Index selection can improve the selection efficiency in a rice recurrent selection population

Guimarães, P. H. R.; Melo, P. G. S.; Cordeiro, A. C. C.; Torga, P. P.; Rangel, P. H. N.; Castro, Adriano Pereira de

doi:10.1007/s10681-021-02819-7

Cited by 10 publications

(11 citation statements)

References 89 publications

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“…Based on the results of heritability estimation in the broad sense for the GY and FL traits varied in: h 2 = 0.39–0.757 and h 2 = 4.36E-07–9.21E-06; h 2 = 0.039–0.80, and 0.02–0.91, environment 1 and environment 2, respectively ( Table 3 ). [ 34 ] found heritability estimates of 0.48 and 0.94 for GY and FL, respectively, using 198 rice progenies by the ANOVA technique. [ 35 ] evaluated upland rice genotypes, by this same technique, and obtained an estimate of h 2 for GY and FL traits 0.35 and 0.77, respectively.…”

Section: Resultsmentioning

confidence: 99%

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Multi-trait and multi-environment Bayesian analysis to predict the G x E interaction in flood-irrigated rice

Silva

Sant’Anna²,

Siqueira

et al. 2022

PLoS ONE

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The biggest challenge for the reproduction of flood-irrigated rice is to identify superior genotypes that present development of high-yielding varieties with specific grain qualities, resistance to abiotic and biotic stresses in addition to superior adaptation to the target environment. Thus, the objectives of this study were to propose a multi-trait and multi-environment Bayesian model to estimate genetic parameters for the flood-irrigated rice crop. To this end, twenty-five rice genotypes belonging to the flood-irrigated rice breeding program were evaluated. Grain yield and flowering were evaluated in the agricultural year 2017/2018. The experimental design used in all experiments was a randomized block design with three replications. The Markov Chain Monte Carlo algorithm was used to estimate genetic parameters and genetic values. The flowering is highly heritable by the Bayesian credibility interval: h2 = 0.039–0.80, and 0.02–0.91, environment 1 and 2, respectively. The genetic correlation between traits was significantly different from zero in the two environments (environment 1: -0.80 to 0.74; environment 2: -0.82 to 0.86. The relationship of CVe and CVg higher for flowering in the reduced model (CVg/CVe = 5.83 and 13.98, environments 1 and 2, respectively). For the complete model, this trait presented an estimate of the relative variation index of: CVe = 4.28 and 4.21, environments 1 and 2, respectively. In summary, the multi-trait and multi-environment Bayesian model allowed a reliable estimate of the genetic parameter of flood-irrigated rice. Bayesian analyzes provide robust inference of genetic parameters. Therefore, we recommend this model for genetic evaluation of flood-irrigated rice genotypes, and their generalization, in other crops. Precise estimates of genetic parameters bring new perspectives on the application of Bayesian methods to solve modeling problems in the genetic improvement of flood-irrigated rice.

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Section: Resultsmentioning

confidence: 99%

“…[ 38 ], using the ANOVA method, found h 2 of 0.76 for FL and [ 39 ] estimate of 0.30 for GY. Regarding the estimate of CVs using the ANOVA method for the FL and GY traits, representing 2.98% and 15.28%, respectively [ 34 ].…”

Section: Resultsmentioning

confidence: 99%

Multi-trait and multi-environment Bayesian analysis to predict the G x E interaction in flood-irrigated rice

Silva

Sant’Anna²,

Siqueira

et al. 2022

PLoS ONE

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“…The major challenge in the use of multi-trait phenotypic selection indices is finding optimum economic weights across traits, which reflects the relative importance of one trait to another in making up the overall value of a line. The choice of economic weights not only affects the ranks of the selected parents for the next generation, but also the expected selection response, genetic gain, and the correlation between the index and the net genetic merit [ 47 , 62 , 63 , 78 ]. For such reasons, we assessed the selection parameters across 2304 economic weights, which demonstrated the significant effect of weights not only in the selection parameters ( Figure S1, Table S4 ), but also on the selected genotypes under each management system ( Table S7 ).…”

Section: Discussionmentioning

confidence: 99%

“…Most breeders, however, do not routinely use selection indices, due to the need for a priori knowledge of fixed effects, variance–covariance matrices, and relative economic weights [ 55 , 61 ]. When two or more traits are used, the assignment of an optimum economic weight for each trait has been cited as the major bottleneck for utilizing selection indices [ 62 , 63 , 64 ], which may reflect the relative economic value (market situation), preferences, retrospective results, generation interval, etc. [ 61 ].…”

Section: Introductionmentioning

confidence: 99%

“…Both methods require a predefined economic weight but differ in terms of restricting specific traits. There has been inconsistent reporting regarding relative economic weights [ 63 ], with some authors using no economic weights at all, some replacing economic weights with “desired gains” [ 67 ], while others assign relative economic weights either randomly [ 62 ] or using an algorithm [ 61 ], which forms another basis in the present study. The objectives of the present study were, therefore, to (i) determine the best economic weight combinations using four agronomic traits (maturity, plant height, grain yield, and GPC), (ii) compare single-trait selection and multi-trait selection indices (LPSI vs. RLPSI) with and without imposing restrictions on the expected genetic gain on grain yield or GPC, and (iii) select a subset of the top ~10% of the cultivars to serve as trait donors in developing improved germplasm for production under an organic management system.…”

Section: Introductionmentioning

confidence: 99%

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Identification of Spring Wheat with Superior Agronomic Performance under Contrasting Nitrogen Managements Using Linear Phenotypic Selection Indices

Iqbal

Semagn²,

Cerón-Rojas³

et al. 2022

Plants

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Both the Linear Phenotypic Selection Index (LPSI) and the Restrictive Linear Phenotypic Selection Index (RLPSI) have been widely used to select parents and progenies, but the effect of economic weights on the selection parameters (the expected genetic gain, response to selection, and the correlation between the indices and genetic merits) have not been investigated in detail. Here, we (i) assessed combinations of 2304 economic weights using four traits (maturity, plant height, grain yield and grain protein content) recorded under four organically (low nitrogen) and five conventionally (high nitrogen) managed environments, (ii) compared single-trait and multi-trait selection indices (LPSI vs. RLPSI by imposing restrictions to the expected genetic gain of either yield or grain protein content), and (iii) selected a subset of about 10% spring wheat cultivars that performed very well under organic and/or conventional management systems. The multi-trait selection indices, with and without imposing restrictions, were superior to single trait selection. However, the selection parameters differed quite a lot depending on the economic weights, which suggests the need for optimizing the weights. Twenty-two of the 196 cultivars that showed superior performance under organic and/or conventional management systems were consistently selected using all five of the selected economic weights, and at least two of the selection scenarios. The selected cultivars belonged to the Canada Western Red Spring (16 cultivars), the Canada Northern Hard Red (3), and the Canada Prairie Spring Red (3), and required 83–93 days to maturity, were 72–100 cm tall, and produced from 4.0 to 6.2 t ha−1 grain yield with 14.6–17.7% GPC. The selected cultivars would be highly useful, not only as potential trait donors for breeding under an organic management system, but also for other studies, including nitrogen use efficiency.

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Parental selection proposal strategy for recurrent selection in tropical wheat breeding

Signorini,

e Silva,

Lima

et al. 2023

Agronomy Journal

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Recurrent selection is an effective breeding method for improving complex traits in wheat (Triticum aestivum L.) breeding. In this study, we propose a genitor selection strategy, employing a multi‐trait selection approach and genetic diversity analysis. To evaluate our proposed method, we conducted a lattice design trial during the winter seasons of 2020 and 2021 in Viçosa, MG, Brazil, comprising 87 lines and 13 commercial cultivars. It was assessed various traits such as plant height, days to heading, wheat leaf rust, tan spot of wheat, a hundred grain mass, hectoliter weight, and grain yield, and the best linear unbiased predictor (BLUP) was obtained for each trait via restricted maximum likelihood (REML). Next, a genotypic distance matrix, derived from principal component analysis using the BLUP values, was computed to perform hierarchical clustering via the unweighted pair group method with arithmetic mean. The selection of superior genotypes was made utilizing the multi‐trait genotype‐ideotype distance index (MGIDI), resulting in the identification of eight lines and two commercial cultivars as candidates. Furthermore, the genetic diversity analysis revealed distinct clusters among the selected genotypes. To assess the divergence of the chosen genitors, this study proposes a complementarity matrix using factor analysis. Our results indicate that this selection strategy effectively directed the identification of the best and divergent genitors. The complementarity matrix developed in this paper to evaluate the crossings proposal can also be used to evaluate combinations in any panel selected via a multi‐trait approach and can be easily applied and interpreted by breeders of various programs.This article is protected by copyright. All rights reserved

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Index selection can improve the selection efficiency in a rice recurrent selection population

Cited by 10 publications

References 89 publications

Multi-trait and multi-environment Bayesian analysis to predict the G x E interaction in flood-irrigated rice

Multi-trait and multi-environment Bayesian analysis to predict the G x E interaction in flood-irrigated rice

Identification of Spring Wheat with Superior Agronomic Performance under Contrasting Nitrogen Managements Using Linear Phenotypic Selection Indices

Parental selection proposal strategy for recurrent selection in tropical wheat breeding

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