering
Data files
Jun 17, 2025 version files 2.15 GB
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README.md
5.12 KB
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S1_Data.csv
1.05 GB
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S2_Data.csv
1.08 GB
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S3_Data.csv
17.47 MB
Abstract
Genetic buffering plays a pivotal role in orchestrating the relationship between genotype and phenotype in outbred populations. While high-throughput screens have identified many instances of genetic buffering – through the detection of "synthetic lethality" or "synthetic sickness" – a formal and general method for its quantitative analysis across systems is lacking. In this report, an axiomatic mathematical framework that can be used to classify, quantify, and compare buffering relationships between genes is described. Importantly, this methodology employs a ratio scale as its basis, thereby permitting the definition of a novel neutrality model for gene interaction – the “parallel” model – which complements the commonly used “product” model. Evidence supporting the parallel model is provided through the statistical analysis of previously published yeast gene interaction data. This analysis reveals the consistent underestimation of double mutant fitness in strains carrying non-interacting query-array pairings (as predicted by the existence of “parallel” relationships between genes). Moreover, a model incorporating parallel neutrality in the determination of expected double mutant fitness largely corrects the underestimation. Finally, it is shown that simple extensions of this newly developed framework permit the unambiguous definition and classification of gene interactions in a formal, general, and mathematical way. Consequently, the concept of genetic buffering as first conceived by Leland Hartwell becomes a specific case within a comprehensive model of gene interaction.
Dataset DOI: 10.5061/dryad.8gtht7712
Description of the data and file structure
This dataset is comprised of the supporting information for the manuscript "A mathematical framework for the quantitative analysis of genetic buffering" (under review). In this manuscript a novel neutrality model for gene interaciton is proposed, the "parallel" model, which complements the commonly used "product" model. To provide support for the parallel model, published yeast gene interaction data (https://doi.org/10.1126/science.1180823) was reanalyzed to calculate e-serial (S1 Data), and e-parallel (S2 Data) values for 5,481,729 crosses derived from previously performed synthetic genetic array analysis. In addition, the standardized residuals for 77,401 non-interacting query-array gene pairs were calculated to assay for systematic errors (S3 Data).
Files and variables
File: S1_Data.csv
Description: Determination of ε-serial for 5,481,729 yeast crosses using raw data obtained from previously published yeast synthetic genetic array experiments** (https://doi.org/10.1126/science.1180823).**
Variables
- Cross #: Yeast genetic cross ID
- Query.ORF_Name: Query strain systematic ORF ID
- Array.ORF_Name: Array strain systematic ORF ID
- t.Query: Query single mutant fitness
- t.Query.SD: Query single mutant fitness standard deviation
- t.Array: Array single mutant fitness
- t.Array.SD: Array single mutant fitness standard deviation
- t.DM.actual: Double mutant fitness (observed)
- t.DM.actual.SD: Double mutant fitness standard devaition (observed)
- t.DM.expected.serial: Expected double mutant fitness (serial neutrality model)
- e.Serial.t-scale: Calculated ε-serial value
- t.DM.expected.SD.serial: Expected double mutant fitness standard deviation (serial neutrality model)
- t-test_statistic.serial: t-test statistic for the null hypothesis that there is no interaction
- degrees_of_freedom: t-test degrees of freedom
- p-value.serial: t-test p-value
- q-value.serial: t-test corrected p-value (Benjamini-Hochberg method)
File: S2_Data.csv
Description: Determination of ε-parallel for 5,481,729 yeast crosses using raw data obtained from previously published yeast synthetic genetic array experiments** (**https://doi.org/10.1126/science.1180823).
Variables
- Cross #: Yeast genetic cross ID
- Query.ORF_Name: Query strain systematic ORF ID
- Array.ORF_Name: Array strain systematic ORF ID
- t.Query: Query single mutant fitness
- t.Query.SD: Query single mutant fitness standard deviation
- t.Array: Array single mutant fitness
- t.Array.SD: Array single mutant fitness standard deviation
- t.DM.actual: Double mutant fitness (observed)
- t.DM.actual.SD: Double mutant fitness standard devaition (observed)
- t.DM.expected.parallel: Expected double mutant fitness (parallel neutrality model)
- e.Parallel.t-scale: Calculated ε-parallel value
- t.DM.expected.SD.parallel: Expected double mutant fitness standard deviation (parallel neutrality model)
- t-test_statistic.parallel: t-test statistic for the null hypothesis that there is no interaction
- degrees_of_freedom: t-test degrees of freedom
- p-value.parallel: t-test p-value
- q-value.parallel: t-test corrected p-value (Benjamini-Hochberg method)
File: S3_Data.csv
Description: Determination of the standardized residuals for 77,401 yeast crosses upon application of either the serial or parallel neutrality models. Raw data was obtained from previously published yeast synthetic genetic array experiments (https://doi.org/10.1126/science.1180823).
Variables
- Cross #: Yeast genetic cross ID
- Query.ORF_Name: Query strain systematic ORF ID
- Array.ORF_Name: Array strain systematic ORF ID
- t.Query: Query single mutant fitness
- t.Array: Array single mutant fitness
- t.DM.actual: Double mutant fitness (observed)
- t.DM.expected.serial: Expected double mutant fitness (serial neutrality model)
- t.DM.expected.parallel: Expected double mutant fitness (parallel neutrality model)
- q-value.serial: t-test corrected p-value (serial neutrality model)
- q-value.parallel: t-test corrected p-value (parallel neutrality model)
- error.serial: calculated error (serial neutrality model)
- error.parallel: calculated error (parallel neutrality model)
- error.hybrid: calculated error (hybrid model)
- standardized_residual.serial: calculated standardized residuals (serial neutrality model)
- standardized_residual.parallel: calculated standardized residuals (parallel neutrality model)
- standardized_residual.hybrid: calculated standardized residuals (hybrid neutrality model)
Code/software
Any open source .csv editor.
Access information
Data was derived from the following sources:
- M. Costanzo, et al., The genetic landscape of a cell. Science 327, 425–431 (2010)