Strategies for increasing genetic variability, methods and selection gains in white oatABSTRACT:
This study evaluated the capacity to increase genetic variability through hybridization and induction of mutation by chemical and physical methods, as well as to establish which conduction method is most effective to maximize the gains of progeny from half sib and full sib of white oats. 999 F4 generation white oat lines from different genetic origins were tested, using the Single Seed Descent (SSD) and Pedigree methods. We evaluated the pathogens causing leaf rust (Puccinia coronata f. sp. avenae), stem rust (Puccinia graminis f. sp. avenae) and leaf spot (Drechslera avenae (Eidam) Scharif). The continuous quantitative variables were measured: days to emergence, days to flowering, days to maturation and tillering. The genotypes were obtained from conventional hybridization and the mutation induction process. A Bayesian inference model based on the Monte Carlo algorithm with Markov chains was used to determine the degree of precision and genetic effects. From the estimated value the response to selection was estimated using different selection pressures and intensities. Mutation induction proved more effective than artificial hybridization in enhancing genetic variability related to precocity, stem rust tolerance, and tillering. The Single Seed Descent method for white oat lines derived from gamma radiation mutations effectively improves precocity, while the Pedigree method aids in selecting for stem rust resistance in sodium azide mutation induced progenies. Although, half-sib progeny selection demands higher intensities for notable gains across all traits, the full-sib technique enhances the likelihood of producing superior white oat progenies with reduced selection pressures.
Key words:
mutation induction; breeding method; Bayesian inference; Avena sativa L
RESUMO:
O objetivo do estudo foi avaliar a capacidade de aumentar a variabilidade genética por meio da hibridização e da indução de mutação por métodos químicos e físicos, bem como estabelecer qual método de condução é mais eficaz para maximizar os ganhos de progênies de meios-irmãos e irmãos-completos de aveia branca. Para realizar tal objetivo, foram testadas 999 linhagens de aveia branca de geração F4 de diferentes origens genéticas, utilizando os métodos Single Seed Descent (SSD) e Pedigree. Além disso, também foram avaliados os patógenos causadores da ferrugem foliar (Puccinia coronata f. sp. avenae), ferrugem do caule (Puccinia graminis f. sp. avenae) e mancha foliar (Drechslera avenae (Eidam) Scharif). As variáveis quantitativas contínuas foram medidas: dias para emergência, dias para floração, dias para maturação e perfilhamento. Os genótipos foram obtidos a partir da hibridação convencional e do processo de indução de mutação. Um modelo de inferência Bayesiana baseado no algoritmo de Monte Carlo com cadeias de Markov foi usado para determinar o grau de precisão e os efeitos genéticos. A partir do valor estimado a resposta à seleção foi estimada utilizando diferentes pressões e intensidades de seleção. Os resultados mostram que a indução de mutação mostrou-se mais eficaz do que a hibridização artificial no aumento da variabilidade genética relacionada à precocidade, tolerância à ferrugem do caule e perfilhamento. Ademias, o método Single Seed Descent para linhas de aveia branca derivadas de mutações de radiação gama melhora efetivamente a precocidade, enquanto o método Pedigree ajuda na seleção de resistência à ferrugem do caule em progênies induzidas por mutação azida de sódio. Portanto, embora a seleção de progênies de meios-irmãos exija intensidades mais altas para ganhos notáveis em todas as características, a técnica de irmãos-completos aumenta a probabilidade de produzir progênies de aveia branca superiores com pressões de seleção reduzidas.
Palavras-chave:
indução de mutação; método de melhoramento; inferência Bayesiana; Avena sativa L
INTRODUCTION
Cereals constitute one of the world’s main staple foods, contributing more than 50% of total daily calorie intake (DAVID & GUIVANT, 2020). In this context, white oats (Avena sativa L., 2n = 6x = 42, AACCDD) have been confirmed as one of the main alternatives for this purpose, due to their nutraceutical potential, with emphasis on the β-glucan fiber in its composition, responsible for reduction of cardiovascular diseases (ERIVE et al., 2020). The crop has been grown in marginal production areas with low fertility not suitable for wheat or barley, due to its apparent adaptation to various soil types and the ability to perform in these environments (GORASH et al., 2017). The average yield in Rio Grande do Sul during the 2025 growing season was about 2,500 kg ha-1 (CONAB, 2025). Due to the great demand for grains, the challenge of genetic improvement of white oats is to obtain genotypes with broad stability, high productivity and grain quality, with tolerance to the main diseases that affect the crop (PRADEBON et al., 2023). This goal can be achieved by using genotypes with an early cycle, which, if positioned correctly, coincide with the period of lowest pathogen pressure (BASSO et al., 2022). However, this practice is dependent on contrasting environmental conditions in each year of cultivation, which justifies the selection of genotypes with genetic tolerance to diseases (PARK et al., 2022).
Leaf rust caused by the obligate biotrophic fungus Puccinia coronata f. sp. avenae (NAZARENO et al., 2018), is characterized as the main disease of white oats, along with stem rust (Puccinia graminis f. sp. avenae) and leaf spot (Drechslera avenae (Eidam) Sharif), these diseases occur in the main oat-producing regions of the world and can cause yield losses of up to 50% in grain productivity (PARK et al., 2022). The economically viable way to protect plants is genetic tolerance (GORASH et al., 2017), which can reduce dependence on fungicide applications and promote improvements in the quality of the final product. This requires expanding genetic variability, obtaining tolerance alleles, combined with assertive selection strategies. Developing tolerant genotypes is an arduous task, as many pathogens have a high number of genetically diverse races, with contrasting degrees of virulence (SINGH et al., 2021). Given this, artificial selection enables changes in gene frequencies that, when based on correct management methods, provide genetic gains over generations of improvement (FALCONER, 1964). When identifying genes and alleles of interest in the breeding population, selections are directed to germplasm that expresses genetic variability for the trait of interest.
The expansion of genetic variability can be obtained via crossing or mutations arising from chemical or physical methods when these mutations are heritable, they can be sources of rare alleles in the gene pool of the species of interest and subsequently selected through plants with a favorable phenotype (PATHAKAKULA et al., 2024). In this context, two of the most used ways to induce mutation consist of the use of ionizing electromagnetic radiation or chemical mutagens (MUTANDA et al., 2025). In view of this, this study evaluated the capacity to increase genetic variability through hybridization and induction of mutation by chemical and physical methods, as well as to establish which conduction method is most effective to maximize the gains of progeny from half-sibs and full-sibs of white oats.
MATERIALS AND METHODS
Field experiment and plant material
The study was carried out in the Área de Difusão Tecnológica, of the Universidade Regional do Noroeste do Estado do Rio Grande do Sul (UNIJUÍ), located in the municipality of Ijuí (28º23’36” S, 53º56’37” W, altitude of 305 meters). The soil in the experimental area is classified as a Typical Dystroferric Red Oxisol (SANTOS et al., 2018). According to Köeppen’s climate characterization, the region’s climate is type Cfa (humid subtropical) (ALVARES et al., 2014).
999 F4 generation white oat lines from different genetic origins were tested, using the Single Seed Descent (SSD) and Pedigree methods. The genotypes were obtained from conventional hybridization and the mutation induction process (Figure 1). The mutation was induced using electromagnetic radiation - gamma radiation (GM) and chemical mutation using sodium azide - NaN3 (SA) and ethyl methanesulfonate (EMS), according to the methodology proposed by NASCIMENTO JÚNIOR et al. (1994). Of the total lines, 537 were conducted using the SSD method, with 3.17, 11.55 and 85.29% of the genotypes arising from EMS mutation, GR mutation and hybridization, respectively. 462 lines were also conducted using the Pedigree method, with lines measuring two meters long and spaced 0.50 m apart, with 56.28%, 22.29% and 21.43% of the genotypes arising from mutation by EMS, SA and hybridization, respectively. Sowing was carried out manually in the second half of February 2023 to accelerate generation advance in the breeding program and to impose greater disease pressure. The panicles were harvested in the moment that plants reached physiological maturity.
Diagram of the process of obtaining genotypes by the white oat genetic improvement program (PMG - UNIJUÍ) for different methods and genetic origins. GM: gamma radiation mutation; EMS: ethyl methanesulfonate mutation; SA: sodium azide mutation; SSD: Single Seed Descent.
Field measurements
Disease incidence was visually assessed using a 0-10 scale, where 0 indicates no pathogen presence and 10 represents maximum severity. In this way, we evaluated the pathogens causing leaf rust - LR (Puccinia coronata f. sp. avenae), stem rust - SR (Puccinia graminis f. sp. avenae) and leaf spot - LS (Drechslera avenae (Eidam) Scharif). No fungicide applications were carried out. The continuous quantitative variables were measured: days to emergence (DE, days), days to flowering (DF, days), days to maturation (DM, days) and tillering (TIL, units).
Data analysis
The data matrix obtained was subjected to the assumptions of the statistical model as described by AZEVEDO et al. (2022). With the assumptions met, a Bayesian inference model based on the Monte Carlo algorithm with Markov chains (MCMC) was used to determine the degree of precision and genetic effects, as follows:
y = Xβ + Z1δ1+ Z2δ2+ e,
Where y is considered the vector of phenotypic values; X and β are, respectively, the incidence matrix and the corresponding vector of systematic effects (general average); Z 1 and Z 2 are the random effects incidence matrices, δ 1 is the block effects vector; δ 2 is the vector of genetic values and; e is the residual vector. 10,000 iterations were used a priori, with the first 1,000 iterations discarded as burn-in. The significance of the convergence of Markov chains was verified by Geweke’s diagnosis (GEWEKE, 1992). The model was fitted with the MCMCglmm function from the MCMCglmm package version 2.35 (HADFIELD, 2010). From the estimated values, we proceeded to determine broad-sense heritability, being: , where σG consists of the genotypic variance and σP consists of the phenotypic variance. The narrow-sense heritability was calculated by: , where consists of the additive genetic variance, consists of the genetic variance of dominance and consists of the genetic variance of the environment. The response to selection was calculated by , where R consists of the response to selection (selection gain); consists of narrow-sense heritability; i consists of the selection intensity (which varies according to the selection pressure) and; σP consists of the phenotypic variance. The corresponding selection pressures and intensities (i) are, respectively: 1%;2.665, 5%;2.063, 10%;1.775, 20%;1.400, 30%;1.160, 50%;0.800. All analyzes were performed using the R software (R CORE TEAM, 2023).
RESULTS AND DISCUSSION
Convergence effects were significant for all methods and genetic origins using the Geweke test, which indicates that inferences can be made (Table 1, Table 2, and Table 3). This shows that it is possible to estimate the heritability parameters for the measured traits. Broad-sense heritability (H2) reflects the portion of the phenotype attributed to the genetic effect, where heritabilities can be classified considering magnitudes between 0.01 and 0.15 as low, 0.16 to 0.50 as medium and high when greater than 0.51 (RESENDE, 1995; PADULETO et al., 2017). Of this total genetic effect, the alleles that can be transferred to the next generation (portion with additivity) are of greater importance for white oat, as it is an autogamous species. In this context, it is important to consider whether the selection will be carried out on half-sibs or full-sibs, as the additive genetic potential changes each time. Narrow-sense heritability tends to be higher for full-sibs (h2FS) where the parents are known, while for half-sibs (h2HS) there is a lower accumulation of genes with an additive effect (HAJNAJARI et al., 2012). For estimation purposes, in this study the values of H2, h2FS and h2HS formed a proportional vector with a ratio of ½, in the sense H2 > h2FS > h2HS, which allows us to verify which methods and genetic origins provided the greatest expansion of genetic variability and of additive genetic potential based solely on the H2 value.
The broad-sense heritability estimates (Table 4) show that the use of Pedigree/EMS provided the highest heritability value in the broad sense (H2 = 0.683) for days to emergence. There was little difference between genetic origins for the SSD method, where hybridization, in this case, was slightly superior to mutagenic processes. For days to flowering, the mutation with EMS (ethyl methanesulfonate) promoted the greatest increase in variability when considering both methods used, while the lowest heritability values were attributed to hybridization and mutation by gamma radiation (H2 = 0.259 and H2 = 0.354, respectively), which implies less variability in the phenotype. There is little variation in the increase in variability when using the Pedigree method.
These results influenced the estimates for days to maturation, with a broad-sense heritability of H2 = 0.838. In this context, the greatest variation in the values estimated from the EMS mutation was seen, while the mutation with SA (sodium azide) surpassed the other genetic bases (H2 = 0.642), regardless of the method used. From a practical breeding perspective, the high heritability for days to maturity indicates efficient phenotypic selection and predictable genetic gain. EMS-induced mutations expand usable genetic variability for cycle adjustment, whereas sodium azide mutations, with consistent heritability, provide greater selection stability (SUNDARRAJAN et al., 2025).
Broad-sense heritability estimates for pathogen tolerance were H2 = 0.358, H2 = 0.396 and H2 = 0.503 for stem rust, leaf rust and leaf spots (Table 2). These reduced values for tolerance to stem rust are mainly due to the little contribution of the genotypes resulting from the EMS mutation (H2 = 0.287). The highest broad-sense heritability values were observed for mutations caused by sodium azide and gamma radiation (H2 = 0.615 and H2 = 0.564, respectively). There was little disparity between the estimates for the other pathogens, with emphasis on SSD/Hybridization in tolerance to leaf rust. The increase in genetic variability for tillering was less pronounced when using genotypes obtained from hybridization, being greater for mutation with EMS. However, it is necessary to use the SSD method to satisfy this statement, with H2 = 0.484, while in the Pedigree method the value of H2 = 0.232 was measured. Furthermore, little difference was estimated between the increase in variability for genotypes arising from hybridization and sodium azide when using the Pedigree method (H2 = 0.441 and H2 = 0.497, respectively).
Based on the potential for expanding genetic variability for the characters of interest, it becomes possible to make predictions of genetic gains when comparing different pressures and selection intensities applied for each method and genetic origin. Selection gains are directly proportional to the estimated heritable portion, with higher heritability values in the narrow sense allowing the selection intensity to be reduced, which increases the probability of selecting the sought-after phenotype. SILVA et al. (2021) found a higher proportion of loss of potentially promising genotypes when selection is carried out randomly, without considering specific populations undergoing improvement.
The assessment of selection gain for days to emergence, flowering and maturity follows the premise of obtaining early genotypes, to enable sowing so that the reproductive period of white oats coincides with lower pathogen pressure. In this way, there was a potential for a reduction of more than seven days for emergence (Figure 2) with pressures of up to 10% for SSD/General and 2% for Pedigree/EMS for selection in full-sibs. For the other estimated scenarios, a reduction of up to six cycle days was possible, with the smallest selection gains attributed to Pedigree/Hybridization and General/EMS, less than five days. Selection based on half-sibs allowed a maximum reduction of approximately six days when using the most rigorous selection (1%) in genotypes conducted under the SSD method.
Estimation of selection gains for different methods, genetic origins and selection pressures for days to emergence. SGFS: selection gain for full siblings; SGHS: selection gain for half siblings; GM: gamma radiation mutation; EMS: ethyl methanesulfonate mutation; SA: sodium azide mutation.
The genetic methods/origins that promoted the precocity gains previously verified did not result in cycle reduction when considering the number of days to flowering (Figure 3). This can be verified by the fact that, regardless of the method and genetic origin, gains of 10.38 to 15.92 days were evaluated for pressures of 10 to 1% with the use of Pedigree/General and SSD/Hybridization. These selection strategies were specific for the greatest gains in reducing the number of days to flowering in selection on full-sibs. Estimates showed little variation if the selection is carried out on half siblings, with gains varying between six and eight days of cycle reduction.
Estimation of selection gains for different methods, genetic origins and selection pressures for days to flowering. SGFS: selection gain for full siblings; SGHS: selection gain for half siblings. GM: gamma radiation mutation; EMS: ethyl methanesulfonate mutation; SA: sodium azide mutation.
The selection gains for days to maturity (Figure 4) showed that it is possible to reduce up to 53 cycle days with the selection methods applied, reaching around 34 days with pressures of 10% considering all the strategies used in the study. In this context, the genetic origin obtained by EMS stands out, where gains of 34.57, 40.86, 42.43, 44.59, 47.93 and 53.03 days were estimated with pressures of 10, 5, 4, 3, 2 and 1%, respectively. The recombinant genetic origin promoted gains that reflect the lower capacity to reduce the number of days to maturity. Therefore, it is necessary to consider the potential for increasing variability presented by mutation induction methods, with greater effectiveness compared to the conventional hybridization method. From the selection based on half siblings, gains of 9.84 to 26.62 days were observed for General/Hybridization and General/EMS when using the highest selection intensity.
Estimation of selection gains for different methods, genetic origins and selection pressures for days to maturity. SGFS: selection gain for full siblings; SGHS: selection gain for half siblings. GM: gamma radiation mutation; EMS: ethyl methanesulfonate mutation; SA: sodium azide mutation.
The reduced values of selection gains for tolerance to the evaluated pathogens may be attributed to the main reason that methods of increasing genetic variability may not have been effective in expanding the heritable portion of tolerance expression. BERLEZI et al. (2023) estimated a genotypic effect of less than 10% when evaluating the incidence of pathogens in 26 white oat cultivars, which justifies the difficulty in selecting tolerant genotypes. Even so, among all pathogens, mutation induction was most effective for stem rust tolerance (Figure 5). The selection gains were superior compared to the use of hybridization in the SSD method, where values ranged from 2.60 to 3.40 for pressures of 5 to 1%. The genotypes arising from a mutation with sodium azide (SA) stood out from the others, with gains between 2.77 and 4.25 for selection pressures starting from 10%. This showed that this strategy can be a favorable source for selecting genotypes with stem rust tolerance genes.
Estimation of selection gains for different methods, genetic origins and selection pressures for stem rust (Puccinia graminis f. sp. avenae). SGFS: selection gain for full siblings; SGHS: selection gain for half siblings. GM: gamma radiation mutation; EMS: ethyl methanesulfonate mutation; SA: sodium azide mutation.
Sodium azide (SA) induces mainly cytosine-to-thymine (C/T) mutations in plant breeding, creating novel variants not found in natural populations (SUNDARRAJAN et al., 2025). Large-scale barley studies show approximately 2,100 single nucleotide variants (SNVs) per line but 38% yield loss, requiring selection to eliminate deleterious effects while capturing beneficial alleles for traits like disease tolerance (LIU et al., 2025).
Insignificant selection gains were inferred for all strategies applied to express tolerance to leaf rust, which shows that few genotypes presented the characteristic (Figure 6). Even with the highest selection intensity, where only 1% of plants are selected, the gain is practically non-existent, even when selecting full-sibs. The mutation was not viable in this case, as the costs from an economic and genetic point of view did not promote the expected result. Mutagenic agents produce major changes in DNA, which can be deleterious, with total loss of genes, inversions, translocations and small intragenic mutations (CHOUGALE et al., 2025), which can be counterproductive to genetic gains. A similar scenario was found for selection gains in tolerance to leaf spots (Figure 7), with values lower than those verified for leaf rust, which states that the strategies used were ineffective for genetic gains in tolerance to pathogens that cause foliar diseases.
Estimation of selection gains for different methods, genetic origins and selection pressures for leaf rust (Puccinia coronata f. sp. avenae). SGFS: selection gain for full siblings; SGHS: selection gain for half siblings. GM: gamma radiation mutation; EMS: ethyl methanesulfonate mutation; SA: sodium azide mutation.
Estimation of selection gains for different methods, genetic origins and selection pressures for leaf spot (Drechslera avenae (Eidam) Scharif). SGFS: selection gain for full siblings; SGHS: selection gain for half siblings. GM: gamma radiation mutation; EMS: ethyl methanesulfonate mutation; SA: sodium azide mutation.
Defining the optimal number of tillers depends on the purpose for which the oats will be used. Genotypes with a reduced number of tillers showed greater grain fitness, while for forage fitness the maximum possible increase in tillering expression genes is sought. For genotypes intended for grain production, excess tillers can lead to cultivation problems, such as increased propensity for lodging and increased variability in panicle maturation at harvest time. The estimates show the potential for selecting genotypes with a lower tillering rate for SSD/Hybridization and Pedigree/EMS (Figure 8), with values between 1.58 and 2.42 and between 1.71 and 2.63 for pressures between 10 and 1%, respectively, with selection practiced in full siblings. Conversely, both the mutation with sodium azide for the Pedigree method and the mutation with EMS and GM for the SSD method promoted significant gains in increasing the number of tillers. Using these selection strategies, gains are expected to be greater than those previously presented with pressures of up to 10%, in addition to being greater than the estimated values for Pedigree/Hybridization.
Estimation of selection gains for different methods, genetic origins and selection pressures for tillering. SGFS: selection gain for full siblings; SGHS: selection gain for half siblings. GM: gamma radiation mutation; EMS: ethyl methanesulfonate mutation; SA: sodium azide mutation.
This study provided important information regarding the effectiveness of artificial mutagenic processes, which can serve as a strategy for expanding genetic variability in breeding programs to achieve gains in the selection of white oat genotypes from phenotypic selection. Future studies using genomic tools may clarify the genetic mechanisms underlying the high heritability and mutation-induced variability observed in this study, supporting more precise selection and sustained genetic gains.
CONCLUSION
Mutation induction proved more efficient than artificial hybridization in expanding genetic variability for precocity, stem rust tolerance, and tillering, regardless of the training method. Lines derived from gamma radiation and advanced by Single Seed Descent showed effective gains in precocity, while the Pedigree method enhanced stem rust selection in sodium azide-derived progenies. Selection based on half-sib progenies required higher selection intensities to achieve significant gains, whereas the full-sib approach increased the likelihood of obtaining superior white oat progenies under lower selection pressures.
ACKNOWLEDGMENTS
The authors thank the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES Foundation), for financing and providing resources to carry out this study - Financial Code 0001.
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ASSOCIATE EDITOR:
Alessandro Dal’Col Lúcio (0000-0003-0761-4200)
Research data is only available upon request.
