Literature DB >> 31863176

Effect of multi-allele combination on rice grain size based on prediction of regression equation model.

Hua Zhong1, Chang Liu1, Weilong Kong1, Yue Zhang1, Gangqing Zhao1, Tong Sun1, Yangsheng Li2.   

Abstract

Rice yield potential is partially affected by grain size and weight, which associates with a great number of genes and QTLs. However, it is still unclear that how multiple alleles in different genes take a combined effect on grain shape/size. Here, we investigated seven core grain size-related functional genes (GL7, GS3, GW8, GS5, TGW6, WTG1, and An-1) and observed a wide phenotypic variation for five agronomic traits (grain length, grain width, grain length-width ratio, grain thickness and thousand-grain weight) in 521 rice germplasm. The correlation analysis showed a strong association among these grain traits which have distinct impacts on determining the final rice grain size. Genotyping analysis demonstrated that a relatively small number of allele combinations were preserved in the diverse population and these allele combinations were significantly associated with differences in grain size. Furthermore, alleles were regarded as individual variables to develop the multiple regression equation. We found that B and C allelic types of GS3 and conventional type of WTG1 played relevant roles in grain size and thousand-grain weight, separately. The models would conduce to devise instructive approaches by selecting appropriate candidate alleles, which could fuel further research for breeding preferred grain shape and high-yielding crop.

Entities:  

Keywords:  Grain shape; Multiple allele combination; Regression equation model; Rice; Yield

Mesh:

Substances:

Year:  2019        PMID: 31863176     DOI: 10.1007/s00438-019-01627-y

Source DB:  PubMed          Journal:  Mol Genet Genomics        ISSN: 1617-4623            Impact factor:   3.291


  23 in total

1.  Natural variation in GS5 plays an important role in regulating grain size and yield in rice.

Authors:  Yibo Li; Chuchuan Fan; Yongzhong Xing; Yunhe Jiang; Lijun Luo; Liang Sun; Di Shao; Chunjue Xu; Xianghua Li; Jinghua Xiao; Yuqing He; Qifa Zhang
Journal:  Nat Genet       Date:  2011-10-23       Impact factor: 38.330

2.  GS3, a major QTL for grain length and weight and minor QTL for grain width and thickness in rice, encodes a putative transmembrane protein.

Authors:  Chuchuan Fan; Yongzhong Xing; Hailiang Mao; Tingting Lu; Bin Han; Caiguo Xu; Xianghua Li; Qifa Zhang
Journal:  Theor Appl Genet       Date:  2006-02-02       Impact factor: 5.699

Review 3.  Genes offering the potential for designing yield-related traits in rice.

Authors:  Mayuko Ikeda; Kotaro Miura; Koichiro Aya; Hidemi Kitano; Makoto Matsuoka
Journal:  Curr Opin Plant Biol       Date:  2013-03-01       Impact factor: 7.834

4.  Loss of function of the IAA-glucose hydrolase gene TGW6 enhances rice grain weight and increases yield.

Authors:  Ken Ishimaru; Naoki Hirotsu; Yuka Madoka; Naomi Murakami; Nao Hara; Haruko Onodera; Takayuki Kashiwagi; Kazuhiro Ujiie; Bun-Ichi Shimizu; Atsuko Onishi; Hisashi Miyagawa; Etsuko Katoh
Journal:  Nat Genet       Date:  2013-04-14       Impact factor: 38.330

Review 5.  Molecular genetic dissection of quantitative trait loci regulating rice grain size.

Authors:  Jianru Zuo; Jiayang Li
Journal:  Annu Rev Genet       Date:  2014-08-18       Impact factor: 16.830

6.  An-1 encodes a basic helix-loop-helix protein that regulates awn development, grain size, and grain number in rice.

Authors:  Jianghong Luo; Hui Liu; Taoying Zhou; Benguo Gu; Xuehui Huang; Yingying Shangguan; Jingjie Zhu; Yan Li; Yan Zhao; Yongchun Wang; Qiang Zhao; Ahong Wang; Ziqun Wang; Tao Sang; Zixuan Wang; Bin Han
Journal:  Plant Cell       Date:  2013-09-27       Impact factor: 11.277

Review 7.  Breeding technologies to increase crop production in a changing world.

Authors:  Mark Tester; Peter Langridge
Journal:  Science       Date:  2010-02-12       Impact factor: 47.728

8.  A causal C-A mutation in the second exon of GS3 highly associated with rice grain length and validated as a functional marker.

Authors:  Chuchuan Fan; Sibin Yu; Chongrong Wang; Yongzhong Xing
Journal:  Theor Appl Genet       Date:  2008-11-20       Impact factor: 5.699

9.  Influence of Multi-Gene Allele Combinations on Grain Size of Rice and Development of a Regression Equation Model to Predict Grain Parameters.

Authors:  Chan-Mi Lee; Jonghwa Park; Backki Kim; Jeonghwan Seo; Gileung Lee; Su Jang; Hee-Jong Koh
Journal:  Rice (N Y)       Date:  2015-10-30       Impact factor: 4.783

10.  Multiple and independent origins of short seeded alleles of GS3 in rice.

Authors:  Noriko Takano-Kai; Hui Jiang; Adrian Powell; Susan McCouch; Itsuro Takamure; Naruto Furuya; Kazuyuki Doi; Atsushi Yoshimura
Journal:  Breed Sci       Date:  2013-03-01       Impact factor: 2.086

View more
  1 in total

1.  Research on Rice Yield Prediction Model Based on Deep Learning.

Authors:  Xiao Han; Fangbiao Liu; Xiaoliang He; Fenglou Ling
Journal:  Comput Intell Neurosci       Date:  2022-04-26
  1 in total

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