Literature DB >> 33767346

Genome-wide association study and Mendelian randomization analysis provide insights for improving rice yield potential.

Jing Su1, Kai Xu1, Zirong Li1, Yuan Hu1, Zhongli Hu2, Xingfei Zheng3, Shufeng Song4, Zhonghai Tang5, Lanzhi Li6.   

Abstract

Rice 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.

Entities:  

Year:  2021        PMID: 33767346     DOI: 10.1038/s41598-021-86389-7

Source DB:  PubMed          Journal:  Sci Rep        ISSN: 2045-2322            Impact factor:   4.379


  39 in total

1.  Natural variation at the DEP1 locus enhances grain yield in rice.

Authors:  Xianzhong Huang; Qian Qian; Zhengbin Liu; Hongying Sun; Shuyuan He; Da Luo; Guangmin Xia; Chengcai Chu; Jiayang Li; Xiangdong Fu
Journal:  Nat Genet       Date:  2009-03-22       Impact factor: 38.330

Review 2.  Genetic and molecular bases of rice yield.

Authors:  Yongzhong Xing; Qifa Zhang
Journal:  Annu Rev Plant Biol       Date:  2010       Impact factor: 26.379

Review 3.  Next-Generation Sequencing Accelerates Crop Gene Discovery.

Authors:  Khanh Le Nguyen; Alexandre Grondin; Brigitte Courtois; Pascal Gantet
Journal:  Trends Plant Sci       Date:  2018-12-17       Impact factor: 18.313

Review 4.  Meta-analysis methods for genome-wide association studies and beyond.

Authors:  Evangelos Evangelou; John P A Ioannidis
Journal:  Nat Rev Genet       Date:  2013-05-09       Impact factor: 53.242

Review 5.  Advances in genome-wide association studies of complex traits in rice.

Authors:  Qin Wang; Jiali Tang; Bin Han; Xuehui Huang
Journal:  Theor Appl Genet       Date:  2019-11-12       Impact factor: 5.699

6.  Relationship between grain yield and quality in rice germplasms grown across different growing areas.

Authors:  Quan Xu; Wenfu Chen; Zhengjin Xu
Journal:  Breed Sci       Date:  2015-06-01       Impact factor: 2.086

7.  Exploring the Relationships Between Yield and Yield-Related Traits for Rice Varieties Released in China From 1978 to 2017.

Authors:  Ronghua Li; Meijuan Li; Umair Ashraf; Shiwei Liu; Jiaen Zhang
Journal:  Front Plant Sci       Date:  2019-05-07       Impact factor: 5.753

8.  Meta-QTL analysis of seed iron and zinc concentration and content in common bean (Phaseolus vulgaris L.).

Authors:  Paulo Izquierdo; Carolina Astudillo; Matthew W Blair; Asif M Iqbal; Bodo Raatz; Karen A Cichy
Journal:  Theor Appl Genet       Date:  2018-05-11       Impact factor: 5.699

9.  Genome-wide association reveals novel genomic loci controlling rice grain yield and its component traits under water-deficit stress during the reproductive stage.

Authors:  Niteen N Kadam; Paul C Struik; Maria C Rebolledo; Xinyou Yin; S V Krishna Jagadish
Journal:  J Exp Bot       Date:  2018-07-18       Impact factor: 6.992

10.  Development of Whole-Genome Agarose-Resolvable LInDel Markers in Rice.

Authors:  Wei Hu; Tianhao Zhou; Pengfei Wang; Bo Wang; Jiaming Song; Zhongmin Han; Lingling Chen; Kede Liu; Yongzhong Xing
Journal:  Rice (N Y)       Date:  2020-01-06       Impact factor: 4.783

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