Graphical and Numerical Analysis of the Components of Gene Effect on the Quality Traits of Bread Wheat (Triticum aestivum L.) under Varying Environmental Conditions

Abstract

Wheat is one of the main cereals. At this time, the crucial difficulty in improving nutritional traits is the influence on genotypes of different environments. Selecting superior genotypes on the basis of a gene effects analysis for varying environments is demanded. In this study, 10 different genotypes of bread wheat (Triticum aestivum L.) were used. Parents, hybrids, and two standard checks were evaluated in a complete randomized block design with three replicates in four environments: E₁ and E₂ (normal and late sown, Rabi 2018-19) & E₃ and E₄ (normal and late sown, Rabi 2019-20). The analysis of the components of the gene effect revealed that most of the characters were governed by additive and dominant gene actions in the environments; for gluten, the wet gluten (E₂) and starch (E₃) content were the only dominant components (H₁ and H₂) with a significant gene effect. Overdominance, asymmetrical distribution of positive–negative and dominant–recessive genes, and narrow-sense heritability were observed in most of the characters in all environments. In a graphical analysis, the regression value b was observed to be in unity among protein content (E₁ and E₃), sedimentation value (E₁, E₂, and E₃), and starch content (E₁), indicating the absence of digenic interactions. Based on the intercept of the regression line on the Wr axis, the degree of dominance for protein content (E₁ and E₃), sedimentation value (E₁, E₂, and E₃), and starch content (E₁) was depicted as overdominance. Therefore, a given population may be improved to isolate superior recombinants for the development of desired parents in future breeding programs.

1. Introduction

Wheat (Triticum aestivum L.), a major agronomic crop cultivated worldwide, is a self-pollinated long-day plant belonging to the Poaceae family that flourishes well in arid and semi-arid regions [1]. It is a chief staple food, supplying approximately 35% of the total food consumed by the global population [2] Because of varied climatic conditions and environmental stresses, wheat is a major crop contributing to food security in the world [3].

Approximately 95% of globally cultivated wheat is a hexaploid and it is extensively used to prepare baked products and bread [4]. Therefore, the composition and nutritional concentrations of wheat have a substantial impact on human health. Although it is potentially enriched, especially in calories, with essential nutrients, most wheat varieties grown today are deficient in nutrients [5]. A high amount of these minerals is wasted during milling, resulting in a deficiency of these minerals in the human diet and leading to malnutrition. Because of malnutrition caused by a cereal-based diet, approximately 2 billion people worldwide, particularly in Asian and African regions, have suffered [6]. As the global population is increasing alarmingly, this phenomenon will grow ever more serious if no urgent remedial strategies are implemented. Many scientists, researchers, and experts have been developing techniques to improve the nutrient content of nutrient-deficient wheat varieties.

Though numerous strategies have been established, they are costly and unsustainable for addressing malnutrition [7,8,9]. Nevertheless, effective approaches, such as supplementation, dietary diversification, fortification, and agronomic biofortification, should be implemented to solve these problems.

Biofortification involves breeding crops to enhance their nutritional value economically and sustainably [10]. It can be performed through conventional selective breeding or genetic engineering. Because of some barriers in the potential uptake of soil nutrients, cereal crops like wheat are usually mineral deficient; for this reason, fortification must be implemented [11]. Continuous applications of weak mineral-deficient fertilizers have negatively affected the nutrient availability of wheat [12]. Therefore, biofortification can be applied to deliver micronutrients to populations who have inadequate access to diverse diets [13,14].

In wheat, fortification can be performed through different approaches. For example, an agronomic approach involves the direct foliar or soil application of fertilizers. The molecular breeding approach includes genomic selection, marker-assisted selection, and quantitative trait locus mapping. Because numerous wild wheat relatives remain unexploited, wheat can be genetically improved by breeding [15,16,17]. Ordinarily, the qualitative and quantitative traits of wheat are governed by single and several genes, respectively [18,19,20]. Through conventional breeding, qualitative traits can be developed more easily than quantitative traits. With advanced technological development, new opportunities can integrate natural variation, genomic achievements, and agronomic applications to improve the iron and zinc content of wheat grains. Scientists should emphasize the development and evaluation of cultivars for their high yield and high quality to meet the demands of future population growth and ensure sustainable grain production in varying environmental conditions. Thus, wheat breeders should aim to produce well-adapted and high-yielding varieties with the finest end-use quality [21,22].

In this study, we aimed to use diallel analysis [23,24] to interpret the genetic content of the parents with respect to quality traits and assess the gene action in all the ways for different qualitative characters in bread wheat.

2. Materials and Methods

2.1. Plant Material and Field Performance Evaluation

The experimental genetic material was composed of 10 parents, their 45 hybrids, and 2 check varieties (

Table 1. List of genotypes and their pedigree.

Sr. No.	Genotypes	Pedigree	Source
1	GW 451	GW324/4/CROC_1/AE.SQUARROSA (205)/JUP/JY/3/SKAUZ/4/KAUZ/5/GW 339	Center of Excellence for Research on Wheat, SDAU, Vijapur 382 870
2	GW 496	HD 2285/CPAN 1861
3	LOK 1	S 308/S 311
4	GW 322	PBW 173/GW 196
5	GW 366	DL 802-3/GW 232
6	HI 1544	HIND162/BOBWHITE/CPAN 2099
7	GW 173	TW 275-7-6-10/LOK1
8	GW 11	LOK 1/HW 1042//LOK 1
9	HD 2864	DL509-2/DL377-8
10	UAS 385	GW344/UAS239/DWR162
Standard check varieties
1	MACS 6222-TS	HD 2189*2//MASC 2496
2	HD 2932-LS	KAUZ/STAR//HD 2643

). The crosses between parents were made in Rabi 2017-18 with 10 × 10 diallel mating excluding reciprocals. The parents, hybrids, and standard checks were evaluated in a complete randomized block design with three replicates at the Regional Research Station, Anand Agricultural University, Anand, India (22°35′ N, 72°55′ E) under normal and late sowing conditions in four environments: E₁ and E₂ under normal (15 to 20 November) and late sowing (1 to 10 December) in Rabi 2018-19 and E₃ and E₄ under normal (15 to 20 November) and late (1 to 10 December) sowing in Rabi 2019-20 (Table S1). A single row of each genotype was planted in a length of 3 m with 20 cm × 10 cm (normal sowing) and 18 cm × 10 cm (late sowing) inter- and intra-row spacing, respectively. All the recommended cultural practices were implemented to raise the crops. Six component traits were observed from the diallel: protein content, sedimentation value, wet gluten content, starch content, iron content, and zinc content.

Grain Protein. These parameters were estimated using Fourier Transform-Near Infrared Reflectance Spectroscopy (FT-NIRS) at the Centre of Excellence for Research on Wheat, Sardar krushinagar Dantiwada Agricultural University, Vijapur. FT-NIRS is a rapid and non-destructive technique routinely used in the food and feed industries.

Grain Fe and Zn analysis. All samples of wheat grain were determined for the micronutrients (Fe and Zn) using an inductively-coupled plasma (ICP-OES), Model Optima 7000 DV, PerkinElmer, Inc., Waltham, MA, USA and the reliability of data was ensured by analyzing blanks at the Micronutrient Research Scheme (ICAR), Anand Agricultural University, Anand.

Data were subjected to analysis of variance to determine significant differences among genotypes. Diallel analysis and the components of genetic variance were estimated in accordance with previously described methods [25,26].

2.2. Graphical and Numerical Approach

The components of genetic variance of the traits with a well-fitted additive–dominance model were computed via the diallel cross method [24]. The adequacy of this model was evaluated with the help of the t² test [23], and a graphical analysis was conducted [25]. The replicated mean data were analyzed statistically using SPAR version 2.0 (IASRI, Pusa, New Delhi, India) [27]. The material used in this experiment was examined in terms of the treatment with assumptions basic to Hayman diallel analysis. The parents used in this study were homozygous and diverse in their origin, while the maternal effects were presumed to be absent in the studied material. Two general tests, namely, the t² test and regression of Wr on Vr, were used to test other assumptions.

2.3. Estimation of Genetic Parameters

Subsequently, the components of genetic variation, viz additive effects (D), dominant components (H₁ & H₂), environmental variance (E), covariance of additive and dominance effects (F), and dominance effects of all loci in heterozygous phase (h²) were estimated. Component analysis was performed only for the data that fit the additive dominance model. These genetic components of variation were calculated from the diallel table as per [24].

3. Results

3.1. Numerical Approach

The estimated t² values of different characters in each environment are presented in

Table 2. t² and regression coefficient of different metric characters of Wr on Vr.

SN	Characters	t2 Test				b (Regression)
		E1	E2	E3	E4	E1	E2	E3	E4
1	Protein content	0.09	2.37	0.019	1.33	0.80 $$	0.32 ++	0.89 $$	0.44 +
2	Sedimentation value	0.43	0.13	0.75	0.46	0.90 $$	0.71 $	0.56 $	0.64
3	Gluten: wet gluten	3.07	1.63	1.40	1.74	0.26 ++	0.32 +	0.29 +	0.50
4	Starch content	2.05	0.25	3.09	10.59 **	0.73 $$	0.25	−0.0008 ++	−0.85
5	Iron content	31.63 **	1.22	15.94 **	1.27	−0.007 ++	−0.19 ++	0.17 ++	−0.23 ++
6	Zinc content	12.52 **	0.03	9.72 **	1.49	0.30 $++	0.43	0.27 ++	−0.14 ++

** Significant at 1% levels, respectively; $, $$ significant at 5% and 1% levels, respectively, where Ho: b = 0; +, ++ Significant at 5% and 1% levels, respectively when Ho: b = 1. E₁: Normal Sowing & E₂: Late Sowing (Rabi 2018-19), E₃: Normal Sowing & E₄: Late Sowing (Rabi 2019-20).

.

3.1.1. Protein Content

In all environments, t² was not significant. The significance of additive (D) and dominant components (H₁ and H₂) of the gene effect revealed that PC was governed by additive and dominant gene actions in all environments (

Table 3. Genetic components of protein content and sedimentation value.

Genetic Components	Protein Content				Sedimentation Value
	E1	E2	E3	E4	E1	E2	E3	E4
$\hat{\mathrm{E}}$	0.00	0.05 *	0.06	0.05 **	0.44	0.50 **	0.62 *	0.67 **
$\hat{\mathrm{D}}$	0.77 **	0.15 **	0.72 **	0.13 **	6.05 **	5.29 **	4.91 **	5.28 **
$\hat{\mathrm{F}}$	1.26 **	0.29 **	1.21 **	0.17	7.02 **	7.12 **	4.41 **	7.05 **
${\hat{\mathrm{H}}}_1$	2.11 **	0.70 **	2.13 **	0.60 **	12.81 **	8.67 **	10.29 **	8.21 **
${\hat{\mathrm{H}}}_2$	1.40 **	0.47 **	1.42 **	0.44 **	8.98 **	5.73 **	7.81 **	5.41 **
	4.21 **	0.21 **	5.09 **	0.12 **	26.67 **	0.20	22.50 **	0.46
(${\hat{\mathrm{H}}}_1$/$\hat{\mathrm{D}}$)0.5	1.65	2.17	1.72	2.19	1.45	1.28	1.45	1.25
${\hat{\mathrm{H}}}_2$/4${\hat{\mathrm{H}}}_1$	0.17	0.17	0.17	0.18	0.18	0.17	0.19	0.16
KD/KR	2.95	2.62	2.91	1.88	2.33	3.22	1.90	3.30
${\hat{\mathrm{h}}}^{{}_2}$/${\hat{\mathrm{H}}}_2$	3.01	0.45	3.58	0.27	2.97	0.03	2.88	0.09
% Heritability (narrow sense)	47.09	19.92	38.07	17.02	44.46	59.75	36.94	57.76

KD/KR = {(4${\hat{\mathrm{D}}\hat{\mathrm{H}}}_1$)^0.5 + $\hat{\mathrm{F}}$}/{(4${\hat{\mathrm{D}}\hat{\mathrm{H}}}_1$)^0.5 − $\hat{\mathrm{F}}$}; *, ** Significant at 5% and 1% levels, respectively. E₁: Normal Sowing & E₂: Late Sowing (Rabi 2018-19), E₃: Normal Sowing & E₄: Late Sowing (Rabi 2019-20).

). The estimates of the average degree of dominance suggested the existence of overdominant gene action in all environments. When H₂/4H₁ was <0.25, the distribution of positive and negative alleles among the parental genotypes in all environments was asymmetrical. A positive F and >1 KD/KR indicated that PC was controlled by overdominant genes in all environments. The h²/H₂ ratio was >1 in E₁ and E₃, suggesting that PC was governed by more than one dominant group of genes. The estimates of h² (net dominant gene effect sum over loci) for PC were significant and positive in all environments. The estimates of narrow-sense heritability were moderate in E₁ (47.09%) and low in E₂ (19.92%), E₃ (38.07%), and E₄ (17.02%).

3.1.2. Sedimentation Value

In all environments, t² was not significant. The significance of the additive (D) and dominant components (H₁ and H₂) of the gene effect revealed that sedimentation value was governed by additive and dominant gene actions in all environments (Table 3).

The estimated average degree of dominance (H1/D)^0.5 suggested the existence of an overdominant gene action in all environments. When H₂/4H₁ was <0.25, the distribution of positive and negative alleles among parental genotypes in all environments was asymmetrical.

A positive F and >1 KD/KR indicated that the character was controlled by overdominant genes in all environments. At least two, or more than two, dominant groups of genes governed the sedimentation value in E₁ and E₃ when the h²/H₂ ratio was >2. The significant and positive estimates of h² in E₁ and E₃ indicated the net dominant gene effect sum over loci. The estimates of narrow-sense heritability were low in E₃ (36.94%) and moderate in E₁ (44.46%). The high narrow-sense heritability in E₂ (59.75%) and E₄ (57.76%) confirmed that sedimentation value was predominantly governed by additive genes.

3.1.3. Gluten: Wet Gluten

In all environments, t² was not significant. The significance of the additive (D) and dominant components (H₁ and H₂) of the gene effect in E₁, E₃, and E₄ revealed that gluten: wet gluten was governed by additive and dominant gene actions. The significance of the dominant components (H₁ and H₂) of the gene effect in E₂ indicated that only a dominant gene action was involved in gluten: wet gluten expression (

Table 4. Genetic components of gluten: wet gluten and starch content.

Genetic Components	Gluten: Wet Gluten				Starch Content
	E1	E2	E3	E4	E1	E2	E3	E1
$\hat{\mathrm{E}}$	0.34	0.47 **	0.50 *	0.45 **	0.28 **	0.21 **	0.21 **	0.28 **
$\hat{\mathrm{D}}$	2.07 **	0.28	1.89 **	0.88 **	0.27 **	0.41 **	−0.04	0.27 **
$\hat{\mathrm{F}}$	3.61	−0.03	2.80	0.57	0.35 **	0.92 **	−0.10	0.35 **
${\hat{\mathrm{H}}}_1$	7.49 **	3.10 **	5.77 **	2.67 **	1.04 **	1.15 **	0.86 **	1.04 **
${\hat{\mathrm{H}}}_2$	4.83 **	2.74 **	3.84 **	2.21 **	0.81 **	0.53 **	0.64 **	0.81 **
${\hat{\mathrm{h}}}^{{}_2}$	10.60 **	3.43 **	8.62 **	0.16	4.26 **	−0.07	0.20	4.26 **
(${\hat{\mathrm{H}}}_1$/$\hat{\mathrm{D}}$)0.5	1.90	3.31	1.75	1.75	1.97	1.67	0.00	1.97
${\hat{\mathrm{H}}}_2$/4${\hat{\mathrm{H}}}_1$	0.16	0.22	0.17	0.21	0.20	0.11	0.19	0.20
KD/KR	2.69	0.97	2.47	1.46	1.99	5.06	1.74	1.99
${\hat{\mathrm{h}}}^{{}_2}$/${\hat{\mathrm{H}}}_2$	2.19	1.25	2.24	0.07	5.26	−0.13	0.31	5.26
% Heritability (narrow sense)	28.37	5.35	27.49	18.38	12.92	28.22	−2.20	12.92

KD/KR = {(4${\hat{\mathrm{D}}\hat{\mathrm{H}}}_1$)^0.5 + $\hat{\mathrm{F}}$}/{(4${\hat{\mathrm{D}}\hat{\mathrm{H}}}_1$)^0.5 − $\hat{\mathrm{F}}$}; *, ** Significant at 5% and 1% levels, respectively. E₁: Normal Sowing & E₂: Late Sowing (Rabi 2018-19), E₃: Normal Sowing & E₄: Late Sowing (Rabi 2019-20).

).

The estimated average degree of dominance suggested the occurrence of an overdominant gene action in all environments. The H₂/4H₁ ratio revealed that the distribution of positive and negative alleles among the parents in all environments was asymmetrical. A positive F and >1 KD/KR indicated that the character was controlled by overdominant genes in all environments except E₂. The h²/H₂ ratio was >1 in all environments except E₄. The significant and positive estimates of h² in E₁, E₂, and E₃ indicated the net dominant gene effect sum over loci. The estimates of narrow-sense heritability were low in E₁ (28.37%), E₂ (5.35%), E₃ (27.49%), and E₄ (18.38%), which also confirmed that gluten: wet gluten was primarily controlled by dominant gene effects.

3.1.4. Starch Content

In all environments except E₄, t² was not significant. The additive (D) and dominant components (H₁ and H₂) of the gene effect were significant in E₁ and E₂; conversely, only the dominant components (H₁ and H₂) of the gene effect were significant in E₃ (Table 4). The average degrees of dominance indicated the existence of overdominance in E₁ and E₂. When H₂/4H₁ was <0.25, the distribution of positive and negative alleles among the parental lines in all the analyses was asymmetrical. A positive F and >1 KD/KR in E₁ and E₂ indicated that the distribution of dominant and recessive genes among the parental genotypes with overdominant genes was symmetrical. In E₁, h²/H₂ was >1, which indicated that starch content was governed by more than one dominant group of genes. The significant and positive estimates of h² in E₁ corresponded to the net dominant gene effect sum over loci. The narrow-sense heritability estimated for the starch content was low in E₁ (12.92%) and E₂ (28.22%).

3.2. Graphical Analysis

In this study, we performed a Wr–Vr homogeneity t² test to examine the validity of the basic assumptions postulated for diallel analysis [24]. In any case, if t² was significant for the studied characters in an individual environment alone, or in combination, failure of the basic assumptions of diallel analysis was considered. In the current study, non-significant t² was detected for three characters, namely, protein content in E₁ and E₃; sedimentation value in E₁, E₂, and E₃; and starch content in E₁ (Table 2).

However, the validity of the assumption in terms of the presence or absence of epistatic gene effects could be verified more precisely on the basis of the Wr–Vr graph, the array points on the Wr–Vr graph were expected to fall on the line of unity (450); thus, b significantly deviated from 0, not from 1, suggesting the absence of an epistatic gene effect. b was statistically at par one for protein content (E₁ and E₃), sedimentation value (E₁, E₂, and E₃), and starch content (E₁), indicating the absence of digenic interactions for these characters in their respective environments. One character (gluten: wet gluten) had a nonrandom distribution of genes at different loci among the parents and/or the presence of interallelic interaction at different loci; hence, this character was excluded from the graphical analysis. Figure 1, Figure 2, Figure 3, Figure 4, Figure 5 and Figure 6 illustrate the results of the graphical analysis.

3.2.1. Protein Content

The Wr–Vr graph showed linear regression, resulting in a unit slope in E₁. The regression line (Y = 0.8041x − 0.2373) intercepted the Wr axis below the origin, indicating the occurrence of an overdominant gene action. The scattered array points of the parents on and around the regression line revealed the existence of sufficient variability among the parents. The array point of GW 173 and GW 11 was the farthest from the origin, suggesting that they were the carriers of high-frequency recessive genes. Moreover, LOK 1, GW 322, and GW 366 occupied intermediate positions on the regression line, indicating that the dominant and recessive genes were equally distributed. Conversely, the remaining parents would be the carriers of genes causing an interallelic interaction because they occupied their positions outside the truncated area of the graph (Figure 1).

Figure 1. Wr–Vr graph for protein content in E₁. P1 = GW 451; P2 = GW 496; P3 = LOK 1; P4 = GW 322; P5 = GW 366; P6 = HI 1544; P7 = GW 173; P8 = GW 11; P9 = HD 2864; P10 = UAS 385.

Figure 1. Wr–Vr graph for protein content in E₁. P1 = GW 451; P2 = GW 496; P3 = LOK 1; P4 = GW 322; P5 = GW 366; P6 = HI 1544; P7 = GW 173; P8 = GW 11; P9 = HD 2864; P10 = UAS 385.

The regression line Y = 0.8941x − 0.3358 (Figure 2) intercepted the Wr axis below the origin, indicating an overdominant gene action in E₃. The array point of GW 322 and GW 496 was the nearest to the origin; thus, it likely contributed to the overdominant genes. GW 173 and GW 11 occupied a position at the tail of the regression line, indicating that they were carriers of an excess of recessive genes. Moreover, LOK 1 and GW 366 occupied intermediate positions on the regression line, indicating that dominant and recessive genes were equally distributed between them. Conversely, GW 451, HI 1544, HD 2864, and UAS 385 could be the carriers of genes causing the interallelic interaction as those occupied positions outside the truncated area of the graph.

Figure 2. Wr–Vr graph for protein content in E₃. P1 = GW 451; P2 = GW 496; P3 = LOK 1; P4 = GW 322; P5 = GW 366; P6 = HI 1544; P7 = GW 173; P8 = GW 11; P9 = HD 2864; P10 = UAS 385.

Figure 2. Wr–Vr graph for protein content in E₃. P1 = GW 451; P2 = GW 496; P3 = LOK 1; P4 = GW 322; P5 = GW 366; P6 = HI 1544; P7 = GW 173; P8 = GW 11; P9 = HD 2864; P10 = UAS 385.

3.2.2. Sedimentation Value

In E₁, the regression line Y = 0.9068x − 1.5904 in the Wr–Vr graph intercepted the Wr axis below the origin, indicating the overdominance behavior of the interacting alleles. GW 366 likely contributed overdominant genes because their array point occupied a position near the origin. GW 173 transmitted a relatively large number of recessive genes because the array point of this parent resided at the tail of the regression line. Moreover, GW 451, GW 322, GW 11, HD 2864, and UAS 385 occupied intermediate positions on the regression line, indicating that dominant and recessive genes were equally distributed between them. Conversely, the remaining parents could be the carriers of genes causing the interallelic interaction because they occupied positions outside the truncated area of the graph (Figure 3).

Figure 3. Wr–Vr graph for sedimentation value in E₁. P1 = GW 451; P2 = GW 496; P3 = LOK 1; P4 = GW 322; P5 = GW 366; P6 = HI 1544; P7 = GW 173; P8 = GW 11; P9 = HD 2864; P10 = UAS 385.

Figure 3. Wr–Vr graph for sedimentation value in E₁. P1 = GW 451; P2 = GW 496; P3 = LOK 1; P4 = GW 322; P5 = GW 366; P6 = HI 1544; P7 = GW 173; P8 = GW 11; P9 = HD 2864; P10 = UAS 385.

In E₂, the regression line Y = 0.711x − 0.4965 intercepted the Wr axis below the origin, indicating the overdominance behavior of the interacting alleles. HI 1544 occupied the position nearest the origin, suggesting it contained more dominant genes; however, GW 322 was the farthest from the origin, indicating the presence of more recessive genes. GW 451, LOK 1, GW 366, GW 173, GW 11, HD 2864, and UAS 385 occupied an intermediate position on the regression line, indicating that dominant and recessive genes were equally distributed between them, while GW 496 could be outside the truncated area of the graph (Figure 4). In E₃, the regression line Y = 0.5645x − 0.302 intercepted the Wr axis below the origin, indicating overdominance. GW 173 occupied the farthest position from the origin, indicating the presence of more recessive genes.

Figure 4. Wr–Vr graph for sedimentation value in E₂. P1 = GW 451; P2 = GW 496; P3 = LOK 1; P4 = GW 322; P5 = GW 366; P6 = HI 1544; P7 = GW 173; P8 = GW 11; P9 = HD 2864; P10 = UAS 385.

Figure 4. Wr–Vr graph for sedimentation value in E₂. P1 = GW 451; P2 = GW 496; P3 = LOK 1; P4 = GW 322; P5 = GW 366; P6 = HI 1544; P7 = GW 173; P8 = GW 11; P9 = HD 2864; P10 = UAS 385.

GW 451, LOK 1, GW 322, GW 11, HD 2864, and UAS 385 occupied intermediate positions on the regression line, indicating that dominant and recessive genes were equally distributed between them. Conversely, GW 496 and HI 1544 could be the carriers of genes causing an interallelic interaction because they occupied positions outside the truncated area of the graph (Figure 5).

Figure 5. Wr–Vr graph for sedimentation value in E₃. P1 = GW 451; P2 = GW 496; P3 = LOK 1; P4 = GW 322; P5 = GW 366; P6 = HI 1544; P7 = GW 173; P8 = GW 11; P9 = HD 2864; P10 = UAS 385.

Figure 5. Wr–Vr graph for sedimentation value in E₃. P1 = GW 451; P2 = GW 496; P3 = LOK 1; P4 = GW 322; P5 = GW 366; P6 = HI 1544; P7 = GW 173; P8 = GW 11; P9 = HD 2864; P10 = UAS 385.

3.2.3. Starch Content

The regression line Y = 0.7382x − 0.2171 intercepted the Wr axis below the origin, indicating the presence of an overdominant gene action in E₁. The scattered array points of the parents on and around the regression line revealed the existence of sufficient variability among the parents. GW 11 attained the positions farthest from the origin, indicating that it was the carrier of high-frequency recessive genes. Conversely, LOK 1, GW 366, HI 1544, and GW 173 occupied intermediate positions on the regression line, indicating that dominant and recessive genes were equally distributed between them. GW 451, GW 496, GW 322, HD 2864, and UAS 385 could be the carriers of genes causing an interallelic interaction because they occupied positions outside the truncated area of the graph (Figure 6).

Figure 6. Wr–Vr graph for starch content in E₁. P1 = GW 451; P2 = GW 496; P3 = LOK 1; P4 = GW 322; P5 = GW 366; P6 = HI 1544; P7 = GW 173; P8 = GW 11; P9 = HD 2864; P10 = UAS 385.

Figure 6. Wr–Vr graph for starch content in E₁. P1 = GW 451; P2 = GW 496; P3 = LOK 1; P4 = GW 322; P5 = GW 366; P6 = HI 1544; P7 = GW 173; P8 = GW 11; P9 = HD 2864; P10 = UAS 385.

4. Discussion

4.1. Numerical Approach

None of the characters estimated for t² was significant; however, the estimates were non-significant in all environments in the case of three characters, namely, protein content, sedimentation value, and wet gluten. For the remaining characters, t² was significant in E₁, E₂, E₃, and E₄, or a combination of either of the environments. The significance of t² suggested the failure of certain assumptions of the diallel analysis. Therefore, the components of genetic variance and related parameters of the characters have been presented and discussed, where the estimated t² of uniformity of the Wr–Vr value was not significant.

The analysis of the components of the gene effect revealed that most of the characters were governed by additive and dominant gene actions across the environments; the influence of the dominant gene was greater than that of the additive gene except for sedimentation value in E₂ and E₄, wet gluten in E₄, starch content in E₂, and iron content in E₂; conversely, for gluten, wet gluten (E₂), starch content (E₃), and zinc content (E₄), only the dominant component (H₁ and H₂) of the gene effect was significant. Genetic components showed that the inheritance of quality traits was under the control of additive effects with partial dominance under normal as well as drought-stress conditions. [28]

The average degrees of dominance varied across the environments. The overdominance behavior of interacting alleles was depicted for protein content (E₁, E₂, E₃, and E₄), sedimentation value (E₁, E₂, E₃, and E₄), gluten: wet gluten (E₁, E₂, E₃, and E₄), starch content (E₁ and E₂), iron content (E₂ and E₄), and zinc content (E₂ and E₄).

The distribution of positive–negative and dominant–recessive genes was asymmetrical in most of the characters. H₂/4H₁ indicated a symmetrical distribution of positive and negative dominant genes in parents in all the studied characters and the result is in agreement with [29].

However, overdominant genes were observed for protein content (E₁, E₂, E₃, and E₄), sedimentation value (E₁, E₂, E₃, and E₄), gluten: wet gluten (E₁, E₃, and E₄), starch content (E₁, E₂, and E₃), iron content (E₂ and E₄), and zinc content (E₂ and E₄); conversely, an excess of recessive genes was detected for gluten: wet gluten (E₂).

The estimates of narrow-sense heritability were low for protein content (E₂, E₃, and E₄), sedimentation value (E3), gluten: wet gluten (E₁, E₂, E₃, and E₄), starch content (E₁, E₂, and E₃), iron content (E₂ and E₄), and zinc content (E₂ and E₄). This result indicated that these characters were more influenced by the dominant gene effect than by the additive gene effect. The narrow-sense heritability was moderate for protein content (E₁) and sedimentation value (E₁), confirming that these characters were under the genetic control of additive and dominant genes. The high narrow-sense heritability observed in sedimentation value (E₂ and E₄) suggested that the character was mainly governed by the additive gene effect. The finding also indicated that under saline conditions heritability in the narrow sense (h² n) was very low, which indicates a possible strong influence of stress in the growing environment [30].

4.2. Graphical Analysis

In graphical analysis, b was shown to be in unity for protein content (E₁ and E₃), sedimentation value (E₁, E₂, and E₃), and starch content (E₁), revealing the absence of digenic interactions of these characters in their respective environments. The remaining characters exhibited a nonrandom distribution of genes at different loci among the parents and/or the presence of interallelic interaction at different loci; hence, these characters were excluded from the graphical analysis.

Based on the intercept of the regression line on the Wr axis, the degree of dominance was depicted as overdominance for protein content (E₁ and E₃), sedimentation value (E₁, E₂, and E₃), and starch content (E₁), whereas none of the characters exhibited complete dominance. The analysis also revealed that negative intercepts of the Wr–Vr regression line supported an overdominance gene action in agreement with the findings of [31].

5. Conclusions

The analysis of the components of the gene effect revealed that most of the characters were governed by additive and dominant gene actions in different environments. The influence of the dominant gene was higher than that of the additive gene except for the sedimentation value in E₂ and E₄, wet gluten in E₄, starch content in E₂, and iron content in E₂. In graphical analysis, b was found to be in unity among protein content (E₁ and E₃), sedimentation value (E₁, E₂, and E₃), and starch content (E₁), indicating the absence of digenic interactions for these characters in their respective environments. The remaining characters exhibited a nonrandom gene distribution at different loci among parents and/or interallelic interactions at different loci. Therefore, a given population may be improved to isolate superior recombinants for the development of desired parents in future breeding programs. Our results can be utilized in specific quality breeding programs to reach conducive results.