Selection of inbred soybean progeny (<i>Glycine max</i>): an approach with population effect

Volpato, Leonardo; Simiqueli, Guilherme Ferreira; Alves, Rodrigo Silva; Rocha, João Romero do Amaral Santos de Carvalho; Conte, Murilo Viotto Del; Resende, Marcos Deon Vilela de; Carneiro, Pedro Crescêncio Souza; Silva, Felipe Lopes da

doi:10.1111/pbr.12648

Cited by 9 publications

(15 citation statements)

References 27 publications

(49 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The significance of the G × E interaction can affect selection of the best genotypes, hindering recommendation of new cultivars by breeders. Freiria et al (2018), Hamawaki et al (2018), and Volpato et al (2018) also found significance for interaction upon studying soybean genotypes.…”

Section: Resultsmentioning

confidence: 93%

Adaptability and yield stability of soybean genotypes by REML/BLUP and GGE Biplot

Gonçalves

Ferreira-Gomes

Lopes

et al. 2020

Crop Breed. Appl. Biotechnol.

View full text Add to dashboard Cite

The aim of this study was to ascertain the association between the REML/BLUP and GGE Biplot methodologies for selection of superior genotypes in regard to adaptability and yield stability for various regions of the Middle North region of Brazil. Sixteen soybean genotypes were evaluated in eight environments during the 2015/2016 and 2016/2017 crop seasons, analyzing the following traits: number of days to maturity, plant height, one hundred seed weight, and grain yield. In this study, the REML/BLUP and the GGE Biplot methods are highly correlated in terms of genotype ranking for selection and recommendation purposes. The genotypes BRASBT13-0528, M8372 IPRO, and BRASBT13-0621 most approximate a hypothetical ideal genotype.

show abstract

Section: Resultsmentioning

confidence: 93%

Adaptability and yield stability of soybean genotypes by REML/BLUP and GGE Biplot

Gonçalves

Ferreira-Gomes

Lopes

et al. 2020

Crop Breed. Appl. Biotechnol.

View full text Add to dashboard Cite

show abstract

“…Autogamous plant breeding programs have used several populations originating from the crosses between different parents evaluated in subsequent generations and experiments (Bernardo, 2014;de Resende et al, 2016;Pereira et al, 2017b;Pires et al, 2014). However, the partitioning of the progeny effect within populations has been neglected in the models applied to predicting genetic values (BLUPs) used in progeny selection (Volpato et al, 2018). In the present study, the populations are not randomly drawn from some larger population of possible populations; thus, this effect was fitted as fixed in the models fitted.…”

Section: Discussion Genetic Effects Of the Modelmentioning

confidence: 99%

Selection of Superior Inbred Progenies toward the Common Bean Ideotype

Rocha¹,

Nunes²,

Carneiro³

et al. 2019

Agronomy Journal

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…Furthermore, because some traits are correlated with each other, selection based on one of them leads to alterations in others [6,7]. In soybean breeding, most of the available methods for the selection of progeny or lines are useful in the analysis of a single trait measured either in a single environment [8,9] or in various environments with the incorporation of the G×E interaction [10–12]. However, researchers often face situations in which multiple traits are measured across multiple environments [13].…”

Section: Introductionmentioning

confidence: 99%

Multi-trait multi-environment models in the genetic selection of segregating soybean progeny

Volpato¹,

Alves²,

Teodoro³

et al. 2019

PLoS ONE

Self Cite

View full text Add to dashboard Cite

At present, single-trait best linear unbiased prediction (BLUP) is the standard method for genetic selection in soybean. However, when genetic selection is performed based on two or more genetically correlated traits and these are analyzed individually, selection bias may arise. Under these conditions, considering the correlation structure between the evaluated traits may provide more-accurate genetic estimates for the evaluated parameters, even under environmental influences. The present study was thus developed to examine the efficiency and applicability of multi-trait multi-environment (MTME) models by the residual maximum likelihood (REML/BLUP) and Bayesian approaches in the genetic selection of segregating soybean progeny. The study involved data pertaining to 203 soybean F 2:4 progeny assessed in two environments for the following traits: number of days to maturity (DM), 100-seed weight (SW), and average seed yield per plot (SY). Variance components and genetic and non-genetic parameters were estimated via the REML/BLUP and Bayesian methods. The variance components estimated and the breeding values and genetic gains predicted with selection through the Bayesian procedure were similar to those obtained by REML/BLUP. The frequentist and Bayesian MTME models provided higher estimates of broad-sense heritability per plot (or heritability of total effects of progeny; ) and mean accuracy of progeny than their respective single-trait versions. Bayesian analysis provided the credibility intervals for the estimates of . Therefore, MTME led to greater predicted gains from selection. On this basis, this procedure can be efficiently applied in the genetic selection of segregating soybean progeny.

show abstract

Selection of inbred soybean progeny (Glycine max): an approach with population effect

Cited by 9 publications

References 27 publications

Adaptability and yield stability of soybean genotypes by REML/BLUP and GGE Biplot

Adaptability and yield stability of soybean genotypes by REML/BLUP and GGE Biplot

Selection of Superior Inbred Progenies toward the Common Bean Ideotype

Multi-trait multi-environment models in the genetic selection of segregating soybean progeny

Contact Info

Product

Resources

About