Literature DB >> 26071534

Root phenotyping: from component trait in the lab to breeding.

René C P Kuijken1, Fred A van Eeuwijk2, Leo F M Marcelis3, Harro J Bouwmeester4.   

Abstract

In the last decade cheaper and faster sequencing methods have resulted in an enormous increase in genomic data. High throughput genotyping, genotyping by sequencing and genomic breeding are becoming a standard in plant breeding. As a result, the collection of phenotypic data is increasingly becoming a limiting factor in plant breeding. Genetic studies on root traits are being hampered by the complexity of these traits and the inaccessibility of the rhizosphere. With an increasing interest in phenotyping, breeders and scientists try to overcome these limitations, resulting in impressive developments in automated phenotyping platforms. Recently, many such platforms have been thoroughly described, yet their efficiency to increase genetic gain often remains undiscussed. This efficiency depends on the heritability of the phenotyped traits as well as the correlation of these traits with agronomically relevant breeding targets. This review provides an overview of the latest developments in root phenotyping and describes the environmental and genetic factors influencing root phenotype and heritability. It also intends to give direction to future phenotyping and breeding strategies for optimizing root system functioning. A quantitative framework to determine the efficiency of phenotyping platforms for genetic gain is described. By increasing heritability, managing effects caused by interactions between genotype and environment and by quantifying the genetic relation between traits phenotyped in platforms and ultimate breeding targets, phenotyping platforms can be utilized to their maximum potential.
© The Author 2015. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Entities:  

Keywords:  Genomic selection; heritability; phenotyping; rhizosphere; root exudation; root system architecture (RSA).

Mesh:

Year:  2015        PMID: 26071534     DOI: 10.1093/jxb/erv239

Source DB:  PubMed          Journal:  J Exp Bot        ISSN: 0022-0957            Impact factor:   6.992


  51 in total

1.  MyROOT: a method and software for the semiautomatic measurement of primary root length in Arabidopsis seedlings.

Authors:  Isabel Betegón-Putze; Alejandro González; Xavier Sevillano; David Blasco-Escámez; Ana I Caño-Delgado
Journal:  Plant J       Date:  2019-04-06       Impact factor: 6.417

2.  Hydrogel-based transparent soils for root phenotyping in vivo.

Authors:  Lin Ma; Yichao Shi; Oskar Siemianowski; Bin Yuan; Timothy K Egner; Seyed Vahid Mirnezami; Kara R Lind; Baskar Ganapathysubramanian; Vincenzo Venditti; Ludovico Cademartiri
Journal:  Proc Natl Acad Sci U S A       Date:  2019-05-14       Impact factor: 11.205

3.  Seeking stable traits to characterize the root system architecture. Study on 60 species located at two sites in natura.

Authors:  Loïc Pagès; Jocelyne Kervella
Journal:  Ann Bot       Date:  2018-06-28       Impact factor: 4.357

Review 4.  Future Challenges in Plant Systems Biology.

Authors:  Mikaël Lucas
Journal:  Methods Mol Biol       Date:  2022

5.  Simulating Root Growth as a Function of Soil Strength and Yield With a Field-Scale Crop Model Coupled With a 3D Architectural Root Model.

Authors:  Sabine Julia Seidel; Thomas Gaiser; Amit Kumar Srivastava; Daniel Leitner; Oliver Schmittmann; Miriam Athmann; Timo Kautz; Julien Guigue; Frank Ewert; Andrea Schnepf
Journal:  Front Plant Sci       Date:  2022-05-20       Impact factor: 6.627

6.  A New Phenotyping Pipeline Reveals Three Types of Lateral Roots and a Random Branching Pattern in Two Cereals.

Authors:  Sixtine Passot; Beatriz Moreno-Ortega; Daniel Moukouanga; Crispulo Balsera; Soazig Guyomarc'h; Mikael Lucas; Guillaume Lobet; Laurent Laplaze; Bertrand Muller; Yann Guédon
Journal:  Plant Physiol       Date:  2018-05-11       Impact factor: 8.340

7.  DIRT/3D: 3D root phenotyping for field-grown maize (Zea mays).

Authors:  Suxing Liu; Carlos Sherard Barrow; Meredith Hanlon; Jonathan P Lynch; Alexander Bucksch
Journal:  Plant Physiol       Date:  2021-10-05       Impact factor: 8.340

8.  Reproductive resilience but not root architecture underpins yield improvement under drought in maize.

Authors:  Carlos Messina; Dan McDonald; Hanna Poffenbarger; Randy Clark; Andrea Salinas; Yinan Fang; Carla Gho; Tom Tang; Geoff Graham; Graeme L Hammer; Mark Cooper
Journal:  J Exp Bot       Date:  2021-07-10       Impact factor: 6.992

9.  Identification of QTL regions for seedling root traits and their effect on nitrogen use efficiency in wheat (Triticum aestivum L.).

Authors:  Xiaoli Fan; Wei Zhang; Na Zhang; Mei Chen; Shusong Zheng; Chunhua Zhao; Jie Han; Jiajia Liu; Xilan Zhang; Liqiang Song; Jun Ji; Xigang Liu; Hongqing Ling; Yiping Tong; Fa Cui; Tao Wang; Junming Li
Journal:  Theor Appl Genet       Date:  2018-09-25       Impact factor: 5.699

10.  ChronoRoot: High-throughput phenotyping by deep segmentation networks reveals novel temporal parameters of plant root system architecture.

Authors:  Nicolás Gaggion; Federico Ariel; Vladimir Daric; Éric Lambert; Simon Legendre; Thomas Roulé; Alejandra Camoirano; Diego H Milone; Martin Crespi; Thomas Blein; Enzo Ferrante
Journal:  Gigascience       Date:  2021-07-20       Impact factor: 6.524

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