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by claude@2026-07, 2026-07-16
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The paper studies how genetic variation maps to phenotypes under influences including epistasis and environmental modulation, using a unified decision-tree framework (classification and regression trees) to model genotype–phenotype relationships. It applies CART across multiple protein fitness landscapes in diverse systems—fluorescent proteins from Entacmaea quadricolor, antifolate resistance in Plasmodium falciparum DHFR under drug gradients, allelic variants from the E. coli LTEE, drug-resistance phenotypes modulated by proteostasis in bacterial DHFR orthologues, and sesquiterpene synthase chemotypic diversification in Nicotiana tabacum. The key finding is that decision trees can capture higher-order mutation–environment interactions, revealing nonlinear, contingent dependencies that parametric approaches may miss. The paper explicitly notes it is a preprint and not peer reviewed, and it does not discuss endometriosis or adenomyosis within the provided text; it was included in the corpus via a keyword match in the upstream search index.
Abstract
Understanding how genetic variation translates into phenotypic outcomes is central to various sub-fields of genetics. This task is complicated by a range of forces–including epistasis, environmental modulation of mutation effects, and ecological influences–that complicate the process of mapping from genotype to phenotype. In this study, we apply a unified decision tree approach, classification and regression trees (CART), to model genotype-phenotype relationships across protein fitness landscapes across a diversity of organisms: (i) a fluorescent protein isolated from Entacmaea quadricolor (bubble-tip anemone), (ii) antifolate resistance in Plasmodium falciparum (malaria parasite) dihydrofolate reductase (DHFR) under drug concentration gradients, (iii) allelic variants from the long-term evolution experiment (LTEE) in Escherichia coli, (iv) proteostasis-modulated drug resistance phenotypes in three bacterial orthologues of DHFR, and (v) chemotypic diversification of sesquiterpene synthases in Nicotiana tabacum (cultivated tobacco). Our results demonstrate that decision trees can effectively capture higher-order interactions between mutations and environments, uncovering nonlinear dependencies and contingencies that are often missed by traditional parametric models. By enabling clear visualization of interaction hierarchies, CART serves as both a predictive tool and an explanatory framework for genotype-phenotype mapping. This approach has use cases across the spectrum, from resolving the genomic architecture of biological traits, to personalized medicine, and varied applications in bioengineering.
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This is a Preprint and has not been peer reviewed. This is version 1 of this Preprint.
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Understanding how genetic variation translates into phenotypic outcomes is central to various sub-fields of genetics. This task is complicated by a range of forces–including epistasis, environmental modulation of mutation effects, and ecological influences–that complicate the process of mapping from genotype to phenotype. In this study, we apply a unified decision tree approach, classification and regression trees (CART), to model genotype-phenotype relationships across protein fitness landscapes across a diversity of organisms: (i) a fluorescent protein isolated from Entacmaea quadricolor (bubble-tip anemone), (ii) antifolate resistance in Plasmodium falciparum (malaria parasite) dihydrofolate reductase (DHFR) under drug concentration gradients, (iii) allelic variants from the long-term evolution experiment (LTEE) in Escherichia coli, (iv) proteostasis-modulated drug resistance phenotypes in three bacterial orthologues of DHFR, and (v) chemotypic diversification of sesquiterpene
synthases in Nicotiana tabacum (cultivated tobacco). Our results demonstrate that decision trees can effectively capture higher-order interactions between mutations and environments, uncovering nonlinear dependencies and contingencies that are often missed by traditional parametric models. By enabling clear visualization of interaction hierarchies, CART serves as both a predictive tool and an explanatory framework for genotype-phenotype mapping. This approach has use cases across the spectrum, from resolving the genomic architecture of biological traits, to personalized medicine, and varied applications in bioengineering.
https://doi.org/10.32942/X2N643
Life Sciences
Population genetics, machine learning, Epistasis, environmental epistasis
Published: 2025-09-23 15:37
Last Updated: 2025-09-23 15:37
CC-By Attribution-NonCommercial-NoDerivatives 4.0 International
Data and Code Availability Statement:
https://github.com/OgPlexus/Cartepistasis1
Language:
English
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