PoplarV1, Main, Exploration, bibRecord, 001225

Quantitative gene-gene and gene-environment mapping for leaf shape variation using tree-based models.

Identifieur interne : 001225 ( Main/Exploration ); précédent : 001224; suivant : 001226

Quantitative gene-gene and gene-environment mapping for leaf shape variation using tree-based models.

Auteurs : Guifang Fu [États-Unis] ; Xiaotian Dai [États-Unis] ; Jürgen Symanzik [États-Unis] ; Shaun Bushman [États-Unis]

Source :

The New phytologist [ 1469-8137 ] ; 2017.

RBID : pubmed:27650962

Descripteurs français

English descriptors

KwdEn :
- Algorithms (MeSH), Computer Simulation (MeSH), Gene-Environment Interaction (MeSH), Genes, Plant (MeSH), Genetic Pleiotropy (MeSH), Image Processing, Computer-Assisted (MeSH), Linkage Disequilibrium (genetics), Models, Genetic (MeSH), Plant Leaves (anatomy & histology), Plant Leaves (genetics), Populus (anatomy & histology), Populus (genetics), Principal Component Analysis (MeSH), Satellite Communications (MeSH), Trees (anatomy & histology), Trees (genetics).
MESH :
- anatomy & histology : Plant Leaves, Populus, Trees.
- genetics : Linkage Disequilibrium, Plant Leaves, Populus, Trees.
- Algorithms, Computer Simulation, Gene-Environment Interaction, Genes, Plant, Genetic Pleiotropy, Image Processing, Computer-Assisted, Models, Genetic, Principal Component Analysis, Satellite Communications.

Abstract

Leaf shape traits have long been a focus of many disciplines, but the complex genetic and environmental interactive mechanisms regulating leaf shape variation have not yet been investigated in detail. The question of the respective roles of genes and environment and how they interact to modulate leaf shape is a thorny evolutionary problem, and sophisticated methodology is needed to address it. In this study, we investigated a framework-level approach that inputs shape image photographs and genetic and environmental data, and then outputs the relative importance ranks of all variables after integrating shape feature extraction, dimension reduction, and tree-based statistical models. The power of the proposed framework was confirmed by simulation and a Populus szechuanica var. tibetica data set. This new methodology resulted in the detection of novel shape characteristics, and also confirmed some previous findings. The quantitative modeling of a combination of polygenetic, plastic, epistatic, and gene-environment interactive effects, as investigated in this study, will improve the discernment of quantitative leaf shape characteristics, and the methods are ready to be applied to other leaf morphology data sets. Unlike the majority of approaches in the quantitative leaf shape literature, this framework-level approach is data-driven, without assuming any pre-known shape attributes, landmarks, or model structures.

DOI: 10.1111/nph.14131
PubMed: 27650962

Affiliations:

États-Unis

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Le document en format XML

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<series><title level="j">The New phytologist</title>
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<term>Computer Simulation (MeSH)</term>
<term>Gene-Environment Interaction (MeSH)</term>
<term>Genes, Plant (MeSH)</term>
<term>Genetic Pleiotropy (MeSH)</term>
<term>Image Processing, Computer-Assisted (MeSH)</term>
<term>Linkage Disequilibrium (genetics)</term>
<term>Models, Genetic (MeSH)</term>
<term>Plant Leaves (anatomy & histology)</term>
<term>Plant Leaves (genetics)</term>
<term>Populus (anatomy & histology)</term>
<term>Populus (genetics)</term>
<term>Principal Component Analysis (MeSH)</term>
<term>Satellite Communications (MeSH)</term>
<term>Trees (anatomy & histology)</term>
<term>Trees (genetics)</term>
</keywords>
<keywords scheme="KwdFr" xml:lang="fr"><term>Algorithmes (MeSH)</term>
<term>Analyse en composantes principales (MeSH)</term>
<term>Arbres (anatomie et histologie)</term>
<term>Arbres (génétique)</term>
<term>Communications par satellite (MeSH)</term>
<term>Déséquilibre de liaison (génétique)</term>
<term>Feuilles de plante (anatomie et histologie)</term>
<term>Feuilles de plante (génétique)</term>
<term>Gènes de plante (MeSH)</term>
<term>Interaction entre gènes et environnement (MeSH)</term>
<term>Modèles génétiques (MeSH)</term>
<term>Pléiotropie (MeSH)</term>
<term>Populus (anatomie et histologie)</term>
<term>Populus (génétique)</term>
<term>Simulation numérique (MeSH)</term>
<term>Traitement d'image par ordinateur (MeSH)</term>
</keywords>
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<term>Feuilles de plante</term>
<term>Populus</term>
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<keywords scheme="MESH" qualifier="anatomy & histology" xml:lang="en"><term>Plant Leaves</term>
<term>Populus</term>
<term>Trees</term>
</keywords>
<keywords scheme="MESH" qualifier="genetics" xml:lang="en"><term>Linkage Disequilibrium</term>
<term>Plant Leaves</term>
<term>Populus</term>
<term>Trees</term>
</keywords>
<keywords scheme="MESH" qualifier="génétique" xml:lang="fr"><term>Arbres</term>
<term>Déséquilibre de liaison</term>
<term>Feuilles de plante</term>
<term>Populus</term>
</keywords>
<keywords scheme="MESH" xml:lang="en"><term>Algorithms</term>
<term>Computer Simulation</term>
<term>Gene-Environment Interaction</term>
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<term>Image Processing, Computer-Assisted</term>
<term>Models, Genetic</term>
<term>Principal Component Analysis</term>
<term>Satellite Communications</term>
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<term>Communications par satellite</term>
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<term>Modèles génétiques</term>
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<front><div type="abstract" xml:lang="en">Leaf shape traits have long been a focus of many disciplines, but the complex genetic and environmental interactive mechanisms regulating leaf shape variation have not yet been investigated in detail. The question of the respective roles of genes and environment and how they interact to modulate leaf shape is a thorny evolutionary problem, and sophisticated methodology is needed to address it. In this study, we investigated a framework-level approach that inputs shape image photographs and genetic and environmental data, and then outputs the relative importance ranks of all variables after integrating shape feature extraction, dimension reduction, and tree-based statistical models. The power of the proposed framework was confirmed by simulation and a Populus szechuanica var. tibetica data set. This new methodology resulted in the detection of novel shape characteristics, and also confirmed some previous findings. The quantitative modeling of a combination of polygenetic, plastic, epistatic, and gene-environment interactive effects, as investigated in this study, will improve the discernment of quantitative leaf shape characteristics, and the methods are ready to be applied to other leaf morphology data sets. Unlike the majority of approaches in the quantitative leaf shape literature, this framework-level approach is data-driven, without assuming any pre-known shape attributes, landmarks, or model structures.</div>
</front>
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<CopyrightInformation>© 2016 The Authors. New Phytologist © 2016 New Phytologist Trust.</CopyrightInformation>
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Quantitative gene-gene and gene-environment mapping for leaf shape variation using tree-based models.

Quantitative gene-gene and gene-environment mapping for leaf shape variation using tree-based models.

Source :

Descripteurs français

English descriptors

Abstract

Links toward previous steps (curation, corpus...)

Le document en format XML

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