Genetic analysis and association detection of agronomic traits in maize genotypes

doi:10.21203/rs.3.rs-4952470/v1

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Genetic analysis and association detection of agronomic traits in maize genotypes

https://doi.org/10.21203/rs.3.rs-4952470/v1

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In maize breeding, enhancing yield through genetic insights is crucial yet challenged by the complex interplay of agronomic traits. This study utilized a diallel mating design involving nine advanced early maize lines to dissect the genetic architecture underlying key agronomic traits and their impact on yield. Over two consecutive years (2018–2019 and 2019–2020), 36 hybrids derived from these lines were grown across two locations, Karaj, Alborz, Iran and Kermanshah (2019–2020), Iran, in a randomized complete block design with three replications. The study aimed to evaluate the general combining ability of the parental lines and the specific combining ability of their hybrids, alongside the mutual influences of critical traits on yield. The analysis of variance revealed significant differences at 1% and 5% probability levels among the hybrids for all traits studied, indicating substantial genetic variability. Diallel analysis suggested that both additive and non-additive genetic effects are crucial in controlling traits such as kernel yield, kernel rows, kernel in row, 1000 kernel weight, plant height, ear height, kernel moisture, and ear wood. Additive effects, as indicated by the Baker's ratio, predominated for these traits. Among the parental lines, KE 79017/3211 demonstrated the strongest general combining ability for kernel yield. Hybrids K 1264/5 − 1×KE 76009/311, KE 77005/2×KE 75016/321, KE 77008/1×KE 77004/1, and KE 77008/1×KE 79017/3211 exhibited significant and positive specific combining ability effects for kernel yield, highlighting their potential in yield-enhancing breeding programs. Correlation analysis showed no significant association between KY*KIN with the KY*KW. However, there were weak positive correlations between KY*KR with other traits such as KY*PH, KY*KR, and KY*EH. The biplot analyses identified genotypes 4, 12, and 31 as superior across various trait combinations. Genotype 12 emerged as notably high-yielding based on average tester coordinates. Using the multi-trait stability index and imposing a selection pressure of 25%, genotype 10 was ranked highest, followed by genotypes 9, 13, 11, 1, 2, and 16, which were considered the most stable and ideal across all evaluated traits. This comprehensive study underscores the importance of both general combining ability and specific combining ability in maize breeding and highlights specific genotypes and hybrid combinations with promising traits for yield enhancement.

Biological sciences/Plant sciences/Plant breeding

Biological sciences/Plant sciences

Breeding

Combining ability

Combination

Enhancement Key trait

Addressing global food security has emerged as a paramount scientific challenge ^1,2. The global population, currently estimated at seven billion, is anticipated to rise to approximately 10 billion by 2050 and potentially 11 billion by the century's end ³. Consequently, as the population expands, the demand for food is expected to surge significantly ⁴. Although global pressures on food production have intensified, demands have been met thus far through enhancements in agricultural productivity and efficiency ⁵. Yet, it is projected that food production must increase by 70% by 2050 to sustain over 9 billion people ^6,7. However, there are pressing concerns that the increased demand for plant products may not be satisfied with existing cultivars and current agricultural practices ⁵. Moreover, the degradation of agricultural lands due to erosion, groundwater pollution from excessive fertilizer and pesticide use, declining crop yields in various regions, and the depletion of natural resources pose significant threats to food security ^8–10. Compounding these issues, observed and projected climate changes are expected to further challenge agriculture and reduce crop yields globally ^11–13. As such, agricultural and environmental research sectors are confronted with unprecedented challenges and are in urgent need of innovative approaches that minimize environmental impacts to secure global food supplies.

Developing new cultivars to enhance yield potential in high-yield environments is a strategic response to the food security challenge ^14–16. This approach holds promise not only in optimal conditions but also under suboptimal growth scenarios ^17,18, where performance improvements are still achievable ^19,20. In agricultural science, breeding maize (Zea mays L.), a crucial crop with diverse nutritional profiles and applications, is central to addressing global food security issues. As a staple food, maize sustains billions of people worldwide ^21–23 and serves essential roles in animal feed, biofuel production, and industrial applications ²⁴. Enhancements in maize breeding have transformed its nutritional and agricultural impact. Notably, biofortification efforts have yielded quality protein maize, which boasts nearly double the amounts of lysine and tryptophan ²⁵. Moreover, breeding programs are increasingly focused on developing disease and pest-resistant varieties, thereby reducing agricultural losses and diminishing the reliance on chemical pesticides. Furthermore, with the looming threats of climate change, the importance of developing maize varieties that can withstand extreme weather conditions, such as droughts or excessive rainfall, has become more pronounced ²⁶.

Breeders are actively seeking optimal genetic materials for breeding purposes and methods to effectively present their results. Many breeders encounter challenges in selecting appropriate parents and crosses for breeding programs ^18,27. This process requires a thorough understanding of the genetic structure and combining ability of the parental lines used in crosses. In 1942, Sprague and Tatum introduced modifications to the concept of combining ability, coining the terms 'general combining ability (GCA)' and 'specific combining ability (SCA)' ²⁸. Essentially, general and specific combining ability relate to the modes of gene action controlling trait expression. GCA pertains to the additive genetic variance, while SCA relates to the non-additive portion of the total variance, predominantly influenced by dominance deviations and epistasis ²⁹. To assess these combining abilities in breeding lines, several methodologies have been proposed, including two-parent designs, multiple crosses, test crosses, line-tester analyses, and diallel mating designs ^18,30. These methods aim to discern the inheritability of traits and to determine the GCA and SCA of the parents and crosses, respectively. Commonly, diallelic crosses are employed, which are recognized as a robust tool for estimating recombination effects that aid in the selection of suitable parents and favorable crosses ³¹. This systematic crossing design enables breeders to evaluate the combinatory ability of different parents and to estimate the genetic effects influencing desired traits. Several studies have focused on genetic analysis and the development of cultivars with favorable genetic potentials in various crops such as maize ^32–35, rice ^36–38, barley ^27,39, wheat ^40,41, and sorghum ^42,43.

In the breeding process, selecting suitable parents is crucial, but equally important is understanding the relationships between traits, especially in relation to yield. Breeders often face challenges due to unfavorable and negative correlations between key traits ^44–46. In maize, kernel yield (KY) results from a combination of quantitative and qualitative traits, presenting a spectrum of negative to positive correlations among these traits. These correlations complicate breeding programs ¹³. Therefore, breeders must balance the improvement of multiple traits while managing the adverse correlations among key traits ². To address this, the genotype by yield*trait (GYT) biplot method has been proposed ⁴⁷. This method assumes that yield is the most crucial trait and that other key traits are significant only when linked with high yield. Furthermore, it suggests that the superiority of a genotype should be evaluated based on its ability to combine yield with other traits ⁴⁷. Ultimately, yield is the primary determinant of a genotype's efficiency, while other traits are valuable only if they correlate with suitable yield levels. Thus, in selecting superior cultivars, the integration of yield with other traits is prioritized over evaluating cultivars based solely on individual traits; a genotype is considered ideal when it achieves an optimal balance between trait correlations and excels across various environments ⁴⁸.

The continuous development and distribution of improved maize cultivars are vital not only to meet the nutritional demands of a growing global population but also to tackle challenges related to climate change and food security. Ongoing research and diverse improvement strategies are necessary to enhance the nutritional value and sustainability of maize as a key global crop. The present study aimed to identify superior genotypes among a set of promising maize genotypes. It evaluated the GCA of parents and the SCA of crosses, and assessed the mutual influences of key traits on yield. Ultimately, superior genotypes were selected based on these evaluations.

Plant material

In this study, a selection of inbred maize lines from different heterotic groups was obtained from the Seed and Plant Improvement Institute (SPII), Karaj, Alborz, Iran. These lines were crossed in a one-way diallel scheme to produce hybrid seeds (Table 1). Controlled pollination was used for all crossings. Maternal lines were centrally planted, while paternal lines were positioned on either side, ensuring that pollination involved covering the maternal inflorescence with pollen from the paternal lines.

Table 1

Characters of selected inbred lines of maize and their hybrids used in the research
Code	Line/hybrid name	Code	Line/hybrid name	Code	Line/hybrid name
Line
P1	K 1263/1	P4	NK79	P7	KE 75016/321
P2	K 1264/5 − 1	P5	KE 77008/1	P8	KE 77004/1
P3	KE 77005/2	P6	KE 76009/311	P9	KE 79017/3211
Hybrid
1	K 1264/5 − 1×K 1263/1	13	KE 75016/321×K 1264/5 − 1	25	KE 77004/1×NK79
2	KE 77005/2×K 1263/1	14	KE 77004/1×K 1264/5 − 1	26	KE 79017/3211×NK79
3	NK79×K 1263/1	15	KE 79017/3211×K 1264/5 − 1	27	KE 76009/311×KE 77008/1
4	KE 77008/1×K 1263/1	16	NK79×KE 77005/2	28	KE 75016/321×KE 77008/1
5	KE 76009/311×K 1263/1	17	KE 77008/1×KE 77005/2	29	KE 77004/1×KE 77008/1
6	KE 75016/321×K 1263/1	18	KE 76009/311×KE 77005/2	30	KE 79017/3211×KE 77008/1
7	KE 77004/1×K 1263/1	19	KE 75016/321×KE 77005/2	31	KE75016/321×KE76009/311
8	KE 79017/3211×K 1263/1	20	KE 77004/1×KE 77005/2	32	KE 77004/1×KE 76009/311
9	KE 77005/2×K 1264/5 − 1	21	KE 79017/3211×KE 77005/2	33	KE79017/3211×KE76009/311
10	NK79×K 1264/5 − 1	22	KE 77008/1×NK79	34	KE 77004/1×KE 75016/321
11	KE 77008/1×K 1264/5 − 1	23	KE 76009/311×NK79	35	KE79017/3211×KE75016/321
12	KE 76009/311×K 1264/5 − 1	24	KE 75016/321×NK79	36	KE 79017/3211×KE 77004/1

Field location and data collection

The F1 hybrids were grown during 2018–2019 and 2019–2020 at the Seed and Plant Improvement Institute and during 2019–2020 at agricultural research station, Kermanshah, Iran (Table 2). The 36 hybrids obtained from the crosses were planted in a randomized complete block design with three replications. To manage potential non-uniformity within the blocks, each block was divided into two sub-blocks. The hybrids were sown in rows 3.25 meters long, spaced 75 cm apart, with 12 mounds per row at 32 cm intervals. All agronomic practices, including weed control, were manually conducted. Harvesting was performed at season's end, maintaining a 25 cm margin from both ends of each plot. Data collected included KY, 1000 kernel weight (KW), plant height (PH), ear height (EH), kernel row number (KR), kernels in row (KIN), kernel moisture (KM), and ear wood (EW). For each trait, 10 samples were analyzed. However, all international, national and institutional guidelines have been taken into account in various stages of experiments.

Table 2

The average, maximum and minimum temperatures as well as the rainfall data recorded at the research stations for the years 2018–2019, and 2019–2020
Location	Month	Maximum temperature (°C)		Minimum temperature (°C)		Rainfall (mm)
Location	Month	2019	2020	2019	2020	2019	2020
Karaj, Alborz, Iran	January	0.43	-1.63	9.22	6.32	57.20	28.10
	February	3.25	0.56	11.58	10.52	1.40	84.60
	March	6.22	5.41	13.68	16.28	101.60	25.40
	April	6.68	6.89	18.30	18.37	32.00	85.40
	May	12.82	13.07	28.01	27.91	11.70	20.90
	June	19.10	16.82	35.02	33.87	0.00	0.00
	July	20.54	20.62	37.50	35.54	0.00	0.30
	August	19.70	16.92	36.19	33.50	0.00	0.00
	September	14.25	16.35	30.81	30.33	30.00	20.30
	October	11.22	10.28	23.52	23.03	75.20	8.40
	November	3.04	6.42	12.43	13.86	11.00	64.40
	December	2.28	-0.52	10.61	7.09	28.40	54.80
Kermanshah, Kermanshah, Iran	January	-	9.02	-	3.90	-	32
	February	-	11.00	-	5.01	-	45
	March	-	18.20	-	7.05	-	57
	April	-	23.70	-	9.20	-	73.00
	May	-	26.81	-	11.42	-	11.00
	June	-	35.20	-	18.17	-	1.00
	July	-	38.00	-	21.39	-	0.00
	August	-	37.71	-	22.23	-	0.00
	September	-	33.40	-	17.43	-	1.00
	October	-	26.77	-	11.84	-	13.00
	November	-	21.00	-	10.05	-	25
	December	-	19.03	-	10.00	-	38

Statistical analysis

Data analysis was performed using SAS software version 9.2. An analysis of variance tested the diversity among genotypes, followed by a diallel analysis based on Griffing's method ^49,50, using a program by Zhang, et al. ⁵¹. This method divided the sum of squares from the crosses into components related to GCA and SCA, calculating the effects for each parent and cross respectively.

The correlation analysis of key traits was performed using the Pearson method. The first step in examining the GYT involved standardizing the original data. This was done following Eq. 1, as suggested by Yan and Frégeau-Reid ⁴⁷.

$$\:{P}_{ij}=\frac{{T}_{ij}-{\stackrel{-}{T}}_{j}}{{S}_{j}}$$

where $\:{P}_{ij}$ is the standardized value of genotype i for trait or yield-trait combination j in the standardized table, $\:{T}_{ij}$ is the original value of genotype i for trait or yield-trait combination j, $\:{\stackrel{-}{T}}_{j}$ is the mean across genotypes for trait or yield-trait combination j, and $\:{S}_{j}$ is the standard deviation for yield-trait combination j. The biplot representing the GYT was constructed using the first and second principal components (PCs) derived from the singular value decomposition (SVD) of the standardized data. In this model, the SVD breaks down the GYT data into genotype eigenvalues, yield-trait combination eigenvalues, and singular values. This process is based on Eq. 2 as proposed by Yan and Frégeau-Reid ⁴⁷.

$$\:{\text{P}}_{\text{i}\text{j}}=\left(d{\lambda\:}_{1}^{\alpha\:}{\zeta\:}_{\text{i}1}\right)\left({\lambda\:}_{1}^{1-\alpha\:}{\tau\:}_{1\text{j}}/\text{d}\right)+\left(d{\lambda\:}_{2}^{\alpha\:}{\zeta\:}_{\text{i}2}\right)\left({\lambda\:}_{2}^{1-\alpha\:}{\tau\:}_{2\text{j}}/\text{d}\right)+{\epsilon\:}_{\text{i}\text{j}}$$

where $\:{\zeta\:}_{\text{i}1}$ and $\:{\zeta\:}_{\text{i}2}$ are the eigenvalues for PC1 and PC2, respectively, for genotype i; $\:{\tau\:}_{1\text{j}}$ and $\:{\tau\:}_{2\text{j}}$ are the eigenvalues for PC1 and PC2, respectively for trait j, and $\:{\epsilon\:}_{\text{i}\text{j}}$ is the residual from fitting the PC1 and PC2 for genotype i on trait j; $\:{{\lambda\:}}_{1}$ and $\:{{\lambda\:}}_{1}$ are the singular values for PC1 and PC2, respectively. α is the singular value partitioning factor. The GYT biplot is created by plotting $\:d{\lambda\:}_{1}^{\alpha\:}{\zeta\:}_{\text{i}1}$ against $\:d{\lambda\:}_{2}^{\alpha\:}{\zeta\:}_{\text{i}2}$ for genotypes, and $\:{\lambda\:}_{1}^{1-\alpha\:}{\tau\:}_{1\text{j}}/\text{d}$ against $\:{\lambda\:}_{2}^{1-\alpha\:}{\tau\:}_{2\text{j}}/\text{d}$ for yield-trait combinations. This analysis focuses on four main patterns: 1. Exploring the associations between different yield-trait combinations, 2. Identifying the best genotype for each yield*trait combination, 3. Ranking genotypes based on a superiority index and evaluating their strengths and weaknesses, and 4. Ranking genotypes based on an ideal hypothetical genotype.

To identify the most superior maize genotypes, a comprehensive evaluation of key traits such as KY, KW, PH, EH, KR, KIN, KM, and EW is necessary. In order to assess the stability of these traits simultaneously, the multi-trait stability index (MTSI) was calculated using Eq. 2 ⁵².

$$\:{MTSI}_{i}={\left[\sum\:_{j=1}^{f}\:{\left({\gamma\:}_{ij}-{\gamma\:}_{j}\right)}^{2}\right]}^{0.5}$$

where $\:{MTSI}_{i}$ is the multi-trait stability index of the genotype i, $\:{\gamma\:}_{ij}$ is the score of the genotype i in the factor j, and $\:{\gamma\:}_{j}$ is the score of the ideal genotype in the factor j. The scores were calculated using factor analysis for genotypes and traits. Finally, stable genotypes were selected based on positive selection differentials for traits intended to increase and negative selection differentials for traits intended to decrease.

Genetic analysis

Initially, was analyzed the data obtained from the experiment, which included various traits such as KY, KW, PH, EH, KR, KIN, KM, and EW. Bartlett's test was employed to assess the uniformity of error across three different environments. The homogeneity of the experimental error for all traits was confirmed by the results of Bartlett's test, allowing to conduct a combined analysis of variance based on the three environments, as detailed in Table 3. The analysis revealed significant environmental influences on all traits except the KR at a 1% probability level, highlighting the impact of environmental conditions on these traits. Notably, there was considerable genetic diversity among the experimental hybrids concerning all traits, also significant at the 1% probability level. The interaction between hybrid and environment was significant for all traits except for KR, underscoring the variability among genotypes across different conditions and the challenges faced by breeders in selection and release of varieties.

Table 3

Combined analysis of variance of studied traits based on the fourth method of Griffing
Source of variation	df	Mean square
Source of variation	df	Kernel yield	100 kernel weights	Plant height	Ear height	Kernel moisture	Ear wood	Kernel row	Kernel in row
Environment	2	64.56**	79248.89**	15294.62**	23017.71**	499.62**	493.79**	0.52ns	290.13**
Block (Environment)	6	11.29	690.19	1207.00	177.98	8.87	3.36	9.05	48.28
Hybrid	35	8.82**	4099.74**	1467.43**	745.39**	9.04**	17.26**	13.55**	71.87**
GCA	8	12.68**	13998.09**	4588.20**	2427.95**	26.42**	54.22**	43.81**	186.53**
SCA	27	7.67**	1166.90**	542.76**	246.95**	3.89**	6.30**	4.59**	37.90**
Environment × Hybrid	70	3.68**	868.92**	249.81**	99.10**	4.11**	3.22**	1.49ns	24.97**
Environment × GCA	16	6.58**	1456.51**	274.75**	166.22**	6.28**	6.66**	1.52ns	41.37**
Environment × SCA	54	2.82**	694.83**	242.42**	79.22**	3.47**	2.20**	1.48ns	20.11**
Error	210	1.33	326.34	136.90	34.36	2.05	1.07	1.24	11.17
CV (%)		8.20	6.01	5.49	5.26	6.75	6.29	6.51	9.03
Baker ratio		0.77	0.96	0.94	0.95	0.93	0.95	0.95	0.91
, * and ns: Significant at 5 percent, 1 percent and non- significant. GCA: General Combining Ability, SCA: Specific Combining Ability.

The genetic diversity observed in the hybrids from crossing was dissected into additive and non-additive variance components using the fourth Griffing method ⁵⁰. The mean squares for both GCA and SCA for all traits were significant at the 1% probability level. This significance indicates that both additive and non-additive gene effects are crucial in the expression and inheritance of these traits. Previous studies such as those by Vacaro, et al. ⁵³, and Heidari, et al. ⁵⁴, have emphasized the predominance of the additive effects over non-additive effects in controlling quantitative traits of maize, corroborating present findings.

Further analysis divided the hybrid-environment interaction into two components: GCA × environment and SCA × environment. Both interactions were significant for all traits except the KR at the 1% probability level (Table 3), pointing to environmental impacts on both additive and non-additive gene effects. This consistency with earlier studies by Mostafavi, et al. ⁵⁵ and Glover, et al. ⁵⁶ confirms the robustness of these results.

Baker's ratio was used to elucidate the additive and non-additive effects of genes more clearly. For traits where this ratio equals one, control by additive effects is complete. A ratio of 0.5 indicates equal influence of both effects, while a value below 0.5 suggests a dominant role for non-additive effects (dominance, over dominance, and epistasis). In this study, the high values of Baker's ratio for all traits suggest a predominant role of additive effects (Table 3). This finding aligns with other research, such as Saremirad and Mostafavi ²⁷, which utilized Baker's ratio to determine gene effects in controlling various traits.

Figure 1 presents the values of the GCA effects for all studied traits. The GCA for KY varied, with a low of -0.97 in line KE 79017/3211 and a high of 0.42 in line KE 76009/311. Notably, the line KE 76009/311 displayed significant positive GCA for KY at the 5% probability level, identifying it as an optimal general combiner to enhance variance and selection efficiency. Consequently, this line, exhibiting positive GCA, can play a crucial role in boosting KY in selection-based breeding programs. Additionally, this line may increase the presence of genes with additive effects in breeding initiatives. Conversely, lines KE 79017/3211 and KE 77004/1 showed significantly negative impacts on KY at both the 1% and 5% probability levels, thus reducing this trait. For the KW, four lines KE 77008/1, NK79, KE 79017/3211, and KE 76009/311 demonstrated significant positive GCA at both the 1% and 5% probability levels, contributing to increased KW. In terms of PH, lines K 1264/5 − 1, NK79, and KE 75016/321 exhibited positive and significant GCA at the 1% probability level, whereas KE 79017/3211 showed significant negative GCA. For EH, lines KE 75016/321 and K 1264/5 − 1 had positive and significant GCA, while KE 79017/3211 and KE 77004/1 showed negative and significant GCA. A reduced EH can mitigate stem breaking and lodging, thereby preserving yield; using lines with negative and significant GCA can enhance the additive effects of genes and increase selection efficiency. Regarding KM, lines KE 75016/321 and KE 76009/311 showed positive GCA, while line K 1263/1 exhibited negative GCA. In the context of EW, lines KE 76009/311, KE 77004/1, and NK79 demonstrated positive GCA, whereas lines KE 77005/2, K 1264/5 − 1, and K 1263/1 indicated negative GCA at the 1% probability level. Lines K 1264/5 − 1, KE 76009/311, and KE 79017/3211 increased the KR in the ear, while KE 77008/1 and KE 77004/1 decreased them. Conversely, lines K 1263/1 and KE 77004/1 increased, and lines KE 76009/311, NK79, and KE 75016/321 decreased the KIN. Overall, lines K 1264/5 − 1 and K 1263/1 positively impacted the studied traits, while line KE 79017/3211 negatively affected these traits. Given that GCA reflects the additive effects of genes, parents with high GCA also possess substantial additive effects, beneficial for synthesizing new cultivars. Mostafavi, et al. ⁵⁵ noted that lines K166B, K3615/2, and K3653/2 are among the top general combiners for KR, enhancing the genetic improvement of these traits. Another study by Dehghanpour ⁵⁷ examined GCA and SCA in early inbred maize lines using the four Griffin method, concluding that additive effects largely influence trait expression, whereas non-additive effects impact traits such as the KR, leaf length, leaves number, PH, and EH.

Figure 2 details the SCA of hybrids for the traits analyzed. In terms of KY, SCA ranged from − 1.83 in the cross KE 77008/1×KE 76009/311 to 1.63 in KE 76009/311×KE 1264/5 − 1. The crosses K 1264/5 − 1×KE 76009/311, KE 77005/2×KE 75016/321, KE 77004/1×KE 77008/1, and KE 77008/1×KE 79017/3211 exhibited positive and significant SCA, suggesting their utility in enhancing non-additive gene effects for hybrid production. Similar findings were reported by Choukan and Mosavat ⁵⁸ and Mostafavi, et al. ⁵⁵. Dehghanpour and Ehdaie ⁵⁹ found that lines KE75039 and K1264/5 − 1 exhibit high GCA stability for KY, minimally interacting with environmental factors; the cross KE75039×K2331 also demonstrated high SCA stability across environments. The KW varied significantly, from − 26.46 in the cross KE 77005/2×KE 76009/311 to 14.86 in KE 77005/2×K 1263/1, yet none of the hybrids showed significant positive SCA for this trait. Regarding PH, hybrids NK79×KE 77005/2 and KE 75016/321×KE 77004/1 showed positive and significant SCA at the 5% probability level, while K 1263/1×KE 75016/321, KE 76009/311×KE 77008/1, and KE 75016/321×KE 79017/3211 had negative SCA. For EH, only the crosses KE 77008/1×KE 77005/2 and KE 75016/321×KE 79017/3211 demonstrated negative and significant SCA. KM changes ranged from − 1.17 in the cross K 1264/5 − 1 NK79 to 1.19 in KE 77005/2×KE 79017/3211, though no significant crosses were noted. For EW, hybrids KE 77008/1×KE 76009/311 and KE 77008/1×KE 77005/2 exhibited positive and significant SCA. Hybrids K 1263/1×KE 77005/2, K 1264/5 − 1×KE 76009/311, and NK79×KE 77008/1 increased the KR, while KE 77005/2×KE 76009/311, KE 77005/2×KE 77008/1, K 1264/5 − 1×KE 79017/3211, and K 1263/1×NK79 increased the KIN. Research on maize has consistently underscored the importance of both GCA and SCA in trait control ^55,58. Dehghanpour ⁶⁰ highlighted that lines OH43/1–42, KE75039, and K1263/1 possess good GCA, which is beneficial for hybrid cultivar preparation. However, for producing single crosses, combinations such as KE75039×K1263/2 − 1, K1263/1×KE72012/1, K1263/1×K2331, and OH43/1/42×K1263/2 − 1, which exhibit considerable SCA, should be considered.

Exploration of correlation and graphical analysis of genotype by yield*trait

Figure 3 presents the results of Pearson's correlation analysis on various traits of experimental maize genotypes. The most notable positive and significant correlation, quantified at 0.73, was observed between PH and EH at a 1% probability level. This strong correlation indicates that an increase in PH is associated with an increase in EH. Other correlations were identified as follows: KM and EH (r = 0.57), KY and KW (r = 0.35), EW and KW (r = 0.33), KY and PH (r = 0.23), KM and KW (r = 0.15), EH and KR (r = 0.12), and KY and KR (r = 0.12). These correlations range from moderate to weak, yet are significant at the 1% and 5% probability levels. A significant negative correlation was most prominently observed between EW and EH (r=-0.57) at a 1% probability level, indicating that an increase in EW is associated with a decrease in EH. Additional correlations included KM and EH (r=-0.49), KW and KIN (r=-0.37), EW and PH (r=-0.30), KM and PH (r=-0.29), EW and KIN (r=-0.24), KM and KIN (r=-0.22), and EH and KW (r=-0.12). These correlations were moderate to weak and significant at the 1% and 5% probability levels. Previous studies, such as those by Akbari, et al. ⁶¹ and Nazeran, et al. ⁶², have also documented similar associations between KY and its components. These complex associations pose challenges in breeding programs. Breeders are thus encouraged to strike a balance while managing the negative correlations among key traits to enhance breeding outcomes, as discussed by Hassani, et al. ². In this context, graphical methods such as GT and GYT biplots analyses have proven to be comprehensive and effective tools ^2,47.

To enhance crop productivity, was conducted a GYT graphical analysis. the findings indicate that the first and second PCs account for 64.54% and 14.38% of the variance, respectively, summing up to 78.92% of the total variance in KY data. This high percentage of variance explained by the first two PCs validates the biplot diagrams' effectiveness in illustrating GYT relationships, as per Cruz, et al. ⁶³. While a complex dataset may not be fully explained by the first two PCs ⁶⁴, the biplot's utility is not negated ⁶⁵. Shojaei, et al. ⁶⁶ demonstrated that approximately 50% of the variation in GYT is captured by the first two PCs. Similarly, Faheem, et al. ⁴⁸ found that the first two PCs account for nearly 85% of the variance, with the PC1 contributing about 74% and the PC2 about 11%. Hassani, et al. ² study revealed that the first and second PCs explain 50.53% and 34.96% of the variance, respectively, totaling 85.49% of the yield data variations.

Analyzing the correlation between yield-trait combinations can reveal trait relationships, aiding in the development of new genotypes. Figure 4A illustrates that the acute angles between most vectors indicate a positive correlation among the yield-trait combinations. This positive correlation is attributed to yield being a primary component in these combinations ⁴⁷. A high correlation between yield-trait combinations implies a high correlation between genotype rankings for those combinations. The graph shows no correlation between the KY*KIN and KY*KW combinations. However, there is a weak positive correlation between KY*KIN and combinations such as KY*PH, KY*KR, and KY*EH. Positive correlations are also observed between KY*KR and KY*EH, KY/EW and KY/KM, and KY*PH height with both KY*KR and KY*EH. These correlations suggest that increasing PH in a genotype may also enhance the KR, EH, and EW, while potentially reducing KM content. However, the correlation between KY*KIN and some traits complicates the breeding process, necessitating strategic planning.

In biplot of Fig. 4B, a polygon formed by connecting genotypes 18, 4, 12, 31, 23, 27, 36, and 20, which are furthest from the origin, is evident. Perpendicular lines drawn from the origin to the polygon sides facilitate the grouping of GYT combinations. Genotypes at the vertices of the polygon exhibit superior performance for the respective combinations, indicating their superiority in terms of those traits. Consequently, genotype 4 was identified as the best for the KY*KIN, while genotypes 12 and 31 excelled in other combinations such as KY/EW, KY/KM, KY*PH, KY*KR, KY*EH, and KY*KW. Genotypes not associated with any combination are considered less desirable and weaker. The polygonal biplot allowed us to categorize the yield-trait combinations into two groups: the first group includes KY*KIN, while the second group encompasses the remaining combinations, such as KY/EW, KY/KM, KY*PH, KY*KR, KY*EH, and KY*KW.

To evaluate the ranking of genotypes based on yield-trait combinations, we utilized the biplot diagram of average tester coordinates (Fig. 4C) and the superiority index (Table 4). In the biplot of tester coordinates, the average axis, represented by an arrow passing through the origin, indicates the mean of all yield-trait combinations. The axis perpendicular to the average axis measures the balance of genotypes across traits. Based on this, genotype 12 and then genotype 31 had the highest average KY and were identified as the best genotypes. In contrast, genotypes 36, 35, 34, and 21 had the lowest average KY. The results from the biplot of average tester coordinates were further confirmed by the superiority index. Various studies ^2,47,48,66 and the findings of the present study demonstrate that the biplot diagram of average tester coordinates in the GYT biplot method is a valuable tool that provides insightful information about genotypes. Identifying the hypothetical ideal genotype is based on the concepts of balance and high KY. The desired genotype is one that exhibits the highest KY and maximum balance. Any genotype that is closest to this hypothetical ideal genotype is considered superior, while the genotype furthest from it is deemed the least favorable. Based on this biplot (Fig. 4D), genotypes 12, 11, 19, 9, and 2 were identified as the best genotypes due to their minimal distance from the hypothetical ideal genotype. Conversely, genotypes 36, 27, 35, 34, and 21 were named as undesirable genotypes because they had the greatest distance from the hypothetical ideal genotype (Fig. 4D). Hassani, et al. ² employed the GYT graphic method to determine trait relationships and select superior genotypes, obtaining valuable results.

Table 4

Standardized genotype by kernel yield*trait data and superiority index for maize genotypes
Genotype	KY×KR	KY×KIN	KY×KW	KY×PH	KY×EH	KY/EW	KY/KM	Mean superiority index
1	-0.75	0.53	-1.28	-0.28	0.05	0.57	0.02	-0.16
2	1.24	0.91	0.37	0.60	0.46	1.55	1.41	0.93
3	0.78	2.28	1.11	1.07	0.74	0.79	1.59	1.19
4	-0.55	1.67	0.73	0.94	1.37	1.78	1.86	1.12
5	-0.05	-0.22	-1.06	-0.27	0.26	-0.54	-0.51	-0.34
6	0.25	0.77	-0.67	-0.28	0.65	-0.27	-0.16	0.04
7	-0.13	1.55	-0.74	-0.11	-0.64	-0.32	0.33	-0.01
8	-0.03	-0.19	-0.77	-1.38	-1.11	-0.05	-0.16	-0.53
9	1.36	0.18	0.24	0.70	0.68	2.18	1.47	0.97
10	0.90	-0.70	0.08	0.89	0.84	0.08	1.17	0.47
11	0.05	0.38	0.82	1.24	1.34	1.57	1.28	0.95
12	3.17	1.37	1.55	2.44	2.10	1.65	1.32	1.94
13	-0.34	-1.34	-0.45	0.60	1.05	-0.15	-1.50	-0.30
14	0.10	0.01	-0.97	0.40	-0.57	-0.54	-0.07	-0.23
15	0.65	0.05	-0.75	-0.73	-0.77	-0.27	-0.22	-0.29
16	-0.22	-0.93	-0.12	0.52	0.20	-0.36	0.08	-0.12
17	-0.42	0.79	0.15	-0.60	-0.80	-0.07	-0.19	-0.16
18	0.04	1.39	-0.56	0.07	-0.49	0.72	0.68	0.26
19	0.93	0.72	0.50	0.32	1.19	1.63	0.21	0.79
20	-1.29	0.47	-2.03	-0.68	-0.86	-0.62	-0.52	-0.79
21	-0.87	-1.67	-1.02	-2.04	-1.52	-0.01	-1.72	-1.27
22	-0.34	-0.56	1.14	-0.08	0.08	-0.28	0.46	0.06
23	0.75	-0.76	1.49	0.68	0.71	-1.24	0.00	0.23
24	-0.34	-0.54	0.47	0.58	0.64	-0.81	-0.47	-0.07
25	-0.50	0.48	-0.04	0.22	-0.58	-0.74	0.18	-0.14
26	0.02	-0.08	1.36	-0.37	-0.61	-0.04	-0.27	0.00
27	-0.71	-1.82	0.38	-1.24	-1.08	-1.87	-1.73	-1.15
28	-1.03	-1.04	0.30	0.57	0.57	0.09	-0.65	-0.17
29	-0.80	0.72	0.74	0.65	0.08	0.30	1.10	0.40
30	-0.53	-0.71	1.38	-0.55	-0.65	-0.16	0.12	-0.16
31	2.06	0.57	1.73	1.82	2.21	0.63	0.61	1.37
32	-0.49	-0.27	-0.27	-0.71	-1.04	-1.37	-0.81	-0.71
33	1.08	-1.36	0.59	-0.72	-0.51	0.08	-0.23	-0.15
34	-1.68	-0.66	-1.63	-0.55	-0.77	-1.67	-1.72	-1.24
35	-0.62	-1.22	-1.44	-1.83	-1.44	-1.06	-1.33	-1.28
36	-1.66	-0.76	-1.33	-1.92	-1.75	-1.18	-1.61	-1.46
Mean	0.00	0.00	0.00	0.00	0.00	0.00	0.00	-
Standard deviation	1.00	1.00	1.00	1.00	1.00	1.00	1.00	-
KY: Kernel yield, KW: 1000 kernel weight, PH: Plant height, EH: ear height, KR: Kernel row number, KIN: Kernels in row, KM: Kernel moisture, EW: Ear wood.

Multi-trait stability index

The factor analysis was performed based on PCA, and the results were interpreted after varimax rotation. Table 5 presents the outcomes of the factor analysis. Factors with eigenvalues greater than one were selected, and their variance, expressed as a percentage, indicates their importance in explaining the overall data variability. In this analysis, three independent factors accounted for a total of 65.89% of the data variations. The first factor explained 33.11% of the total variance and had an eigenvalue of 2.65. This factor had large negative factor coefficients for KY, KR, PH, and EH. The second factor, with an eigenvalue of 1.55, accounted for 19.34% of the variations and included height positive factor coefficients for the KIN and EW, along with a negative coefficient for the KW. The third factor, with an eigenvalue of 1.08, explained 13.44% of the total data variance and had a height negative factor coefficient for KM. The MTSI of the studied genotypes was calculated based on the factor scores of the three mentioned factors. According to this index, a lower value indicates a genotype that is closer to the stable ideal genotype. Conversely, a higher MTSI value for a genotype suggests a greater distance from the ideal stable genotype, making it less desirable for selection. In Fig. 5A, the experimental genotypes were ranked from the highest to the lowest MTSI value. The genotype with the highest index value is placed at the center, while the genotype with the lowest value is positioned at the outermost circle. Applying a selection pressure of 25%, genotype 10 ranked first, followed by genotypes 9, 13, 11, 1, 2, and 16, which were identified as the most ideal stable genotypes in terms of all traits. Comparing the trait values of the selected genotypes based on the MTSI with those of all experimental genotypes revealed that the average values of KY, KR, PH, and EH increased in the selected genotypes. However, the average values of the KIN, KW, EW, and KM decreased. Increases in KY, KR, PH, and EH were considered favorable, but decreases in the KIN and KW are not among the goals of maize breeding programs. Reducing EW and KM is desirable, and the selected genotypes showed a significant reduction in these traits.

Table 5

Prediction of selection differential for studied traits based on MTSI index
Variable	FA1	FA2	FA3	Communality	Uniqueness’s	Goal	h²	SD (%)	SG (%)
KY	-0.59	0.15	0.39	0.52	0.48	increase	0.58	0.73	0.43
KR	-0.56	-0.04	-0.13	0.33	0.67	increase	0.89	2.74	2.44
KIN	0.42	0.81	0.06	0.83	0.17	increase	0.65	-2.14	-1.40
KW	-0.11	-0.85	-0.03	0.74	0.26	increase	0.79	-1.38	-1.09
PH	-0.84	-0.23	-0.08	0.77	0.23	increase	0.83	6.06	5.02
EH	-0.80	-0.10	0.18	0.68	0.32	increase	0.87	7.78	6.74
EW	-0.19	0.67	-0.31	0.58	0.42	decrease	0.81	-8.34	-6.78
KM	0.01	0.12	-0.90	0.82	0.18	decrease	0.54	-4.04	-2.20
Eig.	2.65	1.55	1.08	-	-	-	-	-	-
Var. (%)	33.11	19.34	13.44	-	-	-	-	-	-
Cum. Var. (%)	33.11	52.45	65.89	-	-	-	-	-	-
KY: Kernel yield, KW: 1000 kernel weight, PH: Plant height, EH: ear height, KR: Kernel row number, KIN: Kernels in row, KM: Kernel moisture, EW: Ear wood, Eig: Eigen value, Var: Variance, Cum. Var: Cumulative variance.

Overall, the selected genotypes resulted in selection differential and favorable selection progress in all traits except the KIN and the KW (Table 5). In the selected genotypes, all traits except KM and KY had high heritability. Figure 5B illustrates the strengths and weaknesses of the selected genotypes based on the contribution of each factor to the MTSI. According to this diagram, a lower contribution by a factor (closer to the outer edge) indicates that the traits within that factor are closer to the stable ideal state. The dashed line represents the theoretical value if all factors play an equal role. Given that each genotype is closer to the ideal genotype for factors where it has a smaller contribution in terms of traits within that factor, genotypes 13, 10, and 1, which had the lowest value in the first factor, are close to the ideal genotype for KY, KR, PH, and EH, which had the highest factor coefficients in this factor. Genotypes 11, 2, and 9 had the lowest contribution of the second factor, making them very close to the ideal genotype in terms of the KIN, EW, and the KW. Four genotypes- 10, 16, 11, and 9- had the largest share of the third factor, indicating they are very close to the ideal genotype in terms of KM. In other words, these genotypes have low KM. Sharifi, et al. ⁶⁷ used the MTSI to evaluate yield and other agronomic traits in a set of rice genotypes and showed that this index effectively identifies superior genotypes in terms of yield stability and other agronomic traits. Based on the results of the MTSI index, Rajabi, et al. ¹⁵ introduced five genotypes as stable genotypes under conditions infected with rhizomania disease. Taleghani, et al. ⁶⁸ identified four ideal genotypes simultaneously in terms of different traits using the MTSI index. These results are consistent with the findings obtained from this study regarding the efficiency of the MTSI index in identifying superior genotypes.

In general, the present study successfully identified superior maize genotypes through a comprehensive evaluation of their GCA and SCA, as well as the interplay between key agronomic traits and yield. The diallel analysis revealed that both additive and non-additive gene effects contribute to the inheritance of important traits, with additive effects playing a predominant role. Line KE 79017/3211 emerged as the top performer in terms of GCA for KY, while several hybrids (K 1264/5 − 1×KE 76009/311, KE 77005/2×KE 75016/321, KE 77008/1×KE 77004/1, and KE 77008/1×KE 79017/3211) showed significant SCA for KY, indicating their potential value in breeding programs. The analysis of trait combinations highlighted the complex interrelationships between KY and other agronomic traits, with some showing positive correlations that could be exploited in breeding strategies. The identification of genotypes 12 and 31 as the best performers across multiple trait combinations, along with the ranking of genotype 10 and others based on the MTSI, provides a robust set of candidates for further improvement and deployment in yield enhancement programs. Overall, this study not only contributes to the understanding of the genetic basis of yield in maize but also offers practical insights for breeders to select and develop superior genotypes that can meet the challenges of modern agriculture. The findings underscore the importance of a multi-faceted approach to breeding, considering both the genetic potential of individual lines and the synergistic effects of key traits on yield.

Competing interests

The authors have stated that they do not have any conflicting interests.

Ethics declarations

Not applicable.

Author Contribution

SMSH: Performed trials, analyzed the datasets, and wrote the manuscript, MRS: Assistance in data analysis and writing the manuscript, KM: Assistance in revising the manuscript, AM and SMM: Assistance in data analysis and writing the manuscript. All authors contributed to revising and editing the manuscript. All authors have read and approved the final manuscript.

Acknowledgement

The authors express their gratitude to the technicians at the Seed and Plant Improvement Institute (SPII), Karaj, Iran, for their assistance in conducting the experiments.

Data Availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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No competing interests reported.

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Genetic analysis and association detection of agronomic traits in maize genotypes

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Version 1

Abstract

Figures

Introduction

Materials and methods

Plant material

Field location and data collection

Statistical analysis

Results and discussion

Genetic analysis

Exploration of correlation and graphical analysis of genotype by yield*trait

Multi-trait stability index

Conclusion

Declarations

Competing interests

Ethics declarations

Author Contribution

Acknowledgement

Data Availability

References

Additional Declarations

Status:

Version 1