Multivariate diallel analysis allows multiple gains in segregating populations for agronomic traits in Jatropha

Teodoro, Paulo Eduardo; Rodrigues, Erina Vitório; Peixoto, L.A.; Silva, L.A.; Laviola, Bruno Galvêas; Bhering, Leonardo Lopes

doi:10.4238/gmr16019545

Cited by 3 publications

(6 citation statements)

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“…The use of the multivariate diallel was efficient for a more detailed study of the GCA and SCA combinations, together with the possibility of parental selection with the intention of improving certain traits simultaneously. Similar results were obtained in the literature by several authors, proving the efficiency of this type of analysis (Jung et al 2007;Benin et al 2009;Teodoro et al 2017).…”

Section: Discussionsupporting

confidence: 90%

“…To overcome this restriction, a multivariate diallel approach can be performed, enabling the study of two or more traits simultaneously (Ledo et al 2003). Research studies applying multivariate diallel analysis are available for several important crops, including wheat (Benin et al 2009), sweet passionfruit (Jung et al 2007), popcorn (Gonçalves et al 2014), Jatropha (Teodoro et al 2017) sweet pepper (Nascimento et al 2010) andsoybean (Amaral et al 2004).…”

Section: Introductionmentioning

confidence: 99%

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Uni and multivariate approaches for diallel analysis in early generation trials for soybean tolerance to rust

et al. 2019

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Due to the high impact of Asian soybean rust (SBR) in Brazilian croplands, several studies have been conducted in order to maintain or increase the grain yield gain over years in the presence of the pathogen. The aim of this study was to define a breeding strategy applying uni and multivariate approaches for diallel analyzes in early generation trials and contrasting disease conditions. Thus, assessing genetic parameters to identify traits related to greater tolerance to soybean rust. Deploying a North Carolina design II scheme (4 elite commercial cultivars × 10 rusttolerant experimental lines), we obtained 40 F 2 crosses that were evaluated in a randomized complete block design with 4 replicates. The crosses were conducted in two environments, contrasting only for the fungicide management (with and without rust control), enabling the estimation of the rust impact on 11 traits, divided

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Section: Discussionsupporting

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Uni and multivariate approaches for diallel analysis in early generation trials for soybean tolerance to rust

et al. 2019

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“…A análise dialélica multivariada já foi estudada em diversas culturas, como por exemplo: soja (AMARAL et al, 2004), milho (Gonçalves et al, 2014), trigo (Benin et al, 2009), pimentão (Nascimento et al, 2010), pinhão-manso (Teodoro et al, 2017). Esta análise baseia-se no teste de variância multivariada e na utilização de outras abordagens, como a correlação canônica, componentes principais e índices de seleção, com o objetivo de reduzir as dimensões dos dados e estudar as correlações entre as variáveis (Ledo et al, 2003).…”

Section: Análise Dialélica Multivariadaunclassified

“…As análises de variância dialélica multivariada estão apresentadas na tabela 9 para os manejos 1 e 2, individualmente. Para o ambiente com controle da ferrugem da soja, os efeitos aditivos (CGC I e CGC II) e não-aditivos (CEC) foram significativos, para as características adaptativas e reprodutivas, indicando que os genitores apresentaram alelos complementares e as características puderam ser analisadas juntas na análise dialélica multivariada (Amaral et al, 2004;Benin et al, 2009;Ledo et al, 2003;Nascimento et al, 2010;Teodoro et al, 2017). Para o manejo 2 (sem controle da ferrugem), apenas os efeitos aditivos (CGC I e CGC II) foram significativos para as variáveis adaptativas e, para as variáveis reprodutivas, apenas CGC II.…”

Section: Análise Dialélica Multivariadaunclassified

“…Os caracteres adaptativos referem-se a: número de dias para R5 (NDR5); escores da área foliar (EAF); arquitetura da planta (AP); altura da planta na maturidade (APM); acamamento (AC), valor agronômico (VA), número de dias para maturidade (NDM). Os caracteres reprodutivos são representados por: período de granação ou de enchimento de grãos (PEG); índice de colheita (IC); peso de cem sementes (PCS) e produtividade de grãos (PG 3 0,693 0,004 ** 0,749 0,001 ** 0,631 0 ** 0,757 0,001 ** CEC 27 0,144 0,004 ** 0,318 0,009 ** 0,181 0,092 ns 0,365 0,089 ns Erro 117 ** , * , ns : significativo a 1% e 5% e não significativo, respectivamente, pelo teste de aproximação do F (Harris, 1975 (Benin et al, 2009;Jung et al, 2007;Teodoro et al, 2017).…”

Section: Análise Dialélica Multivariadaunclassified

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Tolerância da soja à ferrugem asiática em dialelo multivariado e ambientes contrastantes de fungicidas

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Multiple-trait model through Bayesian inference applied to Jatropha curcas breeding for bioenergy

et al. 2021

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Multiple-trait model tends to be the best alternative for the analysis of repeated measures, since they consider the genetic and residual correlations between measures and improve the selective accuracy. Thus, the objective of this study was to propose a multiple-trait Bayesian model for repeated measures analysis in Jatropha curcas breeding for bioenergy. To this end, the grain yield trait of 730 individuals of 73 half-sib families was evaluated over six harvests. The Markov Chain Monte Carlo algorithm was used to estimate genetic parameters and genetic values. Genetic correlation between pairs of measures were estimated and four selective intensities (27.4%, 20.5%, 13.7%, and 6.9%) were used to compute the selection gains. The full model was selected based on deviance information criterion. Genetic correlations of low (ρg ≤ 0.33), moderate (0.34 ≤ ρg ≤ 0.66), and high magnitude (ρg ≥ 0.67) were observed between pairs of harvests. Bayesian analyses provide robust inference of genetic parameters and genetic values, with high selective accuracies. In summary, the multiple-trait Bayesian model allowed the reliable selection of superior Jatropha curcas progenies. Therefore, we recommend this model to genetic evaluation of Jatropha curcas genotypes, and its generalization, in other perennials.

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Multivariate diallel analysis allows multiple gains in segregating populations for agronomic traits in Jatropha

Cited by 3 publications

References 16 publications

Uni and multivariate approaches for diallel analysis in early generation trials for soybean tolerance to rust

Uni and multivariate approaches for diallel analysis in early generation trials for soybean tolerance to rust

Tolerância da soja à ferrugem asiática em dialelo multivariado e ambientes contrastantes de fungicidas

Multiple-trait model through Bayesian inference applied to Jatropha curcas breeding for bioenergy

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