| Title |
Artificial Selection of Gn1a Plays an Important role in Improving Rice Yields Across Different Ecological Regions
|
|---|---|
| Published in |
Rice, December 2015
|
| DOI | 10.1186/s12284-015-0071-4 |
| Pubmed ID | |
| Authors |
Jie Wang, Huaxue Xu, Nengwu Li, Fengfeng Fan, Liuting Wang, Yingguo Zhu, Shaoqing Li |
| Abstract |
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. 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. 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. |
X Demographics
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 46 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 46 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 12 | 26% |
| Researcher | 9 | 20% |
| Student > Master | 5 | 11% |
| Student > Bachelor | 2 | 4% |
| Professor > Associate Professor | 2 | 4% |
| Other | 2 | 4% |
| Unknown | 14 | 30% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 22 | 48% |
| Biochemistry, Genetics and Molecular Biology | 7 | 15% |
| Business, Management and Accounting | 1 | 2% |
| Immunology and Microbiology | 1 | 2% |
| Earth and Planetary Sciences | 1 | 2% |
| Other | 0 | 0% |
| Unknown | 14 | 30% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 18 December 2015.
All research outputs
#17,778,896
of 22,835,198 outputs
Outputs from Rice
#227
of 385 outputs
Outputs of similar age
#265,188
of 390,452 outputs
Outputs of similar age from Rice
#4
of 7 outputs
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So far Altmetric has tracked 385 research outputs from this source. They receive a mean Attention Score of 3.7. This one is in the 35th percentile – i.e., 35% of its peers scored the same or lower than it.
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