Rapid prediction of head rice yield and grain shape for genome-wide association study in indica rice

2020 ◽  
Vol 96 ◽  
pp. 103091
Author(s):  
Chang Liu ◽  
Jiling Song ◽  
Yuechan Wang ◽  
Xirui Huang ◽  
Fan Zhang ◽  
...  
PLoS ONE ◽  
2015 ◽  
Vol 10 (12) ◽  
pp. e0145577 ◽  
Author(s):  
Xianjin Qiu ◽  
Yunlong Pang ◽  
Zhihua Yuan ◽  
Danying Xing ◽  
Jianlong Xu ◽  
...  

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yanan Niu ◽  
Tianxiao Chen ◽  
Chunchao Wang ◽  
Kai Chen ◽  
Congcong Shen ◽  
...  

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. 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. 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 ◽  
Vol 11 (1) ◽  
Author(s):  
Jing Su ◽  
Kai Xu ◽  
Zirong Li ◽  
Yuan Hu ◽  
Zhongli Hu ◽  
...  

AbstractRice yield per plant has a complex genetic architecture, which is mainly determined by its three component traits: the number of grains per panicle (GPP), kilo-grain weight (KGW), and tillers per plant (TP). Exploring ideotype breeding based on selection for genetically less complex component traits is an alternative route for further improving rice production. To understand the genetic basis of the relationship between rice yield and component traits, we investigated the four traits of two rice hybrid populations (575 + 1495 F1) in different environments and conducted meta-analyses of genome-wide association study (meta-GWAS). In total, 3589 significant loci for three components traits were detected, while only 3 loci for yield were detected. It indicated that rice yield is mainly controlled by minor-effect loci and hardly to be identified. Selecting quantitative trait locus/gene affected component traits to further enhance yield is recommended. Mendelian randomization design is adopted to investigate the genetic effects of loci on yield through component traits and estimate the genetic relationship between rice yield and its component traits by these loci. The loci for GPP or TP mainly had a positive genetic effect on yield, but the loci for KGW with different direction effects (positive effect or negative effect). Additionally, TP (Beta = 1.865) has a greater effect on yield than KGW (Beta = 1.016) and GPP (Beta = 0.086). Five significant loci for component traits that had an indirect effect on yield were identified. Pyramiding superior alleles of the five loci revealed improved yield. A combination of direct and indirect effects may better contribute to the yield potential of rice. Our findings provided a rationale for using component traits as indirect indices to enhanced rice yield, which will be helpful for further understanding the genetic basis of yield and provide valuable information for improving rice yield potential.


2017 ◽  
Vol 68 (15) ◽  
pp. 4369-4388 ◽  
Author(s):  
Michael Dingkuhn ◽  
Richard Pasco ◽  
Julie M Pasuquin ◽  
Jean Damo ◽  
Jean-Christophe Soulié ◽  
...  

2020 ◽  
Author(s):  
Jing Su ◽  
Kai Xu ◽  
Chao Wu ◽  
Zirong Li ◽  
Zhongli Hu ◽  
...  

Abstract Background: Rice yield has a complex genetic architecture, which mainly determined by its three component traits: the number of grains per panicle (GPP), kilo-grain weight (KGW) and tillers per plant (TP). Exploring ideotype breeding based on selection for genetically less complex component traits is an alternative route for further improving rice production. Thus, it is important that studying the genetic basis of relationship between rice yield and component traits and selecting the component traits to improve the rice production. Main text: In this study, we carried out meta-analyses of genome-wide association study (Meta-GWAS) with two populations (575 + 1495 F1) in different environments for yield and its three component traits in rice. 3589 significant loci for three components traits were detected, while only 3 significant loci for yield were detected. It indicated that rice yield is mainly controlled by minor-effect loci and hardly to be identified. Selecting quantitative trait locus (QTL)/gene affected component traits to further enhance yield is recommended. Mendelian randomization (MR) design was adopted to investigate the genetic effects of loci on yield through component traits and estimate the genetic relationship between rice yield and its component traits by these loci. The loci for GPP or TP mainly had a positive genetic effect on yield, but the loci for KGW with different direction of effects (positive effect or negative effect). Additionally, TP (Beta=1.865) has a greater effect on yield than KGW (Beta=1.016) and GPP (Beta=0.086). Five significant loci for component traits with indirect effect on yield were identified. Pyramiding superior alleles of the five loci revealed improved yield. A combination of direct and indirect effects may better contribute to the yield potential of rice.Conclusions: Our findings provided a rationale for using component traits as indirect indices to enhanced rice yield, which will be helpful for further understanding the genetic basis of yield, and provide valuable information for improving rice yield potential.


2018 ◽  
Vol 69 (3) ◽  
pp. 711-711
Author(s):  
Michael Dingkuhn ◽  
Richard Pasco ◽  
Julie M Pasuquin ◽  
Jean Damo ◽  
Jean-Christophe Soulié ◽  
...  

2017 ◽  
Vol 68 (15) ◽  
pp. 4389-4406 ◽  
Author(s):  
Michael Dingkuhn ◽  
Richard Pasco ◽  
Julie Mae Pasuquin ◽  
Jean Damo ◽  
Jean-Christophe Soulié ◽  
...  

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