A Mathematical Framework for the Quantitative Analysis of Genetic Buffering
Jim Karagiannis.
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.
Introduction
Predicting the relationship between genotypic variation and phenotypic variation is a fundamental goal of the discipline of genetics. From the “modern synthesis” that united Darwinian selection and Mendelian genetics in the early part of the 20th century, to the genomic revolution of the early 21 st century, the discipline’s inability to reliably delineate the pathway from genotype to phenotype has hampered progress in fields ranging from evolutionary biology to the study of human disease.
Methods:
The resulting distributions of standardized residual data were plotted as probability distribution functions, cumulative distributions functions, or as standardized residual plots versus predicted fitness (using Mathematica 14.0). A “hybrid” neutrality model (see Results) was also applied where the individual prediction with the highest q-value (based on the application of the serial or parallel model) was used to create the “hybrid” probability distribution function, cumulative distribution function, or standardized residual plot.
Discussion
In the preceding sections, a formal axiomatic framework for the quantitative analysis of genetic buffering was presented (using the work of Schmitt as a foundation). Simple extensions of this framework provide a means to quantitatively describe the phenotypic effects of individual mutant alleles as well as interactions between alleles in a formal, general, and mathematical way.
Citation: Karagiannis J (2025) A mathematical framework for the quantitative analysis of genetic buffering. PLoS Genet 21(6): e1011730. https://doi.org/10.1371/journal.pgen.1011730
Editor: Heather J. Cordell, Newcastle University, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
Received: December 16, 2024; Accepted: May 15, 2025; Published: June 10, 2025.
Copyright: © 2025 Jim Karagiannis. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The data that support the findings of this study are openly available in the Dryad research data repository at https://doi.org/10.5061/dryad.8gtht7712.
Funding: This work was supported by the Natural Sciences and Engineering Research Council (NSERC, https://www.nserc-crsng.gc.ca/index_eng.asp) (R4029A03 to JK). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
