Published on International Journal of Food & Nutrition
Publication Date: August, 2019
El-Sherbeny, G. A. R., Khaled, G. A. A. & Haitham, M. A. Elsayed
Dept. of Genetics, Fac. Agric., Sohag University
Sohag 82786, Egypt
Twenty eight half diallel crosses among eight diverse parental genotypes of bread wheat were evaluated. The main objective of this study is to identify the gene action of inheritance for the yield component traits. The results demonstrated that, the mean squares of all studied traits were highly significant under normal, drought stress and combined data. The majority of cross combinations had positive significant heterosis over better parent under each environment and combined data, reflecting the prevalence of heterotic effects and non-additive effects. Non-additive gene action (σ2D) was found to play the major role in the inheritance of studied traits under each environment and their combined data. The parents P1, P3, P6, P7 and P8 were the best general combiner for all the studied traits under the two environments and combined data. The promising crosses which showed desirable specific combining ability (SCA) effects gave also high estimate of useful heterosis.
Keywords: Wheat, drought stress, yield component traits, heterosis, half diallel analysis.
Wheat (Triticum aestivum L.) is a very important food crop in Egypt as a source of human food. The rate of growth in the human population need high growth in wheat yielding to satisfy hunger globally. The interaction of genotype × environment G x E gave a significant role in the expression of different genotypes in different environments. Therefore, increase genetic gains in yield component traits by studying interaction (G × E) must be done for providing information about the effect of different environments on cultivar performance toward yield component traits (Bose et al., 2014.
Drought stress is one of the most important abiotic environmental factors during grain filling and may influence both the quantity and quality of the yield causes great loss in yield production (Anwar et al., 2011; Hamidou et al., 2013; Tardieu et al., 2014). Yield losses caused by drought stress controlled by polygenes (Daryanto et al., 2016). So, the improvement of productivity under drought stress required novel genetic resources as a superior genotypes based on drought adaptation (Passioura, 2012; Mohammadi and Amri, 2013; Mwadzingeni et al., 2016).
Grain yield is a complex polygenic trait obtained through many quantitative traits participated with small effects. Therefore, it must be improve direct and indirect traits (No. of spikes/plant, spike length, No. of grains/spike, weight of grains/spike, 1000-grain weight and grain yield/plant) by exploring maximum genetic potential of wheat by crossing good general combining genotypes for grain yield and its components against drought stress conditions (Ijaz and Kashif 2013; Nataša et al., 2017). Diallel cross fashion is a good method for this way leading to identification of hybrid combinations that have the potentiality of producing maximum improvement. Combining ability analysis of Griffing, (1956) is most widely used as a biometrical tool for identifying parental genotypes in terms of their ability to combine in hybrid combinations and nature of gene action in inheritance toward yield component traits (Nazir et al., 2014; Yao et al., 2014; Kumar et al., 2015; Shehzad et al., 2015). The predominance of non-additive gene effects in the inheritance of grain yield/plant and its components was detected by Ahmad et al., (2011). On the other hand, the predominance of additive effects of the majority of genetic variances for yield component traits was reported by Farook et al., (2011); Shehzad et al., (2015). Thus, for beginning successive breeding program, it must be required more information regarding to general combining ability (GCA) and specific combining ability (SCA) of wheat genotypes toward drought tolerance. Consequently, yield component traits remain the main criteria for improved adaptation to water environmental stresses in many breeding programs based on their yield potential and stable performance over a range of environmental conditions. These information help in classifying the parental genotypes in terms of their cross performance to obtain more knowledge about the nature of gene action for inherited traits (Ahmed et al., 2011; Kohan and Heidari, 2014; Yao et al., 2014).
The objectives of this investigation were to assess the genotypic variation for G x E interaction. In addition, to estimate the magnitude of heterosis over better parent and nature of gene action in inheritance of yield component traits.
2. MATERIALS AND METHODS
The genetic materials in this study involved eight different bread wheat parental genotypes: Misr-1 (P1), Sids-12 (P2), Sahel-1 (P3), Katela (P4), Sakha-94 (P5), Deibera (P6), Weiber (P7) and Canada-462 (P8), which presented wide range variability in their yield component traits. The present study was carried out at the El-Kawther Experimental Research Farm of Faculty of Agriculture, Sohag University, Sohag, Egypt through the two successive wheat seasons 2016/2017 and 2017/2018.
In the winter season 2016/2017, eight parental genotypes were planted and crossed according to half diallel mating design to produce 28 F1 hybrids. In the winter season 2017/2018, the seeds of eight parents and their 28 F1 hybrids were sown under normal and drought environmental conditions in a randomized complete block design (RCBD) with three replications. Each plot consisted of 3 rows with long 3 m. and 30 cm. wide. Plants were spaced by 10 cm. within row. The soil at the experimental site was sandy to loamy sand. All recommended cultural practise were applied under normal conditions (every 10 days) and drought stress conditions (every 20 days).
Data were recorded on ten plants/genotype chosen randomly in each plot for No. of spikes/plant, spike length, No. of grains/spike, weight of grains/spike, 1000-grain weight and grain yield/plant traits. In each environment, data were subjected to the analysis of variance to test the significance of the differences among the ten genotypes included eight parents and their 28 F1 hybrids according to Cochran and Cox (1957). Combined data over the two environments were also subjected to the combined analysis of variance to test the interaction of genotypes with environments.
Estimates of heterosis over better parent were determined for each cross as follow:
H (B.P) % = x 100
Where; (F1) is the mean of F1 hybrid and (BP) better parent value.
The heterotic values were tested for significance to establish the differences of the F1 hybrid means from their respective better parent using the least significant difference value (L.S.D.) at 5% and 1% levels of significance, according to (Steel and Torrie, 1985).
General combining ability GCA and specific combining ability SCA variances were partitioned from total genotypic variance in each environment according to (Griffing, 1956) method 2, model 1. In addition, the combined analysis over the two environments was calculated to partition the men squares of genotypes and the interaction of genotypes with environments into sources of variations due to GCA, SCA, and their interaction with the environments (GCA x E and SCA x E). With the assumption there is no epistasis the genetic components could be obtained from the estimates of GCA variance (σ2g), SCA variance (σ2s), GCA x E variance (σ2g x E) and SCA x E variance (σ2s x E) according to (Matzinger and Kempthorne 1956; Singh, 1979). Estimates of heritability in both broad (h2b.s. %) and narrow sense (h2n.s. %) were also calculated.
4. RESULTS AND DISCUSSION
4.1 Estimates of genotypic variation:
All genotypes of wheat (eight parental varieties and their 28 F1 hybrids) were evaluated to estimate the magnitude of genotypic variations genotype × environment interaction (G × E) to study the expression of different genotypes in different environments for No. of spikes/plant, spike length, No. of grains/spike, weight of grains/spike, 1000-grain weight and grain yield/plant traits (Table 1). The results showed that the mean squares of environment, genotypes and genotypes x Environment (G x E) were highly significant for all studied traits under each environment and their combined data. These results, indicating a great variance among parental genotypes, suggesting differential response of the genotypes from environment to another. Moreover, the results for mean performance (Table 2) showed that, the parental varieties P2, P3, P5 and P6 were the best performance for all the studied traits under normal, drought stress and combined data, respectively. Similar results was obtained by Singh et al. (2014); Samir and Ismail (2015); Saied et al. (2017); Jyoti Yadav, (2017); Emad et al. (2018).
4.2 Estimates the magnitude of heterosis over better parent:
The results over better parent showed in Table 3. Concerning to No. of spikes/plant, the best significant positive heterotic values were (1.97% and 11.18%) and (13.16% and 13.43%) for the hybrids (P2xP4) and (P4xP5) under normal and combined data, respectively. In addition, the best heterotic values were 35.63% (P5xP7) and 41.43% (P7xP8) under drought stress.
Regarding to spike length the results indicated that, the best crosses were (P1xP8), (P5xP8) and (P6xP8) with significant heterotic values of (18.81%, 20.52% and 19.61), (13.05%, 11.51% and 12.30%) and (11.87%, 12.39% and 12.30%) under normal, drought stress and combined data, respectively.
The heterotic values for No. of grains/spike exhibited that, the cross P4xP8 had the desirable significant positive heterotic values of 16.89%, 19.94% and 22.29% under normal, drought stress and combined data, respectively. In addition, the crosses (P1xP8) and (P2xP7) showed the best heterotic hybrids with values of 13.37% and 11.10% under normal condition. Moreover, the crosses (P1xP6) and (P4xP6) recorded the highest heterotic crosses with values (20.05% and 12.37%) and (26.71% and 14.89%) under drought stress and combined data, respectively.
As for the heterosis for weight of grains/spike, the best cross was P4xP8 with significant heterotic values 21.11%, 108.74% and 50.99% under normal, drought stress and combined data, respectively. Additionally, the cross P7xP8 presented the highest positive heterotic values 91.59% and 61.98% under normal and combined data, respectively. In the same trend, the cross P1xP2 recorded the best desirable hybrid under drought stress with heterotic value of 45.63%.
Concerning to 1000-grain weight, the best desirable heterotic values toward over better parent was 5.69%, 8.41% and 11.31% for the cross combination P6xP8 under normal, drought stress and combined data, respectively. While, the cross combination P6xP7 showed the highest heterotic values of 4.74% and 10.28% under normal and combined data, respectively. In addition, the best significant heterotic values were 10.01% and 11.89% for the cross combinations P1xP4 and P1xP6 under drought stress, respectively. Although, the cross P7xP8 and P1xP3 showed the best heterotic value of 7.29% and 4.75% under normal condition and combined data, respectively.
Regarding grain yield/plant the results exhibited that, the best significant heterotic F1 hybrid was P7xP8 with heterotic values of 12.45%, 52.61% and 24.52% under normal, drought stress and combined data, respectively. Moreover, the hybrids P1xP3 and P1xP8 exhibited the highest heterotic under normal condition with values of 0.65% and 18.48, respectively. Moreover, the hybrids P1xP5 and P4xP8 recorded the best desirable positive significant heterotic values (32.91% and 6.08%) and (29.55% and 5.29%) under drought stress and combined data, respectively. These finding reflect high degree of genetic diversity among the parental genotypes and support the important role of non-additive gene action controlling these studied traits under each environment and their combined data. Similar results had been obtained by Gomaa, et al. (2014); Kumar and Kherkhi (2014); Singh et al. (2014); Samir and Ismail (2015); Gul et al. (2015); Saied et al. (2017); Jyoti Yadav, (2017).
4.3 Estimates combining ability effects:
Estimates of general combining ability effect (gi) and specific combining ability effects (Sij) under two environment and their combined data for all studied traits are presented in Tables 4 and 5, respectively. Regarding to (gi) the results showed that, the parents P1, P3, P6, P7 and P8 were found to be excellent combiners for the majority of studied traits under the two environments and their combined data. As for (Sij) it could be observed that, the promising hybrids were resulted from the crossing (good x good), (good x poor) and (poor x poor) general combiners. Therefore, it is not necessary that parents having high estimates of GCA effects would also give high estimates of SCA effects in their respective crosses. In general, the promising crosses which showed desirable SCA effects gave also high estimate of useful heterosis as previously mentioned. These finding indicate that non-additive gene action played an important role in the inheritance of these traits. The same results were obtained by Gomaa, et al. (2014); Farooq et al. (2015); Kumar et al. (2015); Jyoti Yadav, (2017); Saied et al. (2017); Ljubicic et al. (2017); Nataša et al. (2017).
4.4 Combining ability analysis of variance:
Analysis of variance and mean squares of GCA and SCA and their interactions with environments for all studied traits are given in Table 6. The results showed that the mean squares of GCA were highly significant for No. of spikes/plant, No. of grains/spike, 1000-grain weight and grain yield/plant under normal condition. Under drought stress, the means squares of GCA were highly significant for No. of spikes/plant, spike length, weight of grains/spikes and 1000-grain weight. However, the mean square of GCA was only significant for spike length under combined data.
The mean squares of SCA were found to be highly significant for No. of spikes/plant, weight of grains/spike and grain yield/plant under each environment and their combined data. However, spike length and No. of grains/spike revealed highly significant SCA mean squares under drought stress and normal conditions, respectively. While, 1000-grain weight trait demonstrated highly significant of SCA mean square under normal and drought stress.
The ratios of GCA/SCA were more than unity for all yield component traits except grain yield/plant (under the each environment and their combined data) and No. of grains/spike (under drought stress).
Concerning the interaction of GCA x E, the mean square of No. of spikes/plant was only highly significant. However, SCA x E interaction mean squares were highly significant for all yield component traits. The ratio of GCA x E/SCA x E was more than one for No. of spikes/plant and weight of grains/spike. These results were in agreement with the results obtained by Gomaa, et al. (2014); Kumar and Kerkhi (2015); Kumar et al. (2015); Mari et al. (2015); Samir and Ismail. (2015); Jyoti Yadav (2017); Saied et al. (2017); Ljubicic et al. (2017).
4.5 Estimates of genetic parameters:
The estimates of genetic parameters obtained from the analysis of the half diallel mating design showed in Table 7. The results demonstrated that, the magnitudes of σ2A were lower than σ2D for all studied yield components traits under each environment and combined data except 1000-grain weight under only combined data. Furthermore, the magnitudes of σ2A x E interaction was less than σ2D x E for all yield component traits. Moreover, the estimates of broad sense heritability larger many times than those of narrow sense heritability for all yield component traits under each environment and their combined data. These results are agree with those obtained by Kohan and Heidari (2014); Irshad et al. (2014); Farooq et al. (2015); Mari et al. (2015); El-Hosary et al. (2015); Kandil et al. (2016); Ljubicic et al. (2017); Saied et al. (2017).
The statistical procedures in this study are declared the importance roll of non-additive effects in the inheritance of the traits. Therefore, the outcome information can help breeders to make better selection of desirable parents to develop an efficient breeding program to obtain new wheat cultivars with high grain yield potential for food and nutritional security.
6. CONFLICT OF INTEREST
The present study was performed in no any conflict of interest
The authors would thanks all participant in this investigation.