Literature DB >> 23543351

An allometric model for mapping seed development in plants.

Zhongwen Huang, Chunfa Tong, Wenhao Bo, Xiaoming Pang, Zhong Wang, Jichen Xu, Junyi Gai, Rongling Wu.   

Abstract

Despite a tremendous effort to map quantitative trait loci (QTLs) responsible for agriculturally and biologically important traits in plants, our understanding of how a QTL governs the developmental process of plant seeds remains elusive. In this article, we address this issue by describing a model for functional mapping of seed development through the incorporation of the relationship between vegetative and reproductive growth. The time difference of reproductive from vegetative growth is described by Reeve and Huxley’s allometric equation. Thus, the implementation of this equation into the framework of functional mapping allows dynamic QTLs for seed development to be identified more precisely. By estimating and testing mathematical parameters that define Reeve and Huxley’s allometric equations of seed growth, the dynamic pattern of the genetic effects of the QTLs identified can be analyzed. We used the model to analyze a soybean data, leading to the detection of QTLs that control the growth of seed dry weight. Three dynamic QTLs, located in two different linkage groups, were detected to affect growth curves of seed dry weight. The QTLs detected may be used to improve seed yield with marker-assisted selection by altering the pattern of seed development in a hope to achieve a maximum size of seeds at a harvest time.

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Year:  2014        PMID: 23543351     DOI: 10.1093/bib/bbt019

Source DB:  PubMed          Journal:  Brief Bioinform        ISSN: 1467-5463            Impact factor:   11.622


  5 in total

1.  Composite Interval Mapping Based on Lattice Design for Error Control May Increase Power of Quantitative Trait Locus Detection.

Authors:  Jianbo He; Jijie Li; Zhongwen Huang; Tuanjie Zhao; Guangnan Xing; Junyi Gai; Rongzhan Guan
Journal:  PLoS One       Date:  2015-06-15       Impact factor: 3.240

2.  A Computational Model for Inferring QTL Control Networks Underlying Developmental Covariation.

Authors:  Libo Jiang; Hexin Shi; Mengmeng Sang; Chenfei Zheng; Yige Cao; Xuli Zhu; Xiaokang Zhuo; Tangren Cheng; Qixiang Zhang; Rongling Wu; Lidan Sun
Journal:  Front Plant Sci       Date:  2019-12-18       Impact factor: 5.753

3.  The genetic control of leaf allometry in the common bean, Phaseolus vulgaris.

Authors:  Miaomiao Zhang; Shilong Zhang; Meixia Ye; Libo Jiang; C Eduardo Vallejos; Rongling Wu
Journal:  BMC Genet       Date:  2020-03-14       Impact factor: 2.797

4.  MVQTLCIM: composite interval mapping of multivariate traits in a hybrid F1 population of outbred species.

Authors:  Fenxiang Liu; Chunfa Tong; Shentong Tao; Jiyan Wu; Yuhua Chen; Dan Yao; Huogen Li; Jisen Shi
Journal:  BMC Bioinformatics       Date:  2017-11-23       Impact factor: 3.169

5.  Model-based QTL detection is sensitive to slight modifications in model formulation.

Authors:  Caterina Barrasso; Mohamed-Mahmoud Memah; Michel Génard; Bénédicte Quilot-Turion
Journal:  PLoS One       Date:  2019-10-03       Impact factor: 3.240

  5 in total

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