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Showing 3 results for Type of Study: Applicable
Omidali Akbarpour,
Volume 4, Issue 1 (9-2017)
Abstract
To conduct any breeding program, understanding of the genetic structure of traits and effect of environment and genetic by environment interaction as well as effects of random or fixed in the analysis of results is essential. Subsequently, analysis of variances and variance components are important in plant and animal breeding. The ANOVA is one of the best estimators for variance components. But this estimator is not preferred to maximum likelihood (ML) and Restricted Maximum Likelihood (REML) methods when variance components are negatively estimated and unbalanced datasets arise. Therefore, the objective of this research is a review of comparison of estimates of variance components using ANOVA, ML and REML method in linear mixed models using experimental data.
Mohammad Reza Jafarzadeh Razmi, Saeid Navabpour, Hossein Sabouri, Seyedeh Sanaz Ramezanpour,
Volume 6, Issue 2 (3-2020)
Abstract
In order to analyze the genetic components of agronomic traits among 116 F9 recombinant lines derived from crosses of Ahlamitarom × Sepidroud rice cultivars, an experiment was conducted as a randomized complete block design in research farm of Gonbad Kavous University of Agriculture with three replications in 2016 and 2017. Genetic linkage map provided with 80 SSR markers, 28 iPBS Markers (79 polymorphic alleles), 7 IRAP markers (17 polymorphic alleles) and 26 ISSR markers (70 polymorphic alleles), which covered 1275.4 cM of the rice genome. QTL analysis was performed by Composite Interval Mapping. In two years, 15 QTLs detected for the studied traits. The additive effected varied from 6.725 g for grain weight up to -85.626 g for grain weight. Also, R2 for the detected QTLs explained from 11.3% to 20% of the total variation. The highest R2 was related to grain weight in the first year of experiment. Among the detected QTLs, qGWs on chromosome 1, were found to be stable and large effector QTLs for rice (Oryza sativa L.) grain weight, and can be used in marker-assisted breeding and selection programs after validation.
Samaneh Akbari, Omidali Akbarpour, Payam Pezeshkpour,
Volume 8, Issue 1 (8-2021)
Abstract
The challenge of the interaction of genotype × environment is one of the main issues in plant breeding. Various statistical methods to estimate the interaction of genotype × environment and choice the stable and productive genotype(s) have been introduced. In this study, 14 lentil genotypes along with two controls (Sepehr and Gachsaran cultivars) were evaluated during four growing seasons (2016-2020). The experiments were conducted in a randomized complete blocks design in three replications at Sarab Changai Agricultural Research Station, Khorammabad (Iran). The combined analysis of variance was used to investigate the interaction of genotype × environment, and results of the analysis showed significant effects for genotype, year, and genotype × environment interaction. Genotypes G5 (FLIP2014-032L) and G12 (ILL8006) were introduced based on Si(1), Si(2), and NPi(1) statistics as stable and high-yielding genotypes. Based on various non-parametric statistics, genotypes G5 (FLIP2014-032L) with a mean grain yield of 1574.68 kg.ha-1 and G12 (ILL8006) with a mean grain yield of 1333.6 kg.ha-1 were introduced as stable genotypes. The heritability rate was estimated on the plot mean basis for yield trait in four years (0.61 ± 0.18) which indicated the capability of the studied genotypes to be selected and improved for grain yield. Based on the results of cluster analysis, the genotypes were divided into three main clusters. The highest distance was observed between the second and third groups. The first cluster included highly stable genotypes.
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