Correlation and selection gains in F6 wheat genotypes¹

ABSTRACT

The agronomic traits have complex quantitative inheritance, wheat breeding has a highly cost and time expending to achieve advanced lines, and new cultivars. The objective of this work was to estimate the phenotypic (r_p), genotypic (r_g) and environmental (r_e) correlation coefficients and the genetic parameters, as well as to estimate the selection gains between agronomic traits in wheat using multivariate analysis. F₆ generation was carried out in a randomized block experimental design, with fifteen wheat genotypes arranged in three replications. The traits measured were days of emergence to flowering, plant height, number of tillers, spike length, number of spikelets, kernel weight and thousand kernel weight. Correlation coefficients, variance components, genetic parameters, selection gain, Euclidiana’s distance, relative contribution of traits and canonical variables were estimated. Selection based on number of tillers, kernel weight and thousand kernel weight may result in expressive selection gains. The number of fertile tillers per plant showed a significant and positive intermediate association with kernel weight and positive correlation with number of spikelets. The cycle showed greater contribution to the genetic divergence among genotypes studied. Distinct groups highlighted the genetic variability among genotypes.

Keywords
Advanced generation; genetic divergence; Triticum aestivum L.

INTRODUCTION

Wheat (Triticum aestivum L.) is the most important cereal in the world, being a major commodity in the international grain market. It is used as a source of human and animal food (Ali et al., 2016). Crop has been extensively searched in recent decades, with breeders' focus on increasing productivity. But as the agronomic traits have complex quantitative inheritance, thus, wheat breeding has a highly cost and time expending to achieve advanced lines, and new cultivars (Ni et al., 2017).

The increase in wheat productivity is the result of two elements: improvement of the growing environment and the genetic gain (Xiao & Tao, 2014). Environmental improvement through agronomic practices is to provide the crop with an environment more favorable to the best development, providing adequate water supply, adequate fertility, and elimination of other biotic and abiotic stresses (Laidig et al., 2017). Genetic gain refers to the increase in performance that is achieved through plant breeding, in this context, a new breeding program should increase grain yield. Several authors have been reporting the genetic gain in wheat worldwide, in Brazil (Woyann et al., 2019), Argentina (Lo Valvo et al., 2018) and China (Yao et al., 2012).

In plant breeding programs, the study of linear relationships among traits may provide important results, especially in identifying traits to be used in indirect selection (Falconer & Mackay, 1996). The correlation coefficient (r) measures the intensity and sense of linear association between two random variables (Galton, 1888; Pearson, 1920) and has been used in association studies among traits in plant breeding trials (Olivoto et al., 2017). Knowledge of the phenotypic, genetic, and environmental correlation among traits is essential for simultaneous selection of several traits, also it is important when target trait reveals low heritability or is difficult to measure (Falconer & Mackay, 1996).

Selection of superior genotypes is easily proceeded when there are large differences in phenotypic expression, thus grouping analysis aims to gather classification criteria, so that there is high homogeneity within the group and heterogeneity between groups, according to criteria of similarity or dissimilarity (Cruz et al., 2004). To select potentially high-yielding wheat lines in breeding programs, superior genotypes need to be selected for with greater accuracy.

The following hypotheses were formulated: (i)the selection gains in advanced generations are satisfactory for wheat and (ii) genetic deviations can be used to select superior genotypes. In this way, the objective of this work was to estimate the phenotypic (r_p), genotypic (r_g) and environment (r_e) correlation coefficients and genetic parameters; as well as to estimate the selection gains between agronomic traits in wheat using multivariate statistics.

MATERIAL AND METHODS

The study was performed in 2018 crop season, in Frederico Westphalen (27°23’26’’S, 53°25’43’’W, 481 m above the sea level). The soil was classified as ferric aluminum Hapludox type and the climate as Cfa according to Köppen (Alvares et al., 2013).

Wheat genotypes in F6 generation developed from single crosses

were evaluated: Mirante/Quartzo (UFSMFW1 01), Mirante/Fundacep Horizonte (UFSMFW 1 02), Supera/Quartzo (UFSMFW1 03), Supera/Quartzo (UFSMFW1 04), BRS 327/Fundacep Horizonte (UFSMFW1 05), Supera/Abalone (UFSMFW1 06), BRS 327/Fundacep Horizonte (UFSMFW1 07), Supera/Abalone (UFSMFW1 08), BRS 327/Fundacep Horizonte (UFSMFW1 09), Supera/Fundacep Horizonte (UFSMFW1 10), Mirante, Supera, Pampeano, Quartzo e TBIO Toruk. The last five cultivars were used in function of the variability among them. To obtain these lines, the crosses were realized in greenhouse and generation advance was performed using Single Seed Descent method (SSD). The experimental design was randomized complete blocks, with three replicates. The plots were composed by four rows 4-m long, spaced by 0.17m, in a density of 350 plants m^-2. The sowing was performed on Jun 12^sd 2018, in the no-tillage system; and pest, disease and weed control was done preventively.

The traits measured were: a) days of emergence to flowering (DF); b) plant height (PH, cm), measured from the soil to spike of main tiller; c) number of tillers (NT), obtained by count of fertile tillers; d) spike length (SL, cm), considering spike insertion point to start of awns; e) number of spikelets (NS), obtained by counting of spikelets of main tiller spike; f) kernel weight (KW, g) obtained by weight of main tiller spike; g) thousand kernel weight (TK, g). Except for days from emergence to flowering, the other traits were obtained at the time of harvest.

The meteorological data were obtained from the Instituto Nacional de Meteorologia (INMET), referring to automatic weather station of Frederico Westphalen - RS (A854), located about 350 m from experiment, for cultivation period of Jun 12^th, 2018, to Oct 31^st, 2018. For that, instantaneous, maximum, and minimum air temperature recorded every hour was used, obtaining average for instantaneous, maximum, and minimum temperature recorded for day. And for daily precipitation was carried out accumulation on a day.

Data were submitted to analysis of variance carried out at 5% significance level of test using the statistical model: $\mathrm{Yij}=\mathrm{\mu }+{\mathrm{g}}_{\mathrm{i}}+{\mathrm{b}}_{\mathrm{j}}+{\mathrm{\epsilon }}_{\mathrm{ij}}$, where µ is the mean, g_i is the effect of i^th genotype, b_j is the effect of j^th block, ε_ij is random error. Assuming treatments effects as random, the accuracy, was estimated (Resende & Duarte, 2007): $\hat{\mathrm{r}}=\left[\frac{1}{1+({\mathrm{\sigma }}_{\mathrm{e}}^2/\mathrm{b})/{\mathrm{\sigma }}_{\mathrm{g}}^2}\right]1/2$, where: b is number of replicates or blocks.

For genotypic, phenotypic, and environmental correlations between two traits (x and y) were estimated by analysis of variance. For it, the mean square associated with treatments (PMT) and residual (PMR) according the formulas: ${\mathrm{PMT}}_{\mathrm{xy}}=\frac{{\mathrm{MST}}_{\mathrm{x}+\mathrm{y}}-{\mathrm{MST}}_{\mathrm{x}}-{\mathrm{MST}}_{\mathrm{y}}}{2}$ and ${\mathrm{PMR}}_{\mathrm{xy}}=\frac{{\mathrm{MSR}}_{\mathrm{x}+\mathrm{y}}-{\mathrm{MSR}}_{\mathrm{x}}-{\mathrm{MSR}}_{\mathrm{y}}}{2}$, phenotypic correlation: ${\mathrm{r}}_{\mathrm{p}}=\frac{{\mathrm{PMT}}_{\mathrm{xy}}}{\sqrt{{\mathrm{MST}}_{\mathrm{x}} {\mathrm{MST}}_{\mathrm{y}}}}$, environmental correlation: ${\mathrm{r}}_{\mathrm{a}}=\frac{{\mathrm{PMR}}_{\mathrm{xy}}}{\sqrt{{{\displaystyle \mathrm{MSR}}}_{\mathrm{x}}{\displaystyle }{{\displaystyle \mathrm{MSR}}}_{\mathrm{y}}}}$ and genotypic correlation: ${\mathrm{r}}_{\mathrm{g}}=\frac{{\hat{\mathrm{\sigma }}}_{\mathrm{gxy}}}{\sqrt{{\hat{\mathrm{\sigma }}}_{\mathrm{gx}}^2{\displaystyle }{\hat{\mathrm{\sigma }}}_{\mathrm{gy}}^2}}$, being ${\hat{\mathrm{\sigma }}}_{\mathrm{gxy}}=\frac{{\mathrm{PMT}}_{\mathrm{xy}}-{\mathrm{PMR}}_{\mathrm{xy}}}{\mathrm{r}}$, where MQT is mean square of treatment, MSR is mean square of residue, ${\hat{\mathrm{\sigma }}}_{\mathrm{gxy}}$ is genotypic covariance estimator, ${{\hat{\mathrm{\sigma }}}^2}_{gx}$ and ${{\hat{\mathrm{\sigma }}}^2}_{gx}$ are the genotypic variance of X and Y, respectively.

The significance test of the phenotypic and environmental correlations, the t test was used as described by Steel & Torrie (1980) according to equation: $\mathrm{t}=\frac{\mathrm{r}}{\sqrt{1-{\mathrm{r}}^2}}\sqrt{\mathrm{n}-2}$

Genotypic correlations were submitted to bootstrap analysis with five thousand (5000) simulations to identify correlations with significance.

Selection gains were estimated through heritability and the proportion of the additive variance explored (Cruz et al., 2004), and it was estimated by: $\mathrm{GS}=\mathrm{ip}{\hat{\mathrm{\sigma }}}_{\mathrm{g}}$ where i is the selection intensity; p parental control; ${\hat{\mathrm{\sigma }}}_{\mathrm{g}}$ genetic standard deviation; h square of heritability, for the selection unit considered.

The relative contribution of each trait was estimated by Singh (1981) methodology. After, for canonical variables analysis, only traits with relative contribution up to 10% were used. Statistical software Genes (Cruz, 2016) and R (R Development Core Team, 2016) was used to perform the analysis.

To study genetic diversity of populations genetic distance matrix was estimated by Euclidiana’s distance, and clustering analysis was done using the UPGMA (Unweighted Pair Group Method with Arithmetic Averages), and it was represented on a dendrogram. The cut level in the dendrogram, which separates populations into distinct groups, was determined by the Mojena’s method (Mojena, 1977), and adjusted by cophenetic correlation. The clustes analysis were performed using “HDMD” (McFerrin, 2013) and “cluster” (Maechler et al., 2018) package in R (R Development Core Team, 2016).

RESULTS AND DISCUSSION

The analysis of variance (Table 1) showed significant difference for days of emergence to flowering, plant height, number of tillers and kernel weight by F test (p < 0.05) genotypes. The data presented low accuracy (spike length), moderate (number of tillers) and high magnitudes (days of emergence to flowering, plant height, number of tillers, kernel weight, thousand kernel weight) (Resende & Duarte, 2007). The genotypes studied presented greater phenotypic variance for the plant height and days of emergence to flowering, and it is worth to mentioning that the environment had large influence (Table 2). It stands out the plant height traits, which revealed higher values of variance being the most influenced by environment.

Table 1
Summary of variance analysis mean (x) and selective accuracy (${\hat{r}}_{gg}$) for days of emergence to flowering (DF), plant height (PH), spike length (SL), number of tillers (NT), number of spikelets (NS), kernel weight (KW), thousand kernel weight (TK)

Source of variation		Mean square
	Degrees of freedom	DF	PH	SL	NT	NS	KW	TK
Block	2	3.75	62.15	1.75	0.28	1.68	0.05	0.90
Genotypes	14	34.92*	364.03*	1.80	2.4	2.56*	0.09*	1.32
Residue	28	0.08	33.67	1.46	1.5	0.73	0.03	0.48
Total	44
X		75.57	68.77	8.15	2.75	13.04	1.03	33,02
r gg		0,98	0.95	0.40	0.64	0.88	0.87	0.66

• ^*
  Significative to F test (p < 0.05).

Table 2
Estimates of phenotypic variance (${\hat{\mathrm{\sigma }}}_{\mathrm{p}}^2$), genetic variance (${\hat{\sigma }}_{\mathrm{g}}^2$), environmental variance (${\hat{\sigma }}_{\mathrm{e}}^2$), heritability (h²), genetic gain (Gs), for days of emergence to flowering (DF), plant height (PH), spike length (SL), number of tillers (NT), number of spikelets (NS), kernel weight (KW), thousand kernel weight (TK)

Estimates	DF	PH	SL	NT	NS	KW	TK
${\hat{\mathrm{\sigma }}}_{\mathrm{p}}^2$	11.64	121.34	0.60	0.80	0.85	0.03	0.44
${\hat{\sigma }}_{\mathrm{g}}^2$	11.61	110.11	0.11	0.30	0.60	0.02	0.17
${\hat{\sigma }}_{\mathrm{e}}^2$	0.03	11.23	0.49	0.50	0.25	0.01	0.26
h2 (%)	99.74	90.74	18.48	37.45	71.28	73.15	40.56
Gs (%)	4.91	15.84	1.91	13.25	5.50	13.64	40.56

Regarding the heritability, the higher estimates were observed for days of emergence to flowering (0.99), plant height (0.91), number of tillers (0.71) and kernel weight (0.73). Other researchers have obtained high heritability values for heading (0.94), plant height (0.93) and kernel weight (0.87) (Wiersma et al., 2001). For plant height (0.97) and number of tillers per plant (0.95) (Mangi et al., 2010). For days to 75% heading/flowering (0.87) and plant height (0.85) (Kefale & Menzir, 2019).

The results reveal a favorable condition for genetic gains with selection through these traits. Thus, these high heritability estimates indicated genetic control over evaluated traits. Therefore, it is possible to infer that traits studied in this set of genotypes may be used to evaluate genetic variability and they are efficient for prediction of selection.

The highest selection gains of wheat genotypes were evidenced for thousand kernel weight (40.56%), plant height (15.84%), number of tillers (13.25%), kernel weight (13.64%), followed by days of emergence to flowering (4.91%), spike length (1.91%) and number of tillers (5.50%) that had low estimates of selection gain among the genotypes. Therefore, selection for number of tillers and thousand kernel weight may increase selection gains since these traits response to more easily to selection due to high heritability coefficients. Fellahi et al. (2018), obtained selection gains of 27.14% for thousand kernel weight and 12.98% for plant height. Meira et al. (2019), in the study of black oat populations the selection strategy is to reduce variability within families and to increase variability among families. This is to combine selection between and within populations to achieve high efficiency in the selection gain.

Around 90% of the correlations showed higher genotypic coefficient than phenotypic (Table 3). These results favor the selection strategy for breeding programs, since phenotypic correlations have genetic and environmental interactions, but only associations of a genetic nature are inheritable.

Table 3
Estimates of phenotypic (r_p), genotypic (r_g) and environmental (r_e) correlation between agronomic traits, for days of emergence to flowering (DF), plant height (PH), spike length (SL), number of tillers (NT), number of spikelets (NS), kernel weight (KW), thousand kernel weight (TK) for wheat genotypes

Trait		PH	SL	NT	NS	KW	TK
DF	rp	0.31*	0.08	-0.55*	-0.07	0.35	0.28
	rg	0.46	0.45	-0.59++	-0.42	0.51	0.46
	re	-0.23	-0.24	0.18	0.34*	-0.26	0.15
PH	rp	-	0.34	-0.30	0.32	0.48	0.47
	rg	-	0.55	-0.49++	0.49	0.60	0.57
	re	-	0.37*	-0.16	-0.24	-0.19	-0.01
SL	rp		-	0.04	0.03	0.04	-0.24
	rg		-	0.43++	0.46	0.49	-0.44++
	re		-	-0.28	-0.22	-0.29	0.04
NT	rp			-	0.18	0.35	0.13
	rg			-	0.46	0.52++	0.39++
	re			-	0.20	0.45*	-0.19
NS	rp				-	0.57*	-0.11
	rg				-	0.63	-0.48
	re				-	0.14	-0.01
KW	rp					-	0.17
	rg					-	0.48
	re					-	-0.37

• Genotypic correlation index “⁺” and “⁺⁺” significative to t test (p < 0.05 and p < 0.01). Phenotypic and environmental correlation index, “*” and “**” significative to t test (p < 0.05 and p < 0.01).

Spike length showed a significant and positive genetic correlation (0.43) with kernel weight and negative (-0.44) with thousand kernel weight. When a genotype has a long spike, it will usually produce a higher kernel weight and consequently will have a reduction in thousand kernel weight. Following this trend, Ahmad et al. (2010) found a positive genetic correlation (0.87 and 0.97) between spike length and kernel weight, studying two different populations. Mohammadi et al. (2011), found correlation (-0.32) between spike length and thousand kernel weight, in researches with durum wheat.

The number of tillers showed a significant and positive genetic correlation (0.52) with the kernel weight, this correlation is expected, since as we have a greater number of tillers, we have a greater number of ears and, consequently, greater weight of grains per plant. Elhani et al. (2007), verified simple correlations ranging from 0.42 to 0.91 between number of tillers and kernel weight, under different management conditions.The results indicated that at the genotypic level there was a positive correlation between number of tillers and kernel weight (0.63) (Table 3). The increased number of spikelets per spike is an efficient strategy to improve grain yield of wheat, since this trait is associated to the increase in the number of grains per spike, considered an important wheat yield component (Fioreze & Rodrigues, 2012).

The negative correlation coefficients were observed between days of emergence to flowering and number of tillers (-0.55). In wheat, the shorter the vegetative period of a genotype, the lower its tillering potential. Compensatory potential of wheat tilling is modified according to development cycle and determine the number of spikes per plant (Carvalho et al., 2015). In this way, photosynthetically active tillers tend to increase grain yield (Fioreze & Rodrigues, 2012).

The plant height showed a strong correlation with kernel weight (0.60), this association suggests that tall plants had greater accumulation of photosynthates for the grains, with a longer reproductive period resulting in higher grain weight (Khan et al., 2013; Rigatti et al., 2019).

The number of fertile tillers per plant showed a significant and positive intermediate association with kernel weight (0.52) and positive correlation with number of tillers (0.46) for genotypic correlation indicating that this trait may be used in indirect selection aiming for to increment grain yield in wheat.

Spike length showed a significant negative genotypic association with thousand kernel weight (-0.442), indicating that larger spikes tend to increase yield. In this way, these traits are important to be considered in the cereals breeding, due to being closely related to the yield components and define wheat productivity (Silva et al., 2010).

For canonical variables, two variables explained more than 90% of the genetic variation among the genotypes (Table 4).

Table 4
Variance (eigenvalues), percentage variance and accumulated variance of canonical variance, aiming to estimate the dissimilarity between ten wheat genotypes, for days of emergence to flowering (DF), plant height (PH), spike length (SL), number of tillers (NT), number of spikelets (NS), kernel weight (KW), thousand kernel weight (TK)

Trait	Variance (eigenvalues)	Percentage variance (%)	Accumulated variances (%)
DF	63.16	89.06	89.06
PH	4.98	7.02	96.08
SL	1.27	1.79	97.87
NT	0.70	0.99	98.86
NS	0.43	0.61	99.47
KW	0.26	0.36	99.84
TK	0.12	0.16	100.00

In this way, a two-dimensional graph was used to represent canonical variables (Figure 1). There is large distance among the wheat genotypes, emphasizing the genotypes Pampeano, UFSMFW1 14, UFSMFW1 03 and UFSMFW1 07 (Figure 1). Based on the graphic dispersion of the populations it is possible to direct he crosses between divergent parents to the desired traits, to promote genetic recombination. In this case, hybridizations could be programmed, for example, between genotypes Pampeano with UFSMFW1 06, TBIO Toruk with Pampeano, UFSMFW1 01 with UFSMFW1 08.

Figure 1
Graphic dispersion of first score (canonical variable 1) and second score (canonical variable 2) for wheat genotypes.

Regarding the relative contribution of traits Singh (1981), days of emergence to flowering expressed the largest contribution (81%) to genetic divergence among genotypes (Table 5). Showing the important genetic variability between the genotypes for the vegetative cycle. The traits kernel weight, number of tillers and number of tillers contributed with 5%, 2% and 2%, respectively, such results agree with Gurjar & Marker (2018).

Table 5
Relative contribution of traits to divergence to Singh method for days of emergence to flowering (DF), plant height (PH), spike length (SL), number of tillers (NT), number of spikelets (NS), kernel weight (KW), thousand kernel weight (TK)

Trait	S.j	Value (%)
DF	12089.94	81.18
PH	1089.92	7.32
SL	105.72	0.71
NT	306.31	2.06
NS	302.60	2.03
KW	856.88	5.75
TK	141.48	0.95

The cluster analysis verified two distinct groups, expressing genetic differences, according to evaluated traits (Figure 2). Group I: composed by genotype Pampeano. Pampeano has a longer period for days of emergence to flowering, higher plant height and longer spike length than the other genotypes. Group II: genotypes UFSMFW1 01 to UFSMFW1 10, Mirante, Quartzo, Supera and TBIO Toruk in exception of Pampeano, the other genotypes have similarity among agronomic traits, being plant height and days of emergence to flowering the traits that more contribution to the divergence. The highest plant height for the genotype Pampeano, corroborated to results obtained by Bornhofen et al. (2013) studying the best genotypes for general combining ability.

Figure 2
Dendrogram using unweighted pair group method with arithmetic mean (UPGMA) from Euclidian’s distance matrix for wheat genotypes. Cophenetic correlation coefficient (r): 0.73.

CONCLUSIONS

Selection based on number of tillers, kernel weight and thousand kernel weight may result in expressive selection gains. The higher significative positive genotypic correlations were verified between number of fertile tillers associated to days of emergence to flowering and kernel weight. The increased number of spikelets per spike is an efficient strategy to improve grain yield of wheat. The cycle showed greater contribution to the genetic divergence among genotypes studied. Distinct groups highlighted the genetic variability among genotypes.

ACKNOWLEDGMENTS

The authors are grateful to the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) to the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES). The authors declare that there is no conflict of interesting carrying this research and publishing this manuscript.

• 1
  This work was extracted from a dissertation of the first author.

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Publication Dates

• Publication in this collection
  17 Oct 2022
• Date of issue
  Sep-Oct 2022

History

• Received
  14 July 2021
• Accepted
  22 Nov 2021