The use of microsatellite markers for the detection of genetic similarity among winter bread wheat lines for chromosome�3A

Mahmood, Abid; Baenziger, P. S.; Budak, Hikmet; Gill, Kulvinder S.; Dweikat, İsmail

doi:10.1007/s00122-004-1766-x

Cited by 17 publications

(11 citation statements)

References 41 publications

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“…In Campbell et al (2003), this grain yield QTL detected in the combined analysis explained 28% of the phenotypic variance and the substitution of a WI allele for a CNN allele caused an increase in grain yield. The detection of yield-related QTL in three separate studies in CNN(RICLs3A) (Ali et al, 2011;Campbell et al, 2003;Shah et al, 1999b) and in the current study using WI(RICLs3A) strongly supports the hypothesis that the WI alleles at these clusters of QTLs has (Mahmood et al, 2004) and will likely make an important contribution to grain yield of elite lines of hard winter wheat. Further investigations and additional RICLs are needed to dissect and precisely map the position and expression of Qyld.neb-3A.1 under diff erent fi eld conditions because the ability to detect genetic loci for grain yield is highly infl uenced by genotype, environment, and their interactions.…”

Section: Discussionsupporting

confidence: 80%

“…Analysis of variance (ANOVA) for AD, PHT, GYLD, GVWT, SPSM, KPS, KPSM, and TKWT was performed separately for each environment using the PROC MIXED procedure (Littell et al, 1996) of SAS version 9.1 (SAS Institute, 2004). In both single location and combined analysis variance components were estimated by considering genotypes, replications, blocks nested within replications, environments, and genotype × environment interaction as random effects.…”

Section: Methodsmentioning

confidence: 99%

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Validation of QTL for Grain Yield‐Related Traits on Wheat Chromosome 3A Using Recombinant Inbred Chromosome Lines

Mengistu

Baenziger²,

Eskridge³

et al. 2012

Crop Science

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Previously, quantitative trait loci (QTLs) for grain yield (GYLD) and yield-related traits were identified by using a population of recombinant inbred chronnosome lines (RICLs) developed from the wheat {Triticum aestivum L.) eultivar Cheyenne (CNN) and its substitution line CNN(WI3A), in which the 3A chromosome of eultivar Wichita (Wl) was substituted for CNN chromosome 3A. Our objectives were to identify and validate the QTL previously identified in CNN(RICLs3A), using the mirror population WI(RICLs3A). A population of 90 WI(RICLs3A) was used to evaluate GYLD, 1000-kernel weight (TKW), kernels per spike (KPS), kernels per square meter (KPSM), spikes per square meter (SPSM), grain volume weight (GVW), plant height (PHT), and anthesis date (AD). Data were collected from replicated trials grown in six Nebraska environments from 2008 to 2009. Twelve QTL for GYLD, TKW, KPS, SPSM, GVW, AD, and PHT were detected. The phenotypic variance explained by these QTL ranged from 12% for SPSM to 53% for GVWT Most of the QTLs were co-localized in one or two regions of chromosome 3A. The major grain yield QTL {QGyld.neb.3A.1) detected in the combined analysis explained 19% of the phenotypic variance and the substitution of CNN alíeles for Wl alíeles decreased grain yield. Using a different genetic background, this study confirmed most of the GYLD and yield-related QTL reported in previous RICLs3A mapping studies on chromosome 3A of winter wheat evaluated in Nebraska. N. Mengistu, Pioneer Hi-Bred

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Section: Discussionsupporting

confidence: 80%

Section: Methodsmentioning

confidence: 99%

Validation of QTL for Grain Yield‐Related Traits on Wheat Chromosome 3A Using Recombinant Inbred Chromosome Lines

Mengistu

Baenziger²,

Eskridge³

et al. 2012

Crop Science

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“…The number of polymorphic bands per loci ranged from 1 to 3 with an average of 1.6 alleles per locus. Similar type of result were also reported by Dong and Zeng (2003), Ravi et al, (2003), ZhongFu et al, (2003), Mahmood et al, (2004) and Medini et al, (2005 Zeb et al, (2009) and Najaphy et al, (2012). The detected genetic diversity for the six parental genotypes was lower than that of earlier reported by Salem et al, (2008) in wheat genotypes.…”

Section: Resultssupporting

confidence: 86%

Molecular Diversity of Wheat (Triticum aestivum L.) Genotypes Resistance to Rice Weevil (Sitophilus oryzae L.) Revealed by SSR Markers

Ahmad¹,

Badoni²,

Akhtar³

et al. 2018

Int.J.Curr.Microbiol.App.Sci

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The present investigation was carried out to analyse for the presence of α-amylase inhibitor gene which is responsible for resistance to rice weevil. A total of 30 simple sequence repeat (SSR) markers were used to study the genetic diversity among 30 wheat genotypes resistant to rice weevil. Among them, seventeen primers were found polymorphic on agarose gel and depicted the significant diversity among susceptible and resistant genotypes while rest thirteen primers were found monomorphic. The number of polymorphic bands per loci ranged from 1 to 3 with an average of 1.6 alleles per locus. A clear cut differentiation was exhibited among the genotypes. The average PIC value was 0.37 ranging from 0.17 (WMC120/ WMC76/WMC245) to 0.67 (WMC267), indicating diverse nature of the wheat genotypes and/or highly informative SSR markers used in this study. The analyzed wheat genotypes showed a good level of genetic variability for assessed quantitative, physio-chemical and molecular characters. Molecular markers linked with major genes for traits of interest which have been amplified by the primer set UCW108, a genic marker for GPC-B1. The range of similarity coefficient varied from 54% (PBN-51) to 74% (K 20). SAHN cluster analysis using UPGMA method separated the parental genotypes into four cluster groups, PBN 51 was positioned as single genotype in separate groups i.e., in cluster-I, K 50, K 76 and K 77 in cluster-II, K 2I and K 50 in cluster-III and K 20 in cluster-IV. In dendrogram, based on results of markers validated were able to diversify the resistant and susceptible parents, first group member PBN51 and fourth group member K20 found on the two distant groups, which were observed as most susceptible and resistant genotypes respectively, according to our controlled experiment on weevil infestation and molecular analysis done and hence, the study supports and correlates the result, obtained by these analysis. The present study also indicates that microsatellite markers able to access the genetic diversity among studied genotypes of wheat for weevil resistance.

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“…As a breeder, one would like to believe that if an allele was identified in their germplasm that could significantly increase grain yield that conventional breeding would have found and used the allele. Mahmood et al (2004) using some of the SSR markers used by Campbell et al (2003) and additional SSR markers looked at the molecular diversity of chromosome 3A in historic to modern wheat cultivars adapted to Nebraska. In using the three key polymorphic SSRs between CNN and WI for the main QTL identified by Campbell et al (2003), they found three main clusters of the cultivars.…”

Section: Breaking Chromosomes − What Have We Learned?mentioning

confidence: 99%

Understanding grain yield: it is a journey, not a destination

Baenziger

Dweikat

Gill

et al. 2011

CZECH J GENET PLANT

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Abstract:Approximately 20 years ago, we began our efforts to understand grain yield in winter wheat using chromosome substitution lines between Cheyenne (CNN) and Wichita (WI). We found that two chromosome substitutions, 3A and 6A, greatly affected grain yield. CNN(WI3A) and CNN(WI6A) had 15 to 20% higher grain yield than CNN, whereas WI(CNN3A) and WI(CNN6A) had 15 to 20% lower grain yield than WI. The differences in grain yield are mainly expressed in higher yielding environments (e.g. eastern Nebraska) indicating genotype by environment interactions (G × E). In studies using hybrid wheat, the gene action for grain yield on these chromosomes was found to be mainly controlled by additive gene action. In subsequent studies, we developed recombinant inbred chromosome lines (RICLs) using monosomics or doubled haploids. In extensive studies we found that two regions on 3A affect grain yield in the CNN(RICLs-3A) with the positive QTLs coming from WI. In WI(RICLs-3A), we found a main region on 3A that affected grain yield with the negative QTL coming from CNN. The 3A region identified using WI(RICLs-3A) coincided with one of the regions previously identified in CNN(RICLs-3A). As expected the QTLs have their greatest effect in higher-yielding environments and also exhibit QTL × E. Using molecular markers on chromosomes 3A and 6A, the favorable alleles on 3A in Wichita may be from Turkey Red, the original hard red winter wheat in the Great Plains and presumably the original source of the favorable alleles. Cheyenne, a selection from Crimea, did not have the favorable alleles. In studying modern cultivars, many high yielding cultivars adapted to eastern Nebraska have the WI-allele indicating that it was selected for in breeding higher yielding cultivars. However, some modern cultivars adapted to western Nebraska where the QTL has less effect retain the CNN-allele, presumably because the allele has less effect (is less important in improving grain yield). In addition many modern cultivars have neither the WI-allele, nor the CNN-allele indicating we have diversified our germplasm and new alleles have been brought into the breeding program in this region.

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The use of microsatellite markers for the detection of genetic similarity among winter bread wheat lines for chromosome�3A

Cited by 17 publications

References 41 publications

Validation of QTL for Grain Yield‐Related Traits on Wheat Chromosome 3A Using Recombinant Inbred Chromosome Lines

Validation of QTL for Grain Yield‐Related Traits on Wheat Chromosome 3A Using Recombinant Inbred Chromosome Lines

Molecular Diversity of Wheat (Triticum aestivum L.) Genotypes Resistance to Rice Weevil (Sitophilus oryzae L.) Revealed by SSR Markers

Understanding grain yield: it is a journey, not a destination

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