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Durum wheat (Triticum turgidum var. durum) is one of the most important cereal crops widely cultivated all over the world with high economic value. In the present study, genetic variation in a mini-core collection of durum wheat germplasm, including 25 breeding lines and 18 landraces, was evaluated using 15 inter-simple sequence repeat (ISSR) and six start codon targeted (SCoT) markers. High levels of polymorphism were observed; 98.70% (ISSR) and 100% (SCoT), which indicated that these markers are useful tools for detection of genetic variation in the collection. Analysis of molecular variance revealed that the major part of genetic variations (90% and 93% for ISSR and SCoT, respectively) occurred within genotypes set. Comparing the genetic variation of breeding lines and landraces based on genetic parameters showed that effective number of alleles (Ne), Nei's gene diversity (He) and Shannon's Information index (I) in landraces were higher than in breeding lines. Although cluster analysis, based on both markers, separated the genotypes in five groups, the dendrogram obtained from SCoT provided the best clustering pattern. Inter-population differentiation (Gst) estimated on the basis of two marker systems representing that a vast portion of the total genetic diversity refers to variation within two sets of genotypes. In conclusion, the results verified a high level of genetic variation among the durum wheat mini-core collection, particularly among landraces, which can be interesting for future breeding programmes.

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Premise of the StudyAccess to improved crop cultivars is the foundation for successful agriculture. New cultivars must have improved yields that are determined by quantitative and qualitative traits. Genotype‐by‐environment interactions (GEI) occur for quantitative traits such as reproductive fitness, longevity, height, weight, yield, and disease resistance. The stability of genotypes across a range of environments can be analyzed using GEI analysis. GEI analysis includes univariate and multivariate analyses with both parametric and non‐parametric models.Methods and ResultsThe program STABILITYSOFT is online software based on JavaScript and R to calculate several univariate parametric and non‐parametric statistics for various crop traits. These statistics include Plaisted and Peterson's mean variance component (θ i), Plaisted's GE variance component (θ (i)), Wricke's ecovalence stability index (W i 2), regression coefficient (b i), deviation from regression (S di 2), Shukla's stability variance (σ i 2), environmental coefficient of variance (CV i), Nassar and Huhn's statistics (S (1), S (2)), Huhn's equation (S (3) and S (6)), Thennarasu's non‐parametric statistics (NP (i)), and Kang's rank‐sum. These statistics are important in the identification of stable genotypes; hence, this program can compare and select genotypes across multiple environment trials for a given data set. This program supports both the repeated data across environments and matrix data types. The accuracy of the results obtained from this software was tested on several crop plants.ConclusionsThis new software provides a user‐friendly interface to estimate stability statistics accurately for plant scientists, agronomists, and breeders who deal with large volumes of quantitative data. This software can also show ranking patterns of genotypes and describe associations among different statistics with yield performance through a heat map plot. The software is available at https://mohsenyousefian.com/stabilitysoft/.

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In this study, inter-simple sequence repeats (ISSR) and start codon targeted (SCoT) markers were used for genetic diversity and relationship analysis of nine Salvia species. Twenty-one and twenty selected ISSR and SCoT primers amplified 350 and 329 loci, respectively, of which all were polymorphic. The obtained average polymorphism information content (ISSR, 0.38; SCoT, 0.40), average band informativeness (ISSR, 16.67; SCoT, 16.45) and resolving power (ISSR, 9.75; SCoT, 12.52) revealed high genetic diversity prevailing among Salvia accessions. Considering the ISSR and SCoT data, the species with a basic chromosome number of x = 8 showed higher values of the percentage polymorphism loci (PPL), the number of observed alleles (Na) and Shannon index (I) than the other species. The partition of clusters in the neighbour-joining dendrogram based on ISSR, SCoT and combined data was similar and grouped all individuals into four clusters. However, the dendrogram generated based on SCoT separated the individuals into sub-clusters in accordance with their species and section. The Mantel test revealed a similar polymorphism distribution pattern between ISSR and SCoT techniques, the correlation coefficient (r) was 0.83, and the results showed that both techniques were effective to assess the genetic diversity. Our results indicated that SCoT markers can be used as a reliable and informative technique for evaluation of genetic diversity and relationships among Salvia species.

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)(2013)(2014)(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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