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Variability Assessment of Aromatic Rice Germplasm by Pheno-Genomic traits and Population Structure Analysis.

ABSTRACT: While the pleasant scent of aromatic rice is making it more popular, with demand for aromatic rice expected to rise in future, varieties of this have low yield potential. Genetic diversity and population structure of aromatic germplasm provide valuable information for yield improvement which has potential market value and farm profit. Here, we show diversity and population structure of 113 rice germplasm based on phenotypic and genotypic traits. Phenotypic traits showed that considerable variation existed across the germplasm. Based on Shannon-Weaver index, the most variable phenotypic trait was lemma-palea color. Detecting 140 alleles, 11 were unique and suitable as a germplasm diagnostic tool. Phylogenetic cluster analysis using genotypic traits classified germplasm into three major groups. Moreover, model-based population structure analysis divided all germplasm into three groups, confirmed by principal component and neighbors joining tree analyses. An analysis of molecular variance (AMOVA) and pairwise FST test showed significant differentiation among all population pairs, ranging from 0.023 to 0.068, suggesting that all three groups differed. Significant correlation coefficient was detected between phenotypic and genotypic traits which could be valuable to select further improvement of germplasm. Findings from this study have the potential for future use in aromatic rice molecular breeding programs.

SUBMITTER: Islam MZ

PROVIDER: S-EPMC6028394 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature

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Variability Assessment of Aromatic Rice Germplasm by Pheno-Genomic traits and Population Structure Analysis.

Islam M Z MZ Khalequzzaman M M Bashar M K MK Ivy N A NA Mian M A K MAK Pittendrigh B R BR Haque M M MM Ali M P MP

Scientific reports 20180702 1

While the pleasant scent of aromatic rice is making it more popular, with demand for aromatic rice expected to rise in future, varieties of this have low yield potential. Genetic diversity and population structure of aromatic germplasm provide valuable information for yield improvement which has potential market value and farm profit. Here, we show diversity and population structure of 113 rice germplasm based on phenotypic and genotypic traits. Phenotypic traits showed that considerable variati ...[more]

PMID: 29967407

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Project description:BACKGROUND:Hybrid breeding is an effective tool to improve yield in rice, while parental selection remains the key and difficult issue. Genomic selection (GS) provides opportunities to predict the performance of hybrids before phenotypes are measured. However, the application of GS is influenced by several genetic and statistical factors. Here, we used a rice North Carolina II (NC II) population constructed by crossing 115 rice varieties with five male sterile lines as a model to evaluate effects of statistical methods, heritability, marker density and training population size on prediction for hybrid performance. RESULTS:From the comparison of six GS methods, we found that predictabilities for different methods are significantly different, with genomic best linear unbiased prediction (GBLUP) and least absolute shrinkage and selection operation (LASSO) being the best, support vector machine (SVM) and partial least square (PLS) being the worst. The marker density has lower influence on predicting rice hybrid performance compared with the size of training population. Additionally, we used the 575 (115?×?5) hybrid rice as a training population to predict eight agronomic traits of all hybrids derived from 120 (115?+?5) rice varieties each mating with 3023 rice accessions from the 3000 rice genomes project (3 K RGP). Of the 362,760 potential hybrids, selection of the top 100 predicted hybrids would lead to 35.5%, 23.25%, 30.21%, 42.87%, 61.80%, 75.83%, 19.24% and 36.12% increase in grain yield per plant, thousand-grain weight, panicle number per plant, plant height, secondary branch number, grain number per panicle, panicle length and primary branch number, respectively. CONCLUSIONS:This study evaluated the factors affecting predictabilities for hybrid prediction and demonstrated the implementation of GS to predict hybrid performance of rice. Our results suggest that GS could enable the rapid selection of superior hybrids, thus increasing the efficiency of rice hybrid breeding.

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Dataset Information

Variability Assessment of Aromatic Rice Germplasm by Pheno-Genomic traits and Population Structure Analysis.

Publications

Variability Assessment of Aromatic Rice Germplasm by Pheno-Genomic traits and Population Structure Analysis.

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