Literature DB >> 24895355

Independent genetic control of maize (Zea mays L.) kernel weight determination and its phenotypic plasticity.

Santiago Alvarez Prado1, Víctor O Sadras2, Lucas Borrás3.   

Abstract

Maize kernel weight (KW) is associated with the duration of the grain-filling period (GFD) and the rate of kernel biomass accumulation (KGR). It is also related to the dynamics of water and hence is physiologically linked to the maximum kernel water content (MWC), kernel desiccation rate (KDR), and moisture concentration at physiological maturity (MCPM). This work proposed that principles of phenotypic plasticity can help to consolidated the understanding of the environmental modulation and genetic control of these traits. For that purpose, a maize population of 245 recombinant inbred lines (RILs) was grown under different environmental conditions. Trait plasticity was calculated as the ratio of the variance of each RIL to the overall phenotypic variance of the population of RILs. This work found a hierarchy of plasticities: KDR ≈ GFD > MCPM > KGR > KW > MWC. There was no phenotypic and genetic correlation between traits per se and trait plasticities. MWC, the trait with the lowest plasticity, was the exception because common quantitative trait loci were found for the trait and its plasticity. Independent genetic control of a trait per se and genetic control of its plasticity is a condition for the independent evolution of traits and their plasticities. This allows breeders potentially to select for high or low plasticity in combination with high or low values of economically relevant traits.
© The Author 2014. 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:  Grain-filling duration; kernel desiccation rate; kernel growth rate; kernel weight; maximum kernel water content; moisture concentration at physiological maturity; phenotypic plasticity; quantitative trait loci.

Mesh:

Year:  2014        PMID: 24895355     DOI: 10.1093/jxb/eru215

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


  6 in total

1.  Genetic dissection of the maize kernel development process via conditional QTL mapping for three developing kernel-related traits in an immortalized F2 population.

Authors:  Zhanhui Zhang; Xiangyuan Wu; Chaonan Shi; Rongna Wang; Shengfei Li; Zhaohui Wang; Zonghua Liu; Yadong Xue; Guiliang Tang; Jihua Tang
Journal:  Mol Genet Genomics       Date:  2015-09-29       Impact factor: 3.291

2.  Genetic architecture of phenotypic means and plasticities of kernel size and weight in maize.

Authors:  Chunhui Li; Xun Wu; Yongxiang Li; Yunsu Shi; Yanchun Song; Dengfeng Zhang; Yu Li; Tianyu Wang
Journal:  Theor Appl Genet       Date:  2019-09-25       Impact factor: 5.699

3.  High-throughput method for ear phenotyping and kernel weight estimation in maize using ear digital imaging.

Authors:  R Makanza; M Zaman-Allah; J E Cairns; J Eyre; J Burgueño; Ángela Pacheco; C Diepenbrock; C Magorokosho; A Tarekegne; M Olsen; B M Prasanna
Journal:  Plant Methods       Date:  2018-06-15       Impact factor: 4.993

Review 4.  Genetic Architecture of Grain Yield-Related Traits in Sorghum and Maize.

Authors:  Wodajo Baye; Qi Xie; Peng Xie
Journal:  Int J Mol Sci       Date:  2022-02-22       Impact factor: 5.923

Review 5.  Explaining pre-emptive acclimation by linking information to plant phenotype.

Authors:  Pedro J Aphalo; Victor O Sadras
Journal:  J Exp Bot       Date:  2022-09-03       Impact factor: 7.298

6.  Large-scale GWAS in sorghum reveals common genetic control of grain size among cereals.

Authors:  Yongfu Tao; Xianrong Zhao; Xuemin Wang; Adrian Hathorn; Colleen Hunt; Alan W Cruickshank; Erik J van Oosterom; Ian D Godwin; Emma S Mace; David R Jordan
Journal:  Plant Biotechnol J       Date:  2019-11-11       Impact factor: 9.803

  6 in total

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