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Genetic interactions matter more in less-optimal environments: a Focused Review of “Phenotype uniformity in combined-stress environments has a different genetic architecture than in single-stress treatments” (Makumburage and Stapleton, 2011)

Identifieur interne : 000199 ( Pmc/Checkpoint ); précédent : 000198; suivant : 000200

Genetic interactions matter more in less-optimal environments: a Focused Review of “Phenotype uniformity in combined-stress environments has a different genetic architecture than in single-stress treatments” (Makumburage and Stapleton, 2011)

Auteurs : Dustin A. Landers ; Ann E. Stapleton

Source :

RBID : PMC:4127526

Abstract

An increase in the distribution of data points indicates the presence of genetic or environmental modifiers. Mapping of the genetic control of the spread of points, the uniformity, allows us to allocate genetic difference in point distribution to adjacent, cis effects or to independently segregating, trans genetic effects. Our genetic architecture-mapping experiment elucidated the “environmental context specificity” of modifiers, the number and effect size of positive and negative alleles important for uniformity in single and combined stress, and the extent of additivity in estimated allele effects in combined stress environments. We found no alleles for low uniformity in combined stress treatments in the maize mapping population we examined. The major advances in this research area since early 2011 have been in improved methods for modeling of distributions and means and detection of important loci. Double hierarchical general linear models and, more recently, a likelihood ratio formulation have been developed to better model and estimate the genetic and environmental effects in populations. These new methods have been applied to real data sets by the method authors and we now encourage additional development of the software and wider application of the methods. We also propose that simulations of genetic regulatory network models to examine differences in uniformity and systematic exploration of models using shared simulations across communities of researchers would be constructive avenues for developing further insight into the genetic mechanisms of variation control.


Url:
DOI: 10.3389/fpls.2014.00384
PubMed: 25157259
PubMed Central: 4127526


Affiliations:


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<given-names>Dustin A.</given-names>
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<xref ref-type="author-notes" rid="fn001">
<sup>*</sup>
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<institution>Department of Biology and Marine Biology, University of North Carolina Wilmington</institution>
<country>Wilmington, NC, USA</country>
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<p>Edited by: Shawn Kaeppler, University of Wisconsin-Madison, USA</p>
</fn>
<fn fn-type="edited-by">
<p>Reviewed by: Seth C. Murray, Texas A&M University, USA; Elhan Ersoz, Syngenta Biotechnology, USA</p>
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<corresp id="fn001">*Correspondence:
<email xlink:type="simple">stapletona@uncw.edu</email>
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<p>This article was submitted to the journal Frontiers in Plant Science.</p>
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<month>8</month>
<year>2014</year>
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<date date-type="received">
<day>18</day>
<month>2</month>
<year>2014</year>
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<date date-type="accepted">
<day>18</day>
<month>7</month>
<year>2014</year>
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<copyright-statement>Copyright © 2014 Landers and Stapleton.</copyright-statement>
<copyright-year>2014</copyright-year>
<license license-type="open-access" xlink:href="http://creativecommons.org/licenses/by/3.0/">
<license-p>This 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.</license-p>
</license>
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<abstract>
<p>An increase in the distribution of data points indicates the presence of genetic or environmental modifiers. Mapping of the genetic control of the spread of points, the uniformity, allows us to allocate genetic difference in point distribution to adjacent, cis effects or to independently segregating, trans genetic effects. Our genetic architecture-mapping experiment elucidated the “environmental context specificity” of modifiers, the number and effect size of positive and negative alleles important for uniformity in single and combined stress, and the extent of additivity in estimated allele effects in combined stress environments. We found no alleles for low uniformity in combined stress treatments in the maize mapping population we examined. The major advances in this research area since early 2011 have been in improved methods for modeling of distributions and means and detection of important loci. Double hierarchical general linear models and, more recently, a likelihood ratio formulation have been developed to better model and estimate the genetic and environmental effects in populations. These new methods have been applied to real data sets by the method authors and we now encourage additional development of the software and wider application of the methods. We also propose that simulations of genetic regulatory network models to examine differences in uniformity and systematic exploration of models using shared simulations across communities of researchers would be constructive avenues for developing further insight into the genetic mechanisms of variation control.</p>
</abstract>
<kwd-group>
<kwd>QTL</kwd>
<kwd>genotype-environment interaction</kwd>
<kwd>modifier</kwd>
<kwd>uniformity</kwd>
<kwd>variance heterogeneity</kwd>
<kwd>combined stress effects</kwd>
<kwd>abiotic stress</kwd>
<kwd>crop</kwd>
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