Seleção de progênies F₂ de feijão-vagem para produção via REML/BLUP

RESUMO

Este trabalho objetivou selecionar progênies F₂ de feijão-vagem para caracteres relacionados à produção de vagens e grãos através do procedimento REML/BLUP. O experimento foi conduzido na área experimental do Instituto Federal Fluminense (IFF), localizado no município de Bom Jesus do Itabapoana-RJ. Foram semeadas linhas de 55 genótipos, entre eles, 42 progênies F₂ oriundas de cruzamentos dialélicos, além dos 13 parentais, utilizados como testemunhas. Cada linha foi composta por 24 plantas, sem repetições e o espaçamento utilizado foi de 1,0x0,5 m. As plantas foram colhidas e avaliadas individualmente para as características produção de vagens verdes por planta e produção de grãos por planta. Foi realizada a seleção entre e dentro de progênies via BLUP utilizando-se o programa Selegen-REML/BLUP. Com base na Máxima Verossimilhança Restrita (REML), foi possível estimar os coeficientes de variação genético e residual de 19,43% e 33,53%, respectivamente para produção de vagens verdes por planta e produção de grãos por planta. Utilizou-se 7,9% de intensidade de seleção, totalizando 100 plantas selecionadas com base no valor genotípico individual. A acurácia estimada para a seleção de progênies foi de 0,5014 para a produção de vagens e 0,5130 para produção de grãos, indicando dificuldades na seleção dos caracteres devido à alta influência ambiental sobre as características de produção. Apesar disso, a predição dos valores genéticos estimados por meio do Melhor Preditor Linear Não-Viesado (BLUP), apontou os cruzamentos Feltrin x UENF 15-23-4, UENF 7-5-1 x UENF 9-24-2 e Feltrin x UENF 14-3-3 como as progênies mais promissoras, com ganhos de 65,66 g, 61,49g e 57,63 g, respectivamente na produção de vagens verdes e 52,45 g, 46,96 g e 49,29 g, respectivamente na produção de grãos. O ganho genético aditivo predito com a seleção foi de 36,05% na produção de grãos, e 33,5% na produção de vagens verdes. Conclui-se que a seleção via BLUP para a produção de vagens e grãos possibilitou a predição e obtenção de ganhos genéticos significativos para o melhoramento do feijão-vagem nas próximas gerações.

Keywords:
Phaseolus vulgaris; green pods; genetic gain.

ABSTRACT

This study aimed to select snap bean F₂ progenies for traits related to production of pods and grains using the REML/BLUP procedure. The experiment was carried out at the experimental area of Instituto Federal Fluminense, located in Bom Jesus do Itabapoana, Rio de Janeiro State, Brazil. The 55 genotypes were sown in lines, 42 F₂ progenies derived from diallelic crosses, and 13 parental genotypes used as controls. Each line consisted of 24 plants, without replications and at a spacing of 1.0x0.5 m. The plants were harvested and evaluated individually for production of green pods and grains per plant.The selection among and within progenies via BLUP using Selegen-REML/BLUP program was carried out. Based on the Restricted Maximum Likelihood (REML), we estimated genetic and residual coefficients of variation of 19.43% and 33.53%, respectively, for green pod production and grain production. We used 7.9% of selection intensity, totaling 100 plants selected based on individual genotypic value. The estimated accuracy for the selection of progeny was 0.5014 for pod production and 0.5130 for grain production, indicating difficulties in the selection of these traits due to the high environmental influence on the production traits. Nevertheless, the prediction of genetic values via BLUP, showed the progenies derived from crosses Feltrin x UENF 15-23-4, UENF 7-5-1 x UENF 9-24-2 and Feltrin x UENF 14-3-3 as the most promising progenies with gains of 65.66 g, 61.49 g and 57.63 g, respectively, for pod production and 52.45 g, 46.96 g and 49.29 g, respectively, for grain production. The predicted additive genetic gain with selection was of 36.05% for grain production and 33.5% for green pod production. We concluded that selection via BLUP for production of pods and grains enabled to predict and obtain significant genetic gains for genetic improvement of snap beans for the next generations.

Keywords:
Phaseolus vulgaris; green pods; genetic gain.

Snap bean (Phaseolus vulgaris, 2n= 2x= 22), just like the common beans, is a predominantly autogamous, diploid species. It was domesticated for over seven thousand years ago, in two centers of origin: Mesoamerican (Mexico and Central America) and Andean region (Vieira, 1999).

Snap bean is a very important vegetable worldwide. The pods are harvested when immature and they are consumed both in natura or industrialized. In Brazil, this vegetable is traditionally produced by family farmers, who still use a small number of cultivars of indeterminate-growth-habit under protected cultivation (Peixoto et al., 2002).

The crop has proven to be promising, among the vegetables which have been grown in Brazil, since it has reached the sixth position in production (CEASA, 2010); especially the Southeastern Region, producing about 37 thousand t/year and the state of Rio de Janeiro which is responsible for about 21% of this amount. Nevertheless, despite being very popular in the North and Northwest of the State, these regions still have low participation in this agricultural crop production due to the unfavorable weather conditions for cultivation (Vilela et al., 2011).

UENF develops a snap-bean breeding program aiming to obtain promising cultivars for productivity of pods for the North and Northwest regions of the State. Silva (2013) characterized genetic diversity of 33 accessions from Active Germplasm Bank of snap bean of UENF using multivariate procedures based on 37 minimum morphological descriptors for snap bean. Then, diallelic crosses were carried out among 12 divergent accessions of the Bank, F₁ generation was evaluated together with parental genotypes based on Griffing’s model 2 (1956). Thenceforth, the authors started this research aiming to select F₂ breeding lines with better pod yield under environmental conditions in the North region of the state of Rio de Janeiro, in order to ensure continuity of the breeding program.

Thus, the authors observed that the breeding programs of the species should be implemented to select and obtain genotypes more adapted with quality and production of superior pods in order to be recommended to the producers of these regions (Vilela et al., 2011). The authors believe that superior genotypes should be selected based on, mainly, genetic components, contributing both as subsidy for planning efficient breeding strategies, as well as for identifying the nature of action of the genes involved in controling the traits to be improved. Another benefit of well estimated genetic components, according to Resende (2007b), is the possibility to predict the genotypic values of the selecting individuals, since the prediction procedure of genetic values depends on the identification of the genetic control of traits under selection, especially concerning individual heritability and repeatability parameters.

The authors consider the need of using specific methods when the aim is to estimate genetic parameters and to predict genetic values. According to Resende (2007b), the standard procedure recommended for quantitative and genetic analysis and for selection based on genotypic values is the REML/BLUP methodology. That means, the authors used the Restricted Maximum Likelihood Method (REML) to estimate the variance components and Best Linear Unbiased Prediction Method (BLUP) to predict genotypic values. The REML/BLUP methodology has been used as a tool associated with progeny selection in several crops like popcorn (Freitas et al., 2013), coffee (Resende et al., 2001; Pereira et al., 2013), sugar cane (Resende & Barbosa, 2006), papaya (Oliveira et al., 2012), common beans (Chiorato et al., 2008) and others. In plant breeding, its use is relatively little, comparing to its use for animal breeding; the reason is because the experiments with plants are generally balanced (Resende, 2007b).

The aim of this work was to select snap bean F₂ progenies for production traits using REML/BLUP methodology.

MATERIAL AND METHODS

The experiment was carried out in the experimental field of Instituto Federal Fluminense, located in the municipality of Bom Jesus de Itbapoana, Northwest of the state of Rio de Janeiro (21º08’S, 41^o40’W, 88 m altitude). The local climate is Aw, according to Köppen classification, alternating hot and rainy season with dry season and average annual temperature ranging from 22 to 25°C and average annual rainfall from 1200 to 1300 mm.

The genetic material in this work consisted of F₂ population derived from a research carried out by Silva (2013), in which the author aimed to evaluate, through partial diallel 6 x 6, twelve snap bean genotypes (Table 1), chosen from 37 morphologic and agronomic traits, in accordance to the Form of Minimum Morphological Descriptors for Beans (Formulário de Descritores Morfológicos Mínimos de Feijão) (Phaseolus vulgaris), recommended by the National Service of Plant Varieties Protection [Serviço Nacional de Proteção de Cultivares (SNPC)], in order to select the best parents for generating productive populations and for the advancement of generations using SSD method (Single Seed Descent).

Table 1
Genotypes used in crosses and main morphological characteristics. Campos dos Goytacazes, UENF, 2014.

The authors evaluated 55 snap bean genotypes; 42 F₂ progenies were obtained from diallelic crosses and 13 parents used as control. Among the parents, three cultivars and 10 lines, from the Snap Bean Breeding Program of Universidade Estadual Norte Fluminense Darcy Ribeiro, were used.

The F₂ plants were arranged in the experimental field without the establishment of experimental plots, being 24 plants of each of F₂ progenies properly arranged and spaced with their parents. Each plant was arranged in spacing of 1.0x0.5 m.

Sowing was carried out at a depth of 2.5 cm, with two seeds per pit. During the experiment, cultural and phytosanitary practices were carried out according to the recommendations for the crop, according to Filgueira (2008), as well as sprinkler irrigation.

The progenies were evalutated in relation to pod and grain production traits, considering the weight of pods per plant (PVP), only the weight of green pods per plant, in grams, was considered (PGP). Analyses were carried out using the individual value of the plants of each progeny.

The authors used individual analyses referring to the values per plant for the two traits used for selection. The authors used the program SELEGEN - REML/BLUP (Restricted Maximum Likelihood - the Best Linear Unbiased Estimation) for estimation and prediction of genetic values. BLUP was the procedure adopted by the program for predicting the genetic values, using variance estimates obtained through REML method, presented by Resende (2007b). Variables were analyzed according to the following model (Resende, 2007b):

Where y, r, a, and e are individual data vectors, repetition effects (fixed), individual additive genetic effects (random), respectively; X and Z are incidence matrices for repetition effects and for individual additive genetic effects, respectively.

Random effects were assumed to be uncorrelated and to be normally distributed.

RESULTS AND DISCUSSION

The traits grain production per plant (PGP) and pod production per plant (PVP) showed genetic variance of 465.69 and 792.08, respectively (Table 2). Estimate of genetic variance (s_g ²) among the progenies, when presenting positive values and different from zero, shows variability among progenies due to genotype and, consequently, the possibility of selecting superior breeding lines for each trait evaluated. Therefore, these values reflect considerable genetic variability or difference among genotypes to be used for selection, mainly for pod production, since this is the most economically relevant trait for the crop.

Table 2
Genetic parameters estimated for grain yield per plant (PGP) and pod production per plant (PVP) and the general mean of the progenies. Campos dos Goytacazes, UENF, 2014.

• σ g 2= genotypic variance; σe2= environmental variance; σf2= phenotypic variance; h2a= broad sense heritability; h2 mp = heritability of progenies average; Ac prog = progenies selection accuracy; CV g (%)=genetic variation coefcient; CV e(%)=environmental variation coefcient; CVr= relative variation coefcient; (CVr = CV g /CV e).

The authors highlight that the phenotypic variation should be composed, most part, of variations from genotype of the selection candidates, contributing to higher heritability of the selected character. From broad-sense heritability anlysis (h² _a) for PGP and PVP traits, the authors observed values around 0.2632 for grain production and 0.2514 for pod production. These values show that 26.32% and 25.14% of the variation for traits PGP and PVP, respectively, are due to genetic causes. Heritability values for the traits showed to be seriously influenced by the high environmental variance obtained; this fact hinders the successful selection based on these traits. However, even being considered of low magnitude, these values are relevant in order to improve these traits, since they are polygenic traits, any gain for such traits should be considered.

Heritability values for PGP and PVP showed to be superior to the estimated by Coelho et al. (2002), which obtained broad-sense heritability values of 0.19 for grain production and 0.25 for pod production in the evaluation of F₂ common bean breeding lines from the cross Ouro 1919 x Milionário 1732 summer-autumn season. Evaluating production components in common bean genotypes, using REML/BLUP methodology, Chiorato et al. (2008) obtained heritability values for grain production superior to the ones obtained in this study, in which broad-sense heritability was estimated at 0.34, and heritability based on the line average estimated at 0.75. According to the authors, this fact allowed greater selective accuracy (0.87) due to the use of a greater number of replications in the experimental model and evaluation of fewer number of plants in a row.

Broad-sense heritability values can show great variation; factors like progenies of different origins and different levels of environmental influence on traits may be crucial in order to obtain better estimates for this parameter (Pereira et al., 2013). Thus, the authors highlight that heritability is not an immutable trait, being not only a property of the characteristic, but also a property of population and environmental conditions in which the population was submitted (Cruz, 2005).

Heritability based on progeny average is determined considering the number of replications and plants evaluated per plot in the experient (Chiorato et al., 2008). In this work, heritability based on the progeny average for PGP and PVP maintained the same broad-sense heritability estimates. This fact occurred in order to estimate genetic parameters, the authors used only data at level of plants within progenies and consequently, without replications, being discarded the progeny averages.

Selective accuracy estimated for PGP showed an average of 0.513, whereas for PVP the average was 0.501; these values reflect dificulties for selecting based on these traits. According to Resende & Duarte (2007), selective accuracy can range from 0 to 1, classified very high (Ac_prog≥0.90), high (0.70≤Ac_prog≤ 0.90), moderate (0.50≤Ac_prog≤0.70), and low (Ac_prog<0.50). The authors observed levels of accuracy for two traits within the interval considered moderate. However, the authors verified the possibility to increase accuracy through experimental measures which search to minimize the environmental influence on genotypes and consequent increase in resulting heritability, keeping the size of the experiment.

Selective accuracy is associated to precision and represents correlation between the predicted genetic values and true genetic values of the candidate for selection. This reliability factor is a function of the coefficient of genotypic determination associated with the trait evaluated, which corresponds to the heritability coefficient, in a intrapopulation selection process (Resende & Duarte, 2007).

The traits PGP and PVP showed values close to CV_g (%) (20.491 and 19.434, respectively), which indicate relative magnitude of changes of a character due to genetic action, being directly proporcional to genetic variance and allowing the breeder a better notion of genetic variability and, consequently, advances that can be obtained through the selection of a particular trait. These values for CV_g show that the selection of better progenies will allow a significant increase of the population genetic value for the traits evaluated.

Another important parameter to define the best breeding strategy for each trait is the coefficient of relative variation (CV_r) or variance índex (I_v), as this is the ratio between CV_g and CV_e. Therefore, this coefficient is not influenced by the average of the trait. According to Vencovsky (1987), when this ratio is close to or above 1, a favorable situation of selection for a particular trait is characterized. Based on this parameter, the traits PGP and PVP can provide acceptable genetic gains, since the magnitude of the CV_r was 0.597 and 0.579, respectively. These values show a higher proportion of genetic variation in relation to the environmental influence, favoring selection process.

The authors highlight the importance of reporting the main advantages of using the mixed model methodology (REML/BLUP) in the simultaneous estimation of genetic parameters and genotypic values prediction in a breeding program are presented by Resende et al. (2001): it can be applied to unbalanced data; (b) it does not require data obtained under strict experimental structures; (c) it allows simultaneous use of a great number of information from various experiments, generating more accurate estimates; (d) it corrects data for environmental effects and it predicts accurately and untainted genotypic values, maximizing genetic gain with selection. As a result, the authors can use the data sets that were taken, usually, in breeding programs, which had never been used in the estimation of genetic parameters, as a matter of appropriate methodology deficiency contemplating especially the unbalancing.

Selection among progeny

F₂ progenies obtained an average value of 105.31 per plant for grain production and 144.81 for pod production per plant. Although these traits to selection of individual plants have been little used in previous studies on snap bean breeding program, these values are relatively low comparing to the ones obtained by Francelino et al. (2011), working with the parental lines present in this study under the same environmental conditions in Bom Jesus do Itabapoana. This indicates that direct interference of some environmental abnormalities on the experiment was noticed.

In a total of F₂ progenies (42 evaluated among 55 genotypes), 27 of these showed for their new averages, values superior than the best control for grain production in this study, the parental line UENF 7-5-1. The authors highlight that this line has obtained satisfactory and constant productive performance considering the study carried out by Francelino et al. (2011), in which the same line was the most productive. These data reinforce not only the line potential but also contribute to indicate the possibility of selecting progenies with superior performance for this trait. Considering trait PVP, 24 F₂ progenies showed productive performance superior to the best control, which was the parental line UENF 7-10-1 for this trait. The high performance of this line for pod production compared to the other controls is also in accordance with the results obtained by Francelino et al. (2011). In this research, line UENF 7-10-1 showed to be the most productive for the trait under environmental conditions in the North region of Rio de Janeiro State.

The progenies 14, 12, 24, 26, 27, 16, 25, 19, 21 and 36 stood out among others (Table 3), since they showed greater genetic gains for PGP and will certainly contribute to further advances for the trait. From these progenies, the authors highlight that four of them are from line UENF 7-6-1 and three are from the just mentioned UENF 7-5-1. Ten progenies showing greater gains for PGP showed average genetic gain in the next generation of 40.876 g/plant; this value can vary according to the selection pressure to be exercised in the line selection.

Table 3
Prediction of genetic effects, predicted genetic gain and new mean of the improved population for the trait grain yield per plant in snap beans F₂ progeny evaluated at Bom Jesus do Itabapoana, Rio de Janeiro State, Brazil. Campos dos Goytacazes, UENF, 2015.

Table 3 presents the top-ranked genotypes, which showed greater gains and higher new averages. The progeny 14, from the cross Feltrin x UENF 15-23-4, showed to be superior than the others, with a gain of 52.455 g/plant in relation to the population average if selected, thus providing a new average of 157.769 g/plant for PGP. Genotypes 46, 40, 49 and 54 obtained lower PGP in this study, with gains ranging from 2.215 to 0 g/plant. These genotypes, if selected, provided new population averages between 107.439 to 105.314 g/plant. Among them, the authors notice only one progeny, from the cross UENF 7-10-1 x UENF 14-3-3, and three parental lines used as controls, UENF 7-14-1, UENF 7-3-1 and UENF 15-23-4.

Considering trait PVP (Table 4), the authors noticed a high degree of correspondence between progenies with the best performance for this trait and the best-ranked progenies for PGP, for the interdependence between both traits evaluated. Progenies 14, 24, 12, 26, 27, 36, 25, 22, 19 and 16 stood out for green pod production in this study; when selected, these progenies will provide predicted average genetic gain for the next generation population of 50.642 g/plant for this trait.

Table 4
Prediction of genetic effects, predicted genetic gain and new mean of the improved population for the trait pods production per plant in snap beans F₂ progeny evaluated at Bom Jesus do Itabapoana, Rio de Janeiro State, Brazil. Campos dos Goytacazes, UENF, 2015.

Once again, progeny 14, from the cross of the commercial variety Feltrin x UENF 15-23-4, stood out reaching the best productive performance for PVP and, consequently, the greatest predicted genetic gain of 65.664 g/plant, obtaining a new predicted average of 210.478 g/plant. Francelino et al. (2011) carried out selections among promising snap bean lines aiming to release improved material for producers from the North and Northwest regions of the State and showed line UENF 15-23-4 among the most productive lines both for grain production and green pod production in the region.

The lowest PVP were obtained by genotypes 1, 7, 49 and 54, showing predicted average gain of only 1.574 g/plant. Among these genotypes, two progenies and two parental lines are presented, UENF 7-3-1 and UENF 15-23-4. Although UENF 15-23-4 is one of the parents from which the line with the greatest PVP derived, the performance of the line itself was not satisfactory under the conditions of this study.

For an efficient selection of superior genotypes, Chiorato et al. (2008) state that this selection should be based both on variance components and on average components and for a considerable genetic gain, the genotypes with the highest averages and the greatest genetic variability should be selected. Based on criteria such as the greatest importance to good productive performance of green pods per plant, as well the greatest genetic variability for PVP, the authors concluded that the use of priority ranking and prediction of values for this trait becomes more feasible. This methodology aims to potentiate genetic gain by selecting the best plants within these selected progenies.

Selection within progenies

Evaluating the traits in plants, the authors verified estimates for each plant or individual. Then, not only the selection among progenies was carried out but also the increase of gains in the population for the next generation, through the selection of ten best plants of each selected progeny, could be obtained.

Analysis of genetic parameters, showed in Table 2, allowed to verify that although in smaller proportions in relation to environmental variance, the authors noticed genetic variability in the population for both traits, with significant values which can be used through selection.

The selection of the most productive plant was carried out based on predicted genetic values of each individual. According to Resende (2007b), REML/BLUP methodology provides ordering potential genotypes for selection exploiting all the genotypic variation among and within progenies, considering each variable analyzed separately, though. Due to the different ordering of progenies by BLUP for selection among progenies for evaluated traits, the authors noticed that priorizing the most relevant trait for the snap bean crop aiming subsequent selection within progenies (among progenies).

At this stage, 10 individuals of each progeny were selected, totalizing 100 plants in order to generate F₃ generation, allowing selection intensity about 7.9%.

The average of genotypic values of each progeny selected after selection of the best plants, general advances obtained through selection process carried out in this study, including genetic gains in relation to average of the original population from the selection among and within progenies and their performance in relation to the commercial controls are presented in Table 5.

Table 5
Average predicted genetic value (µ+g) of the selected plants within progenies for the grain yield per plant (PGP) and pods production per plant (PVP) traits. Campos dos Goytacazes, UENF, 2015.

Selecting plants with best productive performance for pods and grains, new predicted averages for progenies were obtained, especially for plants from the cross Feltrin x UENF 15-23-4, which obtained new averages ranking from 242.643 g to 219.729 g for PVP, production value above all commercial controls used in the experiment.

Although the progenies were highly influenced by the environment, which resulted in low broad-sense heritability values estimated for the two traits, the selection of 100 plants (7.9% of selection intensity), considering the highest predicted genetic values, both for pod and seed production per plant, provided simultaneous predicted additive genetic gain of 36.05% for PGP and 33.5% for PVP in relation to the average of original population.

From this percentage, 29.48% of the gains for PGP resulted from selection among progenies and 6.57% due to individual selection within progenies. For PVP, selection among progenies provided 27.38% of gains and individual selection within progenies resulted in 6.12%, showing that most of variability present in population is among the different progenies evaluated.

Obtaining superior progenies and plants, from these crosses both for pod and grain production, was possible in low heritability conditions of traits, which makes REML/BLUP methodology an effective tool to obtain genetic gains in snap bean breeding program of UENF.

ACKNOWLEDGEMENT

The authors thank to Instituto Federal Fluminense for the concession of the experimental area, to UENF for the research opportunity and to CAPES for the Master scholarship granted to the first author.

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Publication Dates

• Publication in this collection
  Jan-Mar 2017

History

• Received
  21 Aug 2015
• Accepted
  11 May 2016