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An Automated Image Analysis Pipeline Enables Genetic Studies of Shoot and Root Morphology in Carrot (Daucus carota L.)

Carrot is a globally important crop, yet efficient and accurate methods for quantifying its most important agronomic traits are lacking. To address this problem, we developed an automated image analysis platform that extracts components of size and shape for carrot shoots and roots, which are necess...

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Autores principales: Turner, Sarah D., Ellison, Shelby L., Senalik, Douglas A., Simon, Philipp W., Spalding, Edgar P., Miller, Nathan D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6277879/
https://www.ncbi.nlm.nih.gov/pubmed/30542356
http://dx.doi.org/10.3389/fpls.2018.01703
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author Turner, Sarah D.
Ellison, Shelby L.
Senalik, Douglas A.
Simon, Philipp W.
Spalding, Edgar P.
Miller, Nathan D.
author_facet Turner, Sarah D.
Ellison, Shelby L.
Senalik, Douglas A.
Simon, Philipp W.
Spalding, Edgar P.
Miller, Nathan D.
author_sort Turner, Sarah D.
collection PubMed
description Carrot is a globally important crop, yet efficient and accurate methods for quantifying its most important agronomic traits are lacking. To address this problem, we developed an automated image analysis platform that extracts components of size and shape for carrot shoots and roots, which are necessary to advance carrot breeding and genetics. This method reliably measured variation in shoot size and shape, petiole number, petiole length, and petiole width as evidenced by high correlations with hundreds of manual measurements. Similarly, root length and biomass were accurately measured from the images. This platform also quantified shoot and root shapes in terms of principal components, which do not have traditional, manually measurable equivalents. We applied the pipeline in a study of a six-parent diallel population and an F(2) mapping population consisting of 316 individuals. We found high levels of repeatability within a growing environment, with low to moderate repeatability across environments. We also observed co-localization of quantitative trait loci for shoot and root characteristics on chromosomes 1, 2, and 7, suggesting these traits are controlled by genetic linkage and/or pleiotropy. By increasing the number of individuals and phenotypes that can be reliably quantified, the development of a rapid, automated image analysis pipeline to measure carrot shoot and root morphology will expand the scope and scale of breeding and genetic studies.
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spelling pubmed-62778792018-12-12 An Automated Image Analysis Pipeline Enables Genetic Studies of Shoot and Root Morphology in Carrot (Daucus carota L.) Turner, Sarah D. Ellison, Shelby L. Senalik, Douglas A. Simon, Philipp W. Spalding, Edgar P. Miller, Nathan D. Front Plant Sci Plant Science Carrot is a globally important crop, yet efficient and accurate methods for quantifying its most important agronomic traits are lacking. To address this problem, we developed an automated image analysis platform that extracts components of size and shape for carrot shoots and roots, which are necessary to advance carrot breeding and genetics. This method reliably measured variation in shoot size and shape, petiole number, petiole length, and petiole width as evidenced by high correlations with hundreds of manual measurements. Similarly, root length and biomass were accurately measured from the images. This platform also quantified shoot and root shapes in terms of principal components, which do not have traditional, manually measurable equivalents. We applied the pipeline in a study of a six-parent diallel population and an F(2) mapping population consisting of 316 individuals. We found high levels of repeatability within a growing environment, with low to moderate repeatability across environments. We also observed co-localization of quantitative trait loci for shoot and root characteristics on chromosomes 1, 2, and 7, suggesting these traits are controlled by genetic linkage and/or pleiotropy. By increasing the number of individuals and phenotypes that can be reliably quantified, the development of a rapid, automated image analysis pipeline to measure carrot shoot and root morphology will expand the scope and scale of breeding and genetic studies. Frontiers Media S.A. 2018-11-27 /pmc/articles/PMC6277879/ /pubmed/30542356 http://dx.doi.org/10.3389/fpls.2018.01703 Text en Copyright © 2018 Turner, Ellison, Senalik, Simon, Spalding and Miller. http://creativecommons.org/licenses/by/4.0/ 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) and the copyright owner(s) 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.
spellingShingle Plant Science
Turner, Sarah D.
Ellison, Shelby L.
Senalik, Douglas A.
Simon, Philipp W.
Spalding, Edgar P.
Miller, Nathan D.
An Automated Image Analysis Pipeline Enables Genetic Studies of Shoot and Root Morphology in Carrot (Daucus carota L.)
title An Automated Image Analysis Pipeline Enables Genetic Studies of Shoot and Root Morphology in Carrot (Daucus carota L.)
title_full An Automated Image Analysis Pipeline Enables Genetic Studies of Shoot and Root Morphology in Carrot (Daucus carota L.)
title_fullStr An Automated Image Analysis Pipeline Enables Genetic Studies of Shoot and Root Morphology in Carrot (Daucus carota L.)
title_full_unstemmed An Automated Image Analysis Pipeline Enables Genetic Studies of Shoot and Root Morphology in Carrot (Daucus carota L.)
title_short An Automated Image Analysis Pipeline Enables Genetic Studies of Shoot and Root Morphology in Carrot (Daucus carota L.)
title_sort automated image analysis pipeline enables genetic studies of shoot and root morphology in carrot (daucus carota l.)
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6277879/
https://www.ncbi.nlm.nih.gov/pubmed/30542356
http://dx.doi.org/10.3389/fpls.2018.01703
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