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dc.contributor.authorMatsumoto, Tomotaka*
dc.contributor.authorMineta, Katsuhiko*
dc.contributor.authorOsada, Naoki*
dc.contributor.authorAraki, Hitoshi*
dc.date.accessioned2016-01-28T13:14:40Zen
dc.date.available2016-01-28T13:14:40Zen
dc.date.issued2015-11-24en
dc.identifier.citationAn Individual-Based Diploid Model Predicts Limited Conditions Under Which Stochastic Gene Expression Becomes Advantageous 2015, 6 Frontiers in Geneticsen
dc.identifier.issn1664-8021en
dc.identifier.pmid26635872
dc.identifier.doi10.3389/fgene.2015.00336en
dc.identifier.urihttp://hdl.handle.net/10754/595160en
dc.description.abstractRecent studies suggest the existence of a stochasticity in gene expression (SGE) in many organisms, and its non-negligible effect on their phenotype and fitness. To date, however, how SGE affects the key parameters of population genetics are not well understood. SGE can increase the phenotypic variation and act as a load for individuals, if they are at the adaptive optimum in a stable environment. On the other hand, part of the phenotypic variation caused by SGE might become advantageous if individuals at the adaptive optimum become genetically less-adaptive, for example due to an environmental change. Furthermore, SGE of unimportant genes might have little or no fitness consequences. Thus, SGE can be advantageous, disadvantageous, or selectively neutral depending on its context. In addition, there might be a genetic basis that regulates magnitude of SGE, which is often referred to as “modifier genes,” but little is known about the conditions under which such an SGE-modifier gene evolves. In the present study, we conducted individual-based computer simulations to examine these conditions in a diploid model. In the simulations, we considered a single locus that determines organismal fitness for simplicity, and that SGE on the locus creates fitness variation in a stochastic manner. We also considered another locus that modifies the magnitude of SGE. Our results suggested that SGE was always deleterious in stable environments and increased the fixation probability of deleterious mutations in this model. Even under frequently changing environmental conditions, only very strong natural selection made SGE adaptive. These results suggest that the evolution of SGE-modifier genes requires strict balance among the strength of natural selection, magnitude of SGE, and frequency of environmental changes. However, the degree of dominance affected the condition under which SGE becomes advantageous, indicating a better opportunity for the evolution of SGE in different genetic models.en
dc.language.isoenen
dc.publisherFrontiers Media SAen
dc.relation.urlhttp://journal.frontiersin.org/Article/10.3389/fgene.2015.00336/abstracten
dc.rightsThis is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.en
dc.subjectstochastic gene expressionen
dc.subjectenvironmental changeen
dc.subjectviability selectionen
dc.subjecteffective population sizeen
dc.subjectindividual-based simulationen
dc.titleAn Individual-Based Diploid Model Predicts Limited Conditions Under Which Stochastic Gene Expression Becomes Advantageousen
dc.typeArticleen
dc.contributor.departmentComputational Bioscience Research Center (CBRC)*
dc.identifier.journalFrontiers in Geneticsen
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionGraduate School of Systems Life Sciences, Kyushu University, Fukuoka, Japan*
dc.contributor.institutionDepartment of Population Genetics, National Institute of Genetics, Mishima, Japan*
dc.contributor.institutionDepartment of Genetics, SOKENDAI (The Graduate University for Advanced Studies), Mishima, Japan*
dc.contributor.institutionResearch Faculty of Agriculture, Hokkaido University, Sapporo, Japan*
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)*
kaust.authorMineta, Katsuhiko*
refterms.dateFOA2018-06-13T13:52:42Z


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