Microsatellite Markers in and around Rice Genes: Applications in Variety Identification and DUS Testing

Bonow, S.; Pinho, E V R Von; Vieira, Maria G. C.; Vosman, Ben

doi:10.2135/cropsci2008.06.0380

Cited by 26 publications

(26 citation statements)

References 29 publications

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“…The number of detected alleles was generally lower than that reported in the literature. The set of microsatellites used in the study exhibited a lower mean of alleles (3.5) than the 4.4 alleles reported by Bonow et al (2009) and even lower than the 11.7 reported by Rahman et al, 2010 on local Indian varieties. In a preliminary analysis of varieties included in this study, the use of 12 SSRs did not allow the genetic discrimination among the 16 varieties (data not shown).…”

Section: Genetic Diversity Parameters Of Varietiescontrasting

confidence: 59%

Genetic diversity, identification, and certification of Chilean rice varieties using molecular markers

Becerra

Paredes

Gutiérrez

et al. 2015

Chilean J. Agric. Res.

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It takes approximately 14 yr to produce a new rice (Oryza sativa L.) variety, that is, from initial hybridization to its commercial release. Currently, new varieties are identified based on morphological descriptors, which have been efficient over time. However, due to the main constraints on seed type impose to other breeding objectives and the pressure of continuous release of varieties, high degree of parentage, and genetic and morphological uniformity has been observed in the breeding populations. The objectives of this study were: to determine the genetic variability of Chilean and foreign commercial rice varieties, and determine, identify, and certify the genetic relationships among varieties, using simple sequence repeat (SSR) markers. A total of 16 commercial varieties, some of them closely related, were included in the study, which were genetically analyzed using 54 microsatellites. The 54 microsatellite loci allowed the discrimination among the 16 varieties. The number of alleles ranged between 2 and 8 with a mean of 3.54 alleles per locus, while the polymorphism information content (PIC) presented a mean of 0.44. The genetic distance and diversity parameters between pairs of varieties indicate a limited diversity among these genotypes. The cluster analysis indicated that varieties were grouped according to their grain type and pedigree. Results demonstrate that the identification and certification of varieties using microsatellite markers could be a good complement to existing agro-morphological data when varieties are closed related.

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Section: Genetic Diversity Parameters Of Varietiescontrasting

confidence: 59%

Genetic diversity, identification, and certification of Chilean rice varieties using molecular markers

Becerra

Paredes

Gutiérrez

et al. 2015

Chilean J. Agric. Res.

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“…The traits used in assessing crop varieties for DUS have been carefully selected taking into account the plasticity of morphological characteristics and thus are efficient for comparing varieties (Law et al, 2011). However, the measurement of morphological traits is expensive, requiring more space, time consuming (Smykal et al, 2008) and trait expressivity is affected by environment (Bonow et al, 2009) due to gene x environment interaction (Law et al, 2011a). The limitation of using morphological traits is further compounded by the reduction in the variability of morphological traits in elite germplasm (Bonow et al, 2009;Gunjaca et al, 2008) caused by inbred line recycling (Reif et al, 2010;Ristanovic et al, 1985) and essential derivation (White et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

“…However, the measurement of morphological traits is expensive, requiring more space, time consuming (Smykal et al, 2008) and trait expressivity is affected by environment (Bonow et al, 2009) due to gene x environment interaction (Law et al, 2011a). The limitation of using morphological traits is further compounded by the reduction in the variability of morphological traits in elite germplasm (Bonow et al, 2009;Gunjaca et al, 2008) caused by inbred line recycling (Reif et al, 2010;Ristanovic et al, 1985) and essential derivation (White et al, 2006). Pedigree breeding has also been implicated for reducing genetic variability of maize (Newton et al, 2010;Reif et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

A comparative analysis of distinctness, uniformity and stability (DUS) data in discriminating selected Southern African maize (Zea mays L.) inbred lines

Chanda¹,

Mukanga²,

Mwala³

et al. 2014

Afr. J. Agric. Res.

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A comparative analysis of distinctness, uniformity and stability (DUS) data in discriminating selected SouthernAfrican maize (Zea mays L.) inbred lines The ability to discriminate germplasm is important for plant breeding as well as for plant variety protection. To achieve this, plant breeders have been using molecular, physiological and biochemical markers in discriminating and grouping of genotypes. Breeders have been looking for effective, quick and cheaper ways of grouping germplasm. Therefore this study was carried out to assess the ability of the traits used for determining the distinctness, uniformity and stability (DUS) of new plant varieties and agro-morphological characteristics for differentiating Southern African maize inbreds. In this study, 18 maize inbred lines were assessed for their variation based on 25 agronomic and 12 DUS traits. The maize inbred lines were grouped differently based on qualitative or quantitative traits or when combined. The correlation between the qualitative and quantitative similarity matrices was low (r=0.048) and non-significant. This indicated that both qualitative and quantitative traits should be used for effective maize inbred line discrimination. Both qualitative and quantitative similarity matrices were highly significantly (p<0.001) and highly correlated to mixed data (r=0.82 and r=0.61 respectively). The grouping of the inbred lines based on Principal Component Analysis (PCA) was similar to the similarity matrix of the mixed data. The first principal component, which explained 27.8% of the total variation, was due to grain yield and productive parameters. The second component, explaining 13.2% of the total variation was due to number of tassel branches (TBNo) and tassel length (TL). The Shannon diversity index showed that the inbred lines were diverse in days to silking, ear diameter, days to maturity, shelling percentage and leaf colour. It is concluded that for effective discrimination of maize inbred lines both agro-morphological and DUS traits should be used especially when few inbreds are being considered.

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“…Moreover, these markers were irrelevant to morphological traits that are currently used in DUS testing of new plant varieties. However, in a few cases, microsatellite markers in and around the genes were used for discrimination of closely related rice varieties (Bonow et al, 2009). Also, AFLPs could distinguish rapeseed cultivars according to growth habit (winter vs. spring), country of origin, and breeding company of varieties (Lombard et al, 2000).…”

Section: Discussionmentioning

confidence: 99%

“…Lombard et al (2000), using amplified fragment length polymorphisms (AFLPs), could distinguish rapeseed cultivars according to growth type (winter vs. spring), country of origin, and breeding company of varieties. Bonow et al (2009) based on simple sequence repeats (SSRs) derived from upstream region of MADSbox genes and other expressed sequence-tags could discriminate closely related rice varieties according to their subspecies, i.e., japonica and indica. In barley, robust diagnostic PCR-based markers was used for identification of spring and winter types (Cockram et al, 2009) and, in tomato, some disease resistance genes were used for DUS testing (Arens et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Association mapping for morphological traits relevant to registration of barley varieties

Jamali

Mohammadi

Sadeghzadeh

2018

Span. j. agric. res.

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Elucidating marker-trait associations would have fruitful implications in distinctness, uniformity, and stability (DUS) tests of new varieties required for both variety registration and granting plant breeders' rights. As the number of new varieties with narrow genetic bases increases, the necessity for deployment of molecular markers to complement morphological DUS traits gets particular attention. We used simple sequence repeats (SSRs) and sequence related amplification polymorphisms (SRAPs) markers in association mapping of morphological traits in a collection of 143 barley landraces and advanced breeding lines. This panel represented a diverse and uniform sample in terms of both quantitative and categorical traits whilst it was structurally partitioned by number of ear rows (six-and two-rowed) and seasonal growth habit (winter and spring types) characteristics. SSRs were more powerful compared with SRAPs in separating six-and two-rowed genotypes based on both model-based Bayesian and neighbor joining clustering methods. A number of associated SSR and SRAP markers were found for 15 out of 36 DUS traits after considering Bonferroni correction through linear models (GLM and MLM) and chi-square-based tests (SA and AAT). This is also the first report of association of awn roughness and grain color with molecular markers in barley. Moreover, SSR marker BMAC0113 appeared associated with time of ear emergence (TEE), confirming previous findings. These markers could be beneficial to complement and speed up DUS testing of new varieties, as well as for improving management of barley reference collections.Additional keywords: LD mapping; Hordeum vulgare L.; DUS traits; SSRs & SRAPs markers. Abbreviations used: AAT (allelic association test); AFLP (amplified fragment length polymorphism); AR (awn roughness); DUS (distinctness, uniformity, stability); ED (ear density); GLM (general linear model); GSLN (spiculation of inner lateral nerves of lemma); KCAL (color of grain aleurone layer); MLM (mixed linear model); NER (number of ear rows); SA (stratified analysis); SGH (seasonal growth habit); SRAP (sequence-related amplified polymorphism); SSR (simple sequence repeats); TEE (time of ear emergence).

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Microsatellite Markers in and around Rice Genes: Applications in Variety Identification and DUS Testing

Cited by 26 publications

References 29 publications

Genetic diversity, identification, and certification of Chilean rice varieties using molecular markers

Genetic diversity, identification, and certification of Chilean rice varieties using molecular markers

A comparative analysis of distinctness, uniformity and stability (DUS) data in discriminating selected Southern African maize (Zea mays L.) inbred lines

Association mapping for morphological traits relevant to registration of barley varieties

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