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Cereal Research Communications
Authors: B. Vaezi, A. Pour-Aboughadareh, R. Mohammadi, M. Armion, A. Mehraban, T. Hossein-Pour, and M. Dorii

Successful production and development of stable and adaptable cultivars only depend on the positive results achieved from the interaction between genotype and environment that consequently has significant effect on breeding strategies. The objectives of this study were to evaluate genotype by environment interactions for grain yield in barley advanced lines and to determine their stability and general adaptability. For these purposes, 18 advanced lines along with two local cultivars were evaluated at five locations (Gachsaran, Lorestan, Ilam, Moghan and Gonbad) during three consecutive years (2012–2015). The results of the AMMI analysis indicated that main effects due to genotype (G), environment (E) and GE interaction as well as four interaction principal component axes were significant, representing differential responses of the lines to the environments and the need for stability analysis. According to AMMI stability parameters, lines G5 and G7 were the most stable lines across environments. Biplot analysis determined two barley mega-environments in Iran. The first mega-environment contained of Ilam and Gonbad locations, where the recommended G13, G19 and G1 produced the highest yields. The second mega-environment comprised of Lorestan, Gachsarn and Moghan locations, where G2, G9, G5 and G7 were the best adapted lines. Our results revealed that lines G5, G7, G9 and G17 are suggested for further inclusion in the breeding program due to its high grain yield, and among them G5 recommended as the most stable lines for variable semi-warm and warm environments. In addition, our results indicated the efficiency of AMMI and GGE biplot techniques for selecting genotypes that are stable, high yielding, and responsive.

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The knowledge about genetic diversity in the wild relatives of wheat provides useful information for breeding programs and gene pool management. In the present study, an assessment of agro-morphological diversity and molecular variability among 70 accessions of Triticum, belonging to T. boeoticum, T. urartu, T. durum and T. aestivum species, collected from different regions of Iran was made. According to phenotypic analysis, all traits except peduncle length, stem diameter and the number of seeds per spike indicated a high level of diversity among studied accessions. Also, principal component analysis identified six components that explained 87.53% of the total variation in agro-morphological traits. In molecular analysis, 15 start codon targeted (SCoT) polymorphism primers produced 166 bands, out of which, 162 (97.59%) were polymorphic. Analysis of molecular variance (AMOVA) indicated the 63% of the variation resided among populations. The maximum value of polymorphism information content (PIC), the observed (Na) and effective (Ne) number of alleles, Nie’s gene diversity (He) and Shannon’s information index (I) was detected for T. boeoticum than the other species. The SCoT-based tree revealed three different groups corresponding to the genomic constitution in Triticum germplasm, which was in part confirmed by STRUCTURE and principal coordinate (PCoA) analyses. Our results indicated a remarkable level of genetic diversity among studied Iranian Triticum species, especially T. boeoticum, which can be of interest for future breeding and other analyses associated with future studies of the wild relatives of wheat. More importantly, our results revealed that SCoT markers could be used to accurate evaluate genetic diversity and phylogenetic relationships among different Triticum species.

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Progress in plant molecular tools has been resulted in the development of gene-targeted and functional marker systems. CAAT box region is a different pattern of nucleotides with a consensus sequence, GGCCAATCT, which situated upstream of the start codon of eukaryote genes and plays an important role during transcription. In the present study, several CAAT box-derived polymorphism (CBDP) primers were used for fingerprinting in mini-core collection of durum wheat (including internationally developed breeding lines and Iranian landraces). Twelve selected primers amplified 98 loci, of which all were polymorphic. The average values of the polymorphism information content (PIC) and resolving power (Rp) were 0.31 and 9.16, respectively, indicating a high level of variability among studied genotypes. Analysis of molecular variance (AMOVA) indicated that 92% of the total variation resided among populations. The values of the percentage polymorphic bands (PPL), the observed (Na) and effective (Ne) number of alleles, Nei’s gene diversity (He) and Shannon’s information index (I) for Iranian landraces were higher than the breeding lines. The Fandendrogram obtained by cluster analysis grouped all individuals into three main clusters. Our results showed a remarkable level of genetic diversity among studied durum wheat, especially among Iranian landraces, which can be interest for future breeding programs. More importantly, the present study also revealed that CBDP technique was efficient and powerful tool to assess genetic diversity in wheat germplasm. Hence, this technique could be employed individually or in combination with other molecular markers to evaluate genetic diversity and relations among different species.

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Glutenin and gliadin subunits play a key role in flour processing quality by network formation in dough. Wild relatives of crops have served as a pool of genetic variation for decades. In this study, 180 accessions from 12 domesticated and wild relatives of wheat were characterized for the glutenin and gliadin genes with allele-specific molecular markers. A total of 24 alleles were detected for the Glu-A3 and Gli-2A loci, which out of 19 amplified products identified as new alleles. Analysis of molecular variance (AMOVA) indicated that 90 and 65% of the genetic diversity were partitioned within two Aegilops and Triticum genera and their species, respectively. Furthermore, all glutenin and gliadin analyzed loci were polymorphic, indicating large genetic diversity within and between the wild species. Our results revealed that allelic variation of Glu-3A and Gli-As.2 is linked to genomic constitutions so that, Ae. caudata (C genome), Ae. neglecta (UM genome), Ae. umbellulata (U genome) and T. urartu (Au genome) harbor wide variation in the studied subunits. Hence, these species can be used in wheat quality breeding programs.

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In the present study, efficiency of the artificial neural network (ANN) method to identify the best drought tolerance indices was investigated. For this purpose, 25 durum genotypes were evaluated under rainfed and supplemental irrigation environments during two consecutive cropping seasons (2011–2013). The results of combined analysis of variance (ANOVA) revealed that year, environment, genotype and their interaction effects were significant for grain yield. Mean grain yield of the genotypes ranged from 184.93 g plot–1 under rainfed environment to 659.32 g plot–1 under irrigated environment. Based on the ANN results, yield stability index (YSI), harmonic mean (HM) and stress susceptible index (SSI) were identified as the best indices to predict drought-tolerant genotypes. However, mean productivity (MP) followed by geometric mean productivity (GMP) and HM were found to be accurate indices for screening drought tolerant genotypes. In general, our results indicated that genotypes G9, G12, G21, G23 and G24 were identified as more desirable genotypes for cultivation in drought-prone environments. Importantly, these results could provide an evidence that ANN method can play an important role in the selection of drought tolerant genotypes and also could be useful in other biological contexts.

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