| Title |
Development and validation of allele-specific SNP/indel markers for eight yield-enhancing genes using whole-genome sequencing strategy to increase yield potential of rice, Oryza sativa L.
|
|---|---|
| Published in |
Rice, March 2016
|
| DOI | 10.1186/s12284-016-0084-7 |
| Pubmed ID | |
| Authors |
Sung-Ryul Kim, Joie Ramos, Motoyuki Ashikari, Parminder S. Virk, Edgar A. Torres, Eero Nissila, Sherry Lou Hechanova, Ramil Mauleon, Kshirod K. Jena |
| Abstract |
Rice is one of the major staple foods in the world, especially in the developing countries of Asia. Its consumption as a dietary source is also increasing in Africa. To meet the demand for rice to feed the increasing human population, increasing rice yield is essential. Improving the genetic yield potential of rice is one ideal solution. It is imperative to introduce the identified yield-enhancing gene(s) into modern rice cultivars for the rapid improvement of yield potential through marker-assisted breeding. We report the development of PCR-gel-based markers for eight yield-related functional genes (Gn1a, OsSPL14, SCM2, Ghd7, DEP1, SPIKE, GS5, and TGW6) to introduce yield-positive alleles from the donor lines. Six rice cultivars, including three each of donor and recipient lines, respectively, were sequenced by next-generation whole-genome sequencing to detect DNA polymorphisms between the genotypes. Additionally, PCR products containing functional nucleotide polymorphism (FNP) or putative FNPs for yield-related genes were sequenced. DNA polymorphisms discriminating yield-positive alleles and non-target alleles for each gene were selected through sequence analysis and the allele-specific PCR-gel-based markers were developed. The markers were validated with our intermediate breeding lines produced from crosses between the donors and 12 elite indica rice cultivars as recipients. Automated capillary electrophoresis was tested and fluorescence-labeled SNP genotyping markers (Fluidigm SNP genotyping platform) for Gn1a, OsSPL14, Ghd7, GS5, and GS3 genes were developed for high-throughput genotyping. The SNP/indel markers linked to yield related genes functioned properly in our marker-assisted breeding program with identified high yield potential lines. These markers can be utilized in local favorite rice cultivars for yield enhancement. The marker designing strategy using both next generation sequencing and Sanger sequencing methods can be used for suitable marker development of other genes associated with useful agronomic traits. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
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 115 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| China | 1 | <1% |
| Sri Lanka | 1 | <1% |
| Switzerland | 1 | <1% |
| Brazil | 1 | <1% |
| Unknown | 111 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 25 | 22% |
| Student > Ph. D. Student | 20 | 17% |
| Student > Master | 11 | 10% |
| Student > Bachelor | 7 | 6% |
| Other | 6 | 5% |
| Other | 19 | 17% |
| Unknown | 27 | 23% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 61 | 53% |
| Biochemistry, Genetics and Molecular Biology | 9 | 8% |
| Environmental Science | 3 | 3% |
| Computer Science | 3 | 3% |
| Arts and Humanities | 2 | 2% |
| Other | 4 | 3% |
| Unknown | 33 | 29% |
Attention Score in Context
This research output has an Altmetric Attention Score of 10. 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 15 November 2016.
All research outputs
#3,127,783
of 22,856,968 outputs
Outputs from Rice
#32
of 386 outputs
Outputs of similar age
#52,343
of 300,781 outputs
Outputs of similar age from Rice
#1
of 10 outputs
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So far Altmetric has tracked 386 research outputs from this source. They receive a mean Attention Score of 3.7. This one has done particularly well, scoring higher than 91% of its peers.
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