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      Artificial Selection of Gn1a Plays an Important role in Improving Rice Yields Across Different Ecological Regions

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          Abstract

          Background

          Rice is one of the most important crops, and it is essential to improve rice productivity to satisfy the future global food supply demands. Gn1a ( OsCKX2), which encodes cytokinin oxidase/dehydrogenase, plays an important role in regulating rice grain yield.

          Results

          In this study, we analyzed the genetic variation of Gn1a, which influences grain yield through controlling the number of spikelets in rice. The allelic variations in the promoter, 5’ untranslated region (UTR) and coding sequence (CDS) of Gn1a were investigated in 175 cultivars and 21 wild rice accessions. We found that Gn1a showed less sequence variation in the cultivars, but exhibited significant nucleotide diversity in wild rice. A total of 14 alleles, named AP1 to AP14, were identified in the cultivars based on the amino acid divergence of GN1A. Association analysis revealed that the number of spikelets and grain yield were significantly different between the different alleles. Phylogenetic analysis indicated that the three main alleles, AP3, AP8 and AP9, in the cultivars might originate from a common ancestor allele, AP1, in wild rice.

          Conclusions

          Of these alleles in the cultivars, AP9 was suggested as the best allele in indica, as it has shown strong artificial selection in breeding high-yield rice in the past. It might be valuable to explore the high-yield-related alleles of Gn1a to develop high-yield rice cultivars in future breeding programs.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s12284-015-0071-4) contains supplementary material, which is available to authorized users.

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          Most cited references17

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          Cytokinin oxidase regulates rice grain production.

          Most agriculturally important traits are regulated by genes known as quantitative trait loci (QTLs) derived from natural allelic variations. We here show that a QTL that increases grain productivity in rice, Gn1a, is a gene for cytokinin oxidase/dehydrogenase (OsCKX2), an enzyme that degrades the phytohormone cytokinin. Reduced expression of OsCKX2 causes cytokinin accumulation in inflorescence meristems and increases the number of reproductive organs, resulting in enhanced grain yield. QTL pyramiding to combine loci for grain number and plant height in the same genetic background generated lines exhibiting both beneficial traits. These results provide a strategy for tailormade crop improvement.
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            Natural variation at the DEP1 locus enhances grain yield in rice.

            Grain yield is controlled by quantitative trait loci (QTLs) derived from natural variations in many crop plants. Here we report the molecular characterization of a major rice grain yield QTL that acts through the determination of panicle architecture. The dominant allele at the DEP1 locus is a gain-of-function mutation causing truncation of a phosphatidylethanolamine-binding protein-like domain protein. The effect of this allele is to enhance meristematic activity, resulting in a reduced length of the inflorescence internode, an increased number of grains per panicle and a consequent increase in grain yield. This allele is common to many Chinese high-yielding rice varieties and likely represents a relatively recent introduction into the cultivated rice gene pool. We also show that a functionally equivalent allele is present in the temperate cereals and seems to have arisen before the divergence of the wheat and barley lineages.
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              Genetic and molecular bases of rice yield.

              Grain yield in rice is a complex trait multiplicatively determined by its three component traits: number of panicles, number of grains per panicle, and grain weight; all of which are typical quantitative traits. The developments in genome mapping, sequencing, and functional genomic research have provided powerful tools for investigating the genetic and molecular bases of these quantitative traits. Dissection of the genetic bases of the yield traits based on molecular marker linkage maps resolved hundreds of quantitative trait loci (QTLs) for these traits. Mutant analyses and map-based cloning of QTLs have identified a large number of genes required for the basic processes underlying the initiation and development of tillers and panicles, as well as genes controlling numbers and sizes of grains and panicles. Molecular characterization of these genes has greatly advanced the mechanistic understanding of the regulation of these rice yield traits. These findings have significant implications in crop genetic improvement.
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                Author and article information

                Contributors
                shaoqingli@whu.edu.cn
                Journal
                Rice (N Y)
                Rice (N Y)
                Rice
                Springer US (New York )
                1939-8425
                1939-8433
                16 December 2015
                16 December 2015
                2015
                : 8
                : 37
                Affiliations
                State Key Laboratory of Hybrid Rice; Key Laboratory for Research and Utilization of Heterosis in Indica Rice of Ministry of Agriculture; Engineering Research Center for Plant Biotechnology and Germplasm Utilization of Ministry of Education; College of Life Science, Wuhan University, Wuhan, 430072 China
                Article
                71
                10.1186/s12284-015-0071-4
                4681714
                26677125
                bbc34d5a-3152-4903-80fb-0ec0e85cad10
                © Wang et al. 2015

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

                History
                : 4 July 2015
                : 1 December 2015
                Funding
                Funded by: 973 Program
                Award ID: 2011CB100102
                Award Recipient :
                Funded by: 863 program
                Award ID: 2014AA10A604-9
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China CN;
                Award ID: 31370363
                Award Recipient :
                Categories
                Original Article
                Custom metadata
                © The Author(s) 2015

                Agriculture
                rice,artificial selection,gn1a,allele,association analysis
                Agriculture
                rice, artificial selection, gn1a, allele, association analysis

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