2014
DOI: 10.1017/s1479262114000884
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Genetic diversity and population structure in a rice drought stress panel

Abstract: A drought stress panel composed of diverse accessions selected from upland, aerobic, rainfed lowland and irrigated lowland environments, was assembled to serve as germplasm for aerobic adaptation breeding. Aerobic rice requires significant levels of tolerance to drought stress due to intermittent water deficit and high soil impedance caused by aerobic conditions. Genomic information may be utilized to investigate the nature of the panel to guide varietal improvement. Using 153 simple sequence repeat and 384 si… Show more

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Cited by 2 publications
(3 citation statements)
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“…Genetic relatedness among the rice genotypes from six major rice growing states of the US was assessed using SSR markers, which were proven to be effective in identifying small allelic variation [7]. The average polymorphism information content (PIC) of 0.33 observed in our study was less compared to the average PIC of 0.54 in the global rice collection [23].…”
Section: Discussionmentioning
confidence: 64%
See 1 more Smart Citation
“…Genetic relatedness among the rice genotypes from six major rice growing states of the US was assessed using SSR markers, which were proven to be effective in identifying small allelic variation [7]. The average polymorphism information content (PIC) of 0.33 observed in our study was less compared to the average PIC of 0.54 in the global rice collection [23].…”
Section: Discussionmentioning
confidence: 64%
“…Simple sequence repeat (SSR) markers have been extensively used to evaluate genetic diversity in rice [5,6]. The SSR markers are multi-allelic and can detect more genetic variation compared to single nucleotide polymorphism (SNP) [7] and amplified fragment length polymorphism (AFLP) markers [8]. Narrow genetic diversity in the US rice genotypes was observed in a previous study using SSR markers [9].…”
Section: Introductionmentioning
confidence: 99%
“…Five independent runs were used for each subpopulation ( k -value). For each run, simulations were run for 100,000 burn-in and 100,000 Markov Chain Monte Carlo (MCMC) iterations ( Tabanao et al., 2014 ). The Delta k statistic of ( Evanno et al., 2005 ) was estimated using STRUCTURE HARVESTER ( Earl and Vonholdt, 2011 ) to determine the best k value.…”
Section: Methodsmentioning
confidence: 99%