Assessment of Heritable Variation and Best Combining Genotypes for Grain Yield and Its Attributes in Bread Wheat

Grain yield in wheat is the resultant of several plant attributes. It is very important to assess heritable variation involved in the inheritance of these attributes in addition to find the best combining genotypes. For this purpose, the present study involving 5 × 5 full diallel analysis was performed. Twenty F1 hybrids along with their parents (9797, 9801, 9802, Chakwal-50 and Chakwal-86) were planted in field using randomized complete block design (RCBD) with three replications in the research area of Department of Plant Breeding and Genetics, University of Agriculture, Faisalabad during 2015-2016. Plant characters like plant height, flag leaf area, spike length, No. of fertile tillers per plant, No. of grains per spike, No. of spikelets per spike, 1000 grain weight and grain yield per plant were studied in this experiment. Mean squares due to general combining ability (GCA) were highly significant for all the traits except for spike length for which GCA effects were significant. Mean squares due to specific combining ability (SCA) and reciprocal combining ability (RCA) were highly significant for all the characters studied. GCA variance was higher than the SCA variance for spike length and No. of grains per spike exhibiting the predominant role of additive genetic variation in the inheritance of these traits. However, for the characters like plant height, flag leaf area, No. of fertile tillers per plant, No. of spikelets per spike, 1000 grain weight and grain yield per plant, the value of SCA variance was higher than the value of GCA variance showing non additive gene action for these How to cite this paper: Parveen, N., Kanwal, A., Amin, E., Shahzadi, F., Aleem, S., Tahir, M., Younas, A., Aslam, R., Aslam, N., Ghafoor, I., Makhdoom, M., Shakir, M.A. and Najeebullah, M. (2018) Assessment of Heritable Variation and Best Combining Genotypes for Grain Yield and Its Attributes in Bread Wheat. American Journal of Plant Sciences, 9, 1688-1698. https://doi.org/10.4236/ajps.2018.98122 Received: June 6, 2018 Accepted: July 21, 2018 Published: July 24, 2018 Copyright © 2018 by authors and Scientific Research Publishing Inc. This work is licensed under the Creative Commons Attribution International License (CC BY 4.0). http://creativecommons.org/licenses/by/4.0/ Open Access


Introduction
Wheat (Triticum aestivum L.) is a cereal crop originated in Middle East. It is a self pollinated crop belonging to Poaceae family. Bread wheat is hexaploid species and has three genomes namely A, B and D [1]. Genetic study of wheat is complex because of its large genome size. Wheat supply 20% of total calories consumed by human daily. The genetic studies of wheat enabled breeders to develop high yielding, good quality and disease resistant varieties. It has vital position in Pakistan agricultural policies and in international trade wheat share is more than other crops combined. Contribution of wheat to GDP is 1.9% and 9.6% to value added in agriculture. During 2016-2017, it was cultivated on 9052 thousand hectares and total production was 25.75 million tonnes and yield was 2752 kg/ha [2]. Grain is a good source of carbohydrates, vitamins and minerals.
Amount of protein in wheat is more than other cereals and thus a main source of vegetable protein worldwide. Consumption of wheat is increasing by 2% worldwide per year, to fill this increasing demand there is a need to develop high yielding and disease resistant varieties. Genetic studies of wheat provided the information useful in developing wheat varieties with high yield and improved quality [3].
Combining ability studies invented by [4] depict the ability of the parents to transmit their useful characters to next generation and assess the performance of genotypes in cross combinations [5]. Diallel analysis gives information about the inheritance and gene action that enables the plant breeder to do selection in earlier or later generations. Mean performance of a line in cross combinations is evaluated by general combining ability (GCA) and GCA is linked to the percentage of additive type of gene action [6]. SCA is the estimation of the performance of progeny derived from a specific cross in relation to what would be expected based on the average performance of the genotypes involved.
Keeping in view of the above situation the research study was carried out with the following objectives; 1) To assess performance of five bread wheat genotypes and their F 1 s to recognize the best performing genotypes, 2) To study the GCA and SCA to discover the best general combiners and hybrid combinations for yield and its related components.

Materials and Methods
The present research to estimate the combining ability effects for wheat yield and its related traits was carried out in the experimental area of Department of

Results and Discussion
The results pertaining to the heritable variation and inheritance pattern of grain yield and its various attributes are given below.

Plant Height
In wheat negative combining ability effects are desirable for plant height. Analysis of variance for combining ability revealed that mean squares for GCA, SCA, and RCA effects were highly significant (Table 1). Although mean squares of N. Parveen et al.

Flag Leaf Area
Combining ability analysis showed that mean squares due to GCA, SCA and RCA effects were highly significant for flag leaf area (Table 1). Mean square values for SCA were greater than GCA mean squares. Similarly variance of SCA

Spike Length
ANOVA for combining ability depicted that the mean squares for general combining ability were significant. While the mean squares due to SCA effects and RCA effects were highly significant (Table 1). Mean squares of GCA (1.76) were higher than mean squares of SCA (1.51) indicating the importance of additive type of gene action for spike length and mean squares of RCA were 1.85. Calculation of variance components also confirmed additive type of gene action for spike length (Table 2). These results are in agreements with the findings of [9] [15]. Estimates of GCA effects are given in Table 3. Results revealed that the good general combiners for spike length were Chakwal-50 and 9797 with GCA effects of 0.32 and 0.31 respectively. Genotypes 9802 (−0.67) and 9801 (−0.07) exhibited the high negative GCA values thus proved poor combiners for spike length. Estimates for the SCA effects (Table 4) (Table 5).

Fertile Tillers per Plant
ANOVA for combining ability depicted the highly significant mean squares due to GCA (13.8), SCA (6.80) and RCA effects (4.82) ( Table 1). Although the mean squares of GCA were higher than the mean squares of SCA for No. of tillers per plant but computation of variance components showed that SCA variance was higher than the variance of GCA denoting the non-additive gene action for fertile tillers per plant (Table 2). These results are in accordance with the results of [14] [16] [17]. The contrary results were reported by [18] [19].
For this trait the estimates GCA effects revealed that parent namely 9797 exhibited the highest positive GCA value which was 0.88 followed by Chakwal-50 (0.63) and the genotype 9801 exhibited the highest negative value -0.89 followed by Chakwal-86 (−0.63) (

No. of Grains per Spike
Analysis of variance for combining ability showed that there were highly significant means squares for GCA, SCA and RCA (Table 1). These results exhibited that the mean square value for GCA ruled over SCA mean square indicating predominant additive type of gene action for this trait. Higher GCA variance than variance of SCA also confirmed the presence additive genetic effects ( Table  2). The similar findings, which indicated additive type of gene action, were obtained by [9] [20].
Estimates of general combining ability effects (Table 3)  and Chakwal-50 × 9802 respectively (Table 5). Parent, Chakwal-50 proved to be best general combiner and cross, namely 9802 × Chakwal-86 was the best specific combination for No. of grains/spike.

No. of Spikelets per Spike
The analysis of variance for combining ability revealed that mean squares due to GCA (2.26), SCA (4.23) and RCA (2.83) were highly significant. Mean squares due to SCA (4.23) were greater than GCA mean squares (2.26) and reciprocal combining ability (2.83) mean squares (Table 1). Similarly estimates of variance components depicted that SCA variance was higher than GCA variance ( Table   2). Thus indicating predominant role of non additive gene action for No. of spikelets/spike. These results are in agreement with the findings of [10] [21] [22]. The different results which showed additive gene action for spikelets/spike were reported by [20] [23].

1000 Grain Weight
ANOVA for combing ability revealed that mean squares due to GCA (9.23), SCA (15.79) and RCA (12.84) effects were highly significant for 1000 grain weight ( Table 1). The mean squares due to SCA were greater than GCA mean squares. Thus, indicated the presence of non additive type of gene action. For 1000 grain weight the estimates of genetic components of variance due to general, specific and reciprocal combining ability were studied and the estimates were −0.58 (Vg), 8.91 (Vs) and 6.01 (Vr) as given in Table 2. Computation of variance components also exhibited the pronounced role of non additive gene action in controlling the inheritance of 1000 grain weight. The similar conclusion that indicated non additive effects were obtained by [9] [24].

Grain Yield per Plant
ANOVA for combining ability (Table 1) exhibited highly significant mean American Journal of Plant Sciences squares due to GCA (4.64), SCA (9.33) and RCA (7.94). The mean squares due to SCA were greater than GCA mean squares. Thus, indicated the presence of non additive type of gene action for grain yield/plant. Computation of variance components also exhibited the pronounced role of non additive gene action in controlling the inheritance of grain yield/plant ( Table 2). The similar conclusion that indicated non additive effects were obtained by [8] [25]. These results differed from the findings of [23] who found additive gene action for grain yield/plant. Four parents showed positive GCA effects of which the genotype, Chakwal-50 was the best general combiner for grain yield/plant with highest positive GCA effects of 1.23 followed by 9797 (0.94) and one parent (9802) manifested negative GCA effects of −0.98 (

Conclusion
Parent Chakwal-50 proved to be the best general combiner for plant height, spike length, No. of spikelets per spike, No. of grains per spike and grain yield per plant. 9802 was the best general combiner for 1000 grain weight. 9797 showed high GCA effects for fertile tillers per plant while for flag leaf area Chakwal-86 was the good general combiner. So, it is concluded that Chakwal-50 may be used in breeding programmes to develop high yielding wheat varieties. 9802 × Chakwal-86 was the best specific combination for grain yield per plant and most of the yield related traits and this specific combination can be used in developing hybrid varieties.