Core Ideas Partitioning of the progenies effect within populations has several advantages. FAI‐BLUP index capitalizes the progenies × growth seasons interaction. The selected inbreed progenies showed favorable genotypes for the target traits. Genetic breeding towards the common bean ideotype can accelerate the cultivar release. ABSTRACTThe goal of breeding programs is selection toward the ideal plant type. In this study, field experiments were performed to select common bean inbred progenies that maximize the probability of extracting superior lines. A total of 124 inbred progenies of three consecutive generations (F2:3, F2:4, and F2:5) were conducted in field experiments over three different environments (one generation in each environment). Seven different traits, related to disease severity, commercial acceptance grain, and yield, were evaluated by best linear unbiased prediction. This work underscored the importance of incorporating population information into the statistical model as a means of comparing progenies from different populations with higher efficacy, even when kinship information between populations is not available. Toward the common bean ideotype, 20 inbred progenies of greater potential were selected using the factor analysis and genotype‐ideotype distance (FAI‐BLUP) index. This index is based on the structural equation models by joining the factor analysis technique (exploratory factor analysis) with the ideotype design (confirmatory factor analysis). The predicted genetic gain was increased for all the traits in all generations. Selection strategies that capture the multitrait information capitalize the progenies × growth season interactions and are based on the ideotype, such as as the FAI‐BLUP index, have the potential for use in genetic breeding toward the common bean ideotype and can accelerate the release of more adapted cultivars.
To meet the growing demand of the soybean consumer market, cultivars increasingly early, productive and resistant to biotic and abiotic stress are sought. Several populations are obtained in soybean breeding programmes, but progeny are selected without being weighted for their respective population effect. As a consequence, progeny originating from high-merit populations may be discarded too early. Given this scenario, this study proposes to employ the selection index with progeny and population effect via best linear unbiased prediction (SIPP-BLUP) for the genetic selection of early and productive soybean progeny. A total of 180 progeny derived from three populations were evaluated for yield-related traits. Genetic gains from selection, Spearman correlation and coincidence index were used to check the efficiency of the models with and without the population effect. The SIPP-BLUP index achieved greater selection accuracy and was efficient in the identification and future selection of early soybean progeny. Therefore, this study demonstrates that soybean breeding programmes should consider the population effect via SIPP-BLUP in progeny selection to obtain future lines that really contribute to genetic gain. K E Y W O R D Saccuracy, breeding values, earlier-maturing progeny, genetic gain, genotype × environment interaction, mixed models, soybean seed yield
Reaction norms fitted through random regression models (RRM) have been widely used in animal and plant breeding for analyses of genotype × environment (G × E) interaction. However, in annual crops, they remain unexplored. Thus, this study aimed to evaluate the applicability and efficiency of RRM fitted through Legendre polynomials as a tool to recommend cotton (Gossypium hirsutum L.) genotypes. To this end, a data set with 12 genotypes of cotton evaluated in 10 environments for fiber length (FL) and fiber fineness was used. The restricted maximum likelihood/best linear unbiased prediction (REML/BLUP) procedure was used to estimate the variance components and to predict the genetic values. Results showed that there was genetic variability among cotton genotypes and that the reaction norms over the environmental gradient illustrated the G × E interaction. Very high selective accuracies (̂> 0.90) were found for both traits in all environments, which indicates high reliability in the genotype's recommendation. The areas under the reaction norms were calculated for the recommendation of genotypes for unfavorable, favorable, and overall environments. Regarding genotypes recommendation, areas under reaction norms allow recommending genotypes for unfavorable and favorable environments, as well as for overall recommendation, for both traits. This study is the first considering reaction norms fitted through RRM for the recommendation of cotton genotypes and demonstrated the potential of this technique in cotton breeding, besides its great potential to deal with G × E interactions.
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