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The genetic diversity and structure of indica rice in China as detected by single nucleotide polymorphism analysis

Overview of attention for article published in BMC Genomic Data, March 2016
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Title
The genetic diversity and structure of indica rice in China as detected by single nucleotide polymorphism analysis
Published in
BMC Genomic Data, March 2016
DOI 10.1186/s12863-016-0361-x
Pubmed ID
Authors

Qun Xu, Xiaoping Yuan, Shan Wang, Yue Feng, Hanyong Yu, Yiping Wang, Yaolong Yang, Xinghua Wei, Ximing Li

Abstract

Rice (Oryza sativa L.) is the staple food of more than half of the world's population. The identification of genetic diversity in local varieties of rice compared with that of improved or introduced varieties is important in breeding elite varieties for sustainable agriculture. Array-based single nucleotide polymorphism (SNP) detection is a useful technique for such studies and breeding applications. We developed a 5291-SNP genome-wide array and used it to genotype 471 indica rice accessions in China using Illumina's Infinium technology. Local, introduced, and improved rice varieties were clustered into three sub-groups, with some overlapping shown in principal component analysis and neighbor-joining tree, also confirmed by model-based structure. A minor allele frequency ≥0.2 was observed in 72 % of polymorphic SNPs in local rice varieties, which was higher than that in other sub-groups. Local rice varieties also had the highest mean polymorphism information content (PIC) and genetic diversity. Analysis of molecular variance showed that 90.61 % of genetic variation was a result of differences within sub-groups. Our results revealed that SNP analysis clustered local varieties, introduced varieties, and improved varieties into three clear sub-groups. The distribution of parameter PIC values on sub-group genomes revealed that genetic differentiation among them might not be on a genome-wide scale, but rather on selected loci or chromosomal intervals. The result of Gene Ontology enrichment analysis showed that genes nearby those selected SNPs associated different molecular functions or various traits among sub-groups.

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The data shown below were compiled from readership statistics for 44 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 44 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 14 32%
Student > Doctoral Student 7 16%
Student > Master 6 14%
Student > Ph. D. Student 4 9%
Professor 3 7%
Other 1 2%
Unknown 9 20%
Readers by discipline Count As %
Agricultural and Biological Sciences 28 64%
Biochemistry, Genetics and Molecular Biology 6 14%
Computer Science 2 5%
Unknown 8 18%
Attention Score in Context

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 15 March 2016.
All research outputs
#20,655,488
of 25,373,627 outputs
Outputs from BMC Genomic Data
#861
of 1,204 outputs
Outputs of similar age
#233,393
of 314,261 outputs
Outputs of similar age from BMC Genomic Data
#21
of 31 outputs
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We're also able to compare this research output to 31 others from the same source and published within six weeks on either side of this one. This one is in the 16th percentile – i.e., 16% of its contemporaries scored the same or lower than it.