Factor analysis applied to the G+GE matrix via REML/BLUP for multi-environment data

Peixouto, Leandro Santos; Nunes, José Aírton Rodrigues; Furtado, Daniel Ferreira

doi:10.1590/1984-70332016v16n1a1

Cited by 23 publications

(21 citation statements)

References 19 publications

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“…Other authors reported similar results, reinforcing that this method leads to a refinement of cultivar selection, as well as having the advantage of providing results on the proper measurement scale of the trait (Carbonell et al, 2007). The GE interaction is one of the great challenges for breeders, particularly in the evaluation stages of genotypes for recommendation for producers, since the experiments are carried out at different locations and in different years and growing seasons (Peixouto et al, 2016).…”

Section: Traits With H Gmentioning

confidence: 99%

Estimation of genetic parameters and prediction of genotypic values in common beans using mixed models#

Delfini¹,

Neto²

2019

Emir J Food Agric

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In the international scenario of agriculture, Brazil stands out as the main producer and consumer of common bean (Phaseolus vulgaris L.) The increase in the productive potential of the crop is mainly due to breeding programs. The objective of this study was to estimate genetic parameters, predict genotypic values with REML/BLUP (Restricted Maximum Likelihood/Best Linear Unbiased Prediction) and, based on these values, study the variability in common bean cultivars with carioca and black grain. Twenty three agromorphological descriptors were evaluated, among them grain yield. Deviance analysis detected significant differences between the cultivars in both groups. Selective accuracy (Ac) was considered high for most of the traits. Broad-sense heritability ( ) ranged from 0.05 to 0.72, but it was low for the trait yield (YLD). In the carioca grain group, the values for the traits related to plant morphology were higher than in the black group. Nevertheless, the values in the black group were higher in relation to the pod and seed traits. The correlations for YLD were moderate but different in the two commercial groups studied. In the black group, variables related to the seed morphology were correlated with grain yield, and in the carioca group, traits related to seed quantity. Based on the groupings, variability among the cultivars was observed. Three distinct clusters were formed for the carioca group and four for the black group. Based on the predicted genetic values, genetic variability and the most adapted and stable cultivars were detected among the cultivars in the studied environments. #This article has been posted in Preprints with a doi 10.20944/preprints201803.0215.v1 (https://www.preprints.org/manuscript/201803.0215/v1) (Creative Commons CC BY 4.0 license) *Corresponding author, Email: leandrosag@uel.br

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Section: Traits With H Gmentioning

confidence: 99%

Estimation of genetic parameters and prediction of genotypic values in common beans using mixed models#

Delfini¹,

Neto²

2019

Emir J Food Agric

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“…The mixed-model methods, such as the restricted maximum likelihood/best linear unbiased prediction (REML/BLUP), enable the estimation of variance components and the prediction of genetic values free of environmental effects (Peixouto et al, 2016). The following methods may be used: the harmonic mean of genotypic values (HMGV), in order to infer mean and stability; the relative performance of predicted genotypic values (RPGV), to analyze the genotypic adaptability and the mean yield; and the harmonic mean of the relative performance of predicted genotypic values (HMRPGV), to identify highlyproductive, adapted, and stable genotypes (Gomez et al, 2014;Costa et al, 2015;Spinelli et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Agronomic performance of modern soybean cultivars in multi-environment trials

Matei

Benin

Woyann

et al. 2017

Pesq. agropec. bras.

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The objective of this work was to evaluate the productive performance, and the adaptability and stability parameters of modern soybean (Glycine max) cultivars in multi-environment trials, as well as to identify the ideal genotypes for eight growing environments in Brazil. A randomized complete block experimental design was carried out, with three replicates, for the evaluation of 46 soybean cultivars in eight environments, in the microregions of adaptation 102, 201, and 202, in the 2014/2015 crop season. A complex genotype x environment interaction occurred, with changes in the ranking of genotypes among locations. The NA 5909 RG, M6410IPRO, NS 5959 IPRO, NS6823RR, M5917IPRO, NS 6767 RR, and 6563RSF IPRO cultivars showed the highest mean yields. The NA 5909 RG, NS6823RR, M6410IPRO, and NS 5959 IPRO cultivars showed high adaptability and stability and high grain yield, in the evaluated environments, and were ranked next to the ideal genotype for the analyzed environments. There are modern soybean cultivars, which are adapted, stable, and highly productive, for cultivation in the microregions 102, 201, and 202 for soybean crop adaptation in Brazil.

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“…However, in the case of G × E, the two-stage method tends to fit easily compared to traditional methods, such as the factor-analytic method. e factor-analytic method tries to simplify a complex covariance structure and, in some cases, for example, in the case of G × E, difficulty in reaching convergence [32].…”

Section: A1 A2mentioning

confidence: 99%

A Two-Stage Method for Improving the Prediction Accuracy of Complex Traits by Incorporating Genotype by Environment Interactions inBrassica napus

Xiong

Wang

Zou

et al. 2020

Discrete Dynamics in Nature and Society

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Improving the prediction accuracy of a complex trait of interest is key to performing genomic selection (GS) for crop breeding. For the complex trait measured in multiple environments, this paper proposes a two-stage method to solve a linear model that jointly models the genetic effects and the genotype × environment interaction (G × E) effects. In the first stage, the least absolute shrinkage and selection operator (LASSO) penalized method was utilized to identify quantitative trait loci (QTL). Then, the ordinary least squares (OLS) approach was used in the second stage to reestimate the QTL effects. As a case study, this approach was used to improve the prediction accuracies of flowering time (FT), oil content (OC), and seed yield per plant (SY) in Brassica napus (B. napus). The results showed that the G × E effects reduced the mean squared error (MSE) significantly. Numerous QTL were environment-specific and presented minor effects. On average, the two-stage method, named OLS post-LASSO, offers the highest prediction accuracies (correlations are 0.8789, 0.9045, and 0.5507 for FT, OC, and SY, respectively). It was followed by the marker × environment interaction (M × E) genomic best linear unbiased prediction (GBLUP) model (correlations are 0.8347, 0.8205, and 0.4005 for FT, OC, and SY, respectively), the LASSO method (correlations are 0.7583, 0.7755, and 0.2718 for FT, OC, and SY, respectively), and the stratified GBLUP model (correlations are 0.6789, 0.6361, and 0.2860 for FT, OC, and SY, respectively). The two-stage method showed an obvious improvement in the prediction accuracy, and this study will provide methods and reference to improve GS of breeding.

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Factor analysis applied to the G+GE matrix via REML/BLUP for multi-environment data

Cited by 23 publications

References 19 publications

Estimation of genetic parameters and prediction of genotypic values in common beans using mixed models#

Estimation of genetic parameters and prediction of genotypic values in common beans using mixed models#

Agronomic performance of modern soybean cultivars in multi-environment trials

A Two-Stage Method for Improving the Prediction Accuracy of Complex Traits by Incorporating Genotype by Environment Interactions inBrassica napus

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