A mathematical framework for the quantitative analysis of genetic buffering

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-interacti..., , # A mathematical framework for the quantitative analysis of genetic buffering

Dataset DOI: [10.5061/dryad.8gtht7712](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](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).

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