Dataset Information

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

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.

SUBMITTER: Xu Q

PROVIDER: S-EPMC4793538 | biostudies-literature | 2016 Mar

REPOSITORIES: biostudies-literature

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

Xu Qun Q Yuan Xiaoping X Wang Shan S Feng Yue Y Yu Hanyong H Wang Yiping Y Yang Yaolong Y Wei Xinghua X Li Ximing X

BMC genetics 20160315

<h4>Background</h4>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.<h4>Results</h4>We developed a 5291-SNP genome-wide array and used it to genot ...[more]

PMID: 26979342

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Project description:BackgroundRutabaga or swede (Brassica napus ssp. napobrassica (L.) Hanelt) varies in root and leaf shape and colour, flesh colour, foliage growth habits, maturity date, seed quality parameters, disease resistance and other traits. Despite these morphological differences, no in-depth molecular analyses of genetic diversity have been conducted in this crop. Understanding this diversity is important for conservation and broadening the use of this resource.ResultsThis study investigated the genetic diversity within and among 124 rutabaga accessions from five Nordic countries (Norway, Sweden, Finland, Denmark and Iceland) using a 15 K single nucleotide polymorphism (SNP) Brassica array. After excluding markers that did not amplify genomic DNA, monomorphic and low coverage site markers, the accessions were analyzedwith 6861 SNP markers. Allelic frequency statistics, including polymorphism information content (PIC), minor allele frequency (MAF) and mean expected heterozygosity ([Formula: see text]e) and population differentiation statistics such as Wright's F-statistics (FST) and analysis of molecular variance (AMOVA) indicated that the rutabaga accessions from Norway, Sweden, Finland and Denmark were not genetically different from each other. In contrast, accessions from these countries were significantly different from the accessions from Iceland (P < 0.05). Bayesian analysis with the software STRUCTURE placed 66.9% of the rutabaga accessions into three to four clusters, while the remaining 33.1% constituted admixtures. Three multivariate analyses: principal coordinate analysis (PCoA), the unweighted pair group method with arithmetic mean (UPGMA) and neighbour-joining (NJ) clustering methods grouped the 124 accessions into four to six subgroups.ConclusionOverall, the correlation of the accessions with their geographic origin was very low, except for the accessions from Iceland. Thus, Icelandic rutabaga accessions can offer valuable germplasm for crop improvement.

Dataset Information

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

Publications

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

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