In any hybridization program, recognition of the best combination of two (or more) parental genotypes to maximize variance within related breeding populations, and as a result the chance of recognizing superior transgressive segregants in the segregating populations, are the most critical challenge to plant breeders. Since the combining ability was introduced in 1942, it has been widely adopted in plant breeding to compare performances of lines in hybrid combinations. In addition, the ability to predict optimal genotype combinations for different traits based on molecular-based genetic data would greatly enhance the efficiency of plant breeding programmes. This article reviews our current understanding of combining ability in plant breeding as well as recent advances in research in this field. It brings an introduction to combining ability and the concept of general and specific combining ability, methods for estimating combining ability, and QTL mapping of related traits.

The phenotype of an individual depends upon both the genetic make-up and environmental influences. Genotype × environment interaction is considered as an important source of discrepancy in any crop, and different methods have been used to distinguish genotypes for their behavior in different environmental conditions. These constitute univariate parametric, such as environmental variance, regression slope, and deviation from regression, to multivariate methods. In this review, we summarize the priorities and limitations of different parametric stability statistics, and also their correlations which might help agronomists and crop breeders to choose the proper methods for their analysis.

Starch accumulates in plants as granules in chloroplasts of source organs such as leaves (transitory starch) or in amyloplasts of sink organs such as seeds, tubers and roots (storage starch). Starch is composed of two types of glucose polymers: the essentially linear polymer amylose and highly branched amylopectin. The amylose content of wheat and rice seeds is an important quality trait, affecting the nutritional and sensory quality of two of the world's most important crops. In this review, we focus on the relationship between amylose biosynthesis and the structure, physical behaviour and functionality of wheat and rice grains. We briefly describe the structure and composition of starch and then in more detail describe what is known about the mechanism of amylose synthesis and how the amount of amylose in starch might be controlled. This more specifically includes analysis of GBSS alleles, the relationship between waxy allelic forms and amylose, and related quantitative trait loci. Finally, different methods for increasing or lowering amylose content are evaluated.

The brown rice variant had the lowest GI and II values but these advantages were lost with polishing.

The methods utilized to analyze genotype by environment interaction (GEI) and assess the stability and adaptability of genotypes are constantly changing and developing. In this regard, often instead of depending on a single analysis, it is better to use a combination of several methods to measure the nature of the GEI from various dimensions. In this study, the GEI was investigated using different methods. For this purpose, 18 sugar beet genotypes were evaluated in randomized complete block design in five research stations over 2 years. The additive effects analysis of the additive main effects and multiplicative interaction (AMMI) model showed that the effects of genotype, environment and GEI were significant for root yield (RY), white sugar yield (WSY), sugar content (SC), and extraction coefficient of sugar (ECS). The multiplicative effect's analysis of AMMI into interaction principal components (IPCs) showed that the number of significant components varies from one to four in the studied traits. According to the biplot of the mean yield against the weighted average of absolute scores (WAAS) of the IPCs, G2 and G16 for RY, G16 and G2 for WSY, G6, G4, and G1 for SC and G8, G10 and G15 for ECS were identified as stable genotypes with optimum performance. The likelihood ratio test showed that the effects of genotype and GEI was significant for all studied traits. In terms of RY and WSY, G3 and G4 had high mean values of the best linear unbiased predictions (BLUP), so they were identified as suitable genotypes. However, in terms of SC and ECS, G15 obtained high mean values of the BLUP. The GGE biplot method classified environments into four (RY and ECS) and three (WSY and SC) mega-environments (MEs). Based on the multi-trait stability index (MTSI), G15, G10, G6, and G1 were the most ideal genotypes.