Literature DB >> 34362301

Identification and allele mining of new candidate genes underlying rice grain weight and grain shape by genome-wide association study.

Yanan Niu1,2, Tianxiao Chen1,2,3, Chunchao Wang1, Kai Chen3, Congcong Shen3, Huizhen Chen3,4, Shuangbing Zhu1, Zhichao Wu1, Tianqing Zheng1, Fan Zhang5, Jianlong Xu6,7.   

Abstract

BACKGROUND: Grain weight and grain shape are important agronomic traits that affect the grain yield potential and grain quality of rice. Both grain weight and grain shape are controlled by multiple genes. The 3,000 Rice Genomes Project (3 K RGP) greatly facilitates the discovery of agriculturally important genetic variants and germplasm resources for grain weight and grain shape. <br> RESULTS: Abundant natural variations and distinct phenotic differentiation among the subgroups in grain weight and grain shape were observed in a large population of 2,453 accessions from the 3 K RGP. A total of 21 stable quantitative trait nucleotides (QTNs) for the four traits were consistently identified in at least two of 3-year trials by genome-wide association study (GWAS), including six new QTNs (qTGW3.1, qTGW9, qTGW11, qGL4/qRLW4, qGL10, and qRLW1) for grain weight and grain shape. We further predicted seven candidate genes (Os03g0186600, Os09g0544400, Os11g0163600, Os04g0580700, Os10g0399700, Os10g0400100 and Os01g0171000) for the six new QTNs by high-density association and gene-based haplotype analyses. The favorable haplotypes of the seven candidate genes and five previously cloned genes in elite accessions with high TGW and RLW are also provided. <br> CONCLUSIONS: Our results deepen the understanding of the genetic basis of grain weight and grain shape in rice and provide valuable information for improving rice grain yield and grain quality through molecular breeding.
© 2021. The Author(s).

Entities:  

Keywords:  Favorable haplotype; Grain shape; Grain weight; Quantitative trait nucleotides (QTNs); Rice germplasm

Year:  2021        PMID: 34362301     DOI: 10.1186/s12864-021-07901-x

Source DB:  PubMed          Journal:  BMC Genomics        ISSN: 1471-2164            Impact factor:   3.969


  46 in total

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Authors:  Shaokui Wang; Kun Wu; Qingbo Yuan; Xueying Liu; Zhengbin Liu; Xiaoyan Lin; Ruizhen Zeng; Haitao Zhu; Guojun Dong; Qian Qian; Guiquan Zhang; Xiangdong Fu
Journal:  Nat Genet       Date:  2012-06-24       Impact factor: 38.330

2.  A QTL for rice grain width and weight encodes a previously unknown RING-type E3 ubiquitin ligase.

Authors:  Xian-Jun Song; Wei Huang; Min Shi; Mei-Zhen Zhu; Hong-Xuan Lin
Journal:  Nat Genet       Date:  2007-04-08       Impact factor: 38.330

3.  Dissecting the Genetic Basis of Grain Shape and Chalkiness Traits in Hybrid Rice Using Multiple Collaborative Populations.

Authors:  Junyi Gong; Jiashun Miao; Yan Zhao; Qiang Zhao; Qi Feng; Qilin Zhan; Benyi Cheng; Junhui Xia; Xuehui Huang; Shihua Yang; Bin Han
Journal:  Mol Plant       Date:  2017-08-10       Impact factor: 13.164

4.  The OsSPL16-GW7 regulatory module determines grain shape and simultaneously improves rice yield and grain quality.

Authors:  Shaokui Wang; Shan Li; Qian Liu; Kun Wu; Jianqing Zhang; Shuansuo Wang; Yi Wang; Xiangbin Chen; Yi Zhang; Caixia Gao; Feng Wang; Haixiang Huang; Xiangdong Fu
Journal:  Nat Genet       Date:  2015-07-06       Impact factor: 38.330

5.  Copy number variation at the GL7 locus contributes to grain size diversity in rice.

Authors:  Yuexing Wang; Guosheng Xiong; Jiang Hu; Liang Jiang; Hong Yu; Jie Xu; Yunxia Fang; Longjun Zeng; Erbo Xu; Jing Xu; Weijun Ye; Xiangbing Meng; Ruifang Liu; Hongqi Chen; Yanhui Jing; Yonghong Wang; Xudong Zhu; Jiayang Li; Qian Qian
Journal:  Nat Genet       Date:  2015-07-06       Impact factor: 38.330

Review 6.  Genetic bases of rice grain shape: so many genes, so little known.

Authors:  Rongyu Huang; Liangrong Jiang; Jingsheng Zheng; Tiansheng Wang; Houcong Wang; Yumin Huang; Zonglie Hong
Journal:  Trends Plant Sci       Date:  2012-12-04       Impact factor: 18.313

7.  GRAIN SIZE AND NUMBER1 Negatively Regulates the OsMKKK10-OsMKK4-OsMPK6 Cascade to Coordinate the Trade-off between Grain Number per Panicle and Grain Size in Rice.

Authors:  Tao Guo; Ke Chen; Nai-Qian Dong; Chuan-Lin Shi; Wang-Wei Ye; Ji-Ping Gao; Jun-Xiang Shan; Hong-Xuan Lin
Journal:  Plant Cell       Date:  2018-03-27       Impact factor: 11.277

8.  Yield Trends Are Insufficient to Double Global Crop Production by 2050.

Authors:  Deepak K Ray; Nathaniel D Mueller; Paul C West; Jonathan A Foley
Journal:  PLoS One       Date:  2013-06-19       Impact factor: 3.240

9.  GS9 acts as a transcriptional activator to regulate rice grain shape and appearance quality.

Authors:  Dong-Sheng Zhao; Qian-Feng Li; Chang-Quan Zhang; Chen Zhang; Qing-Qing Yang; Li-Xu Pan; Xin-Yu Ren; Jun Lu; Ming-Hong Gu; Qiao-Quan Liu
Journal:  Nat Commun       Date:  2018-03-27       Impact factor: 14.919

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  4 in total

1.  Whole genome sequencing of ASD 16 and ADT 43 to identify predominant grain size and starch associated alleles in rice.

Authors:  Jayakanthan Mannu; Abillasha Mohan Latha; Shalini Rajagopal; Hari Dharani A Lalitha; Raveendran Muthurajan; Arul Loganathan; Mohankumar Subbarayalu; Gnanam Ramasamy; Ramalingam Jegadeesan
Journal:  Mol Biol Rep       Date:  2022-10-06       Impact factor: 2.742

2.  Understanding complex genetic architecture of rice grain weight through QTL-meta analysis and candidate gene identification.

Authors:  C Anilkumar; Rameswar Prasad Sah; T P Muhammed Azharudheen; Sasmita Behera; Namita Singh; Nitish Ranjan Prakash; N C Sunitha; B N Devanna; B C Marndi; B C Patra; Sunil Kumar Nair
Journal:  Sci Rep       Date:  2022-08-16       Impact factor: 4.996

3.  Identification of QTL for Stem Traits in Wheat (Triticum aestivum L.).

Authors:  Yanan Niu; Tianxiao Chen; Chenchen Zhao; Ce Guo; Meixue Zhou
Journal:  Front Plant Sci       Date:  2022-07-14       Impact factor: 6.627

4.  Identification of QTLs for rice grain size and weight by high-throughput SNP markers in the IR64 x Sadri population.

Authors:  Kashif Aslam; Shahzad Amir Naveed; Muhammad Sabar; Ghulam Shabir; Shahid Masood Shah; Abdul Rehman Khan; Muhammad Musaddiq Shah; Sajid Fiaz; Jianlong Xu; Muhammad Arif
Journal:  Front Genet       Date:  2022-08-19       Impact factor: 4.772

  4 in total

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