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Article

Genetic Mapping of Quantitative Trait Loci Associated with Plant Height and Yield Component Traits in a Wheat (Triticum aestivum L.) Doubled Haploid Population Derived from Tugela-DN × Elands

by
Mantshiuwa Christinah Lephuthing
1,2,
Thobeka Philile Khumalo
1,
Vicki Louise Tolmay
1,2,
Ernest Dube
1,3 and
Toi John Tsilo
1,2,*
1
Germplasm Department, Agricultural Research Council–Small Grain Institute, Bethlehem 9700, South Africa
2
Department of Life and Consumer Sciences, University of South Africa, Florida 1710, South Africa
3
Department of Agriculture and Game Ranch Management, School of Natural Resource Management, Nelson Mandela University, George 6560, South Africa
*
Author to whom correspondence should be addressed.
Agronomy 2022, 12(10), 2283; https://doi.org/10.3390/agronomy12102283
Submission received: 3 August 2022 / Revised: 13 September 2022 / Accepted: 21 September 2022 / Published: 23 September 2022
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Abstract

:
Grain yield is a quantitatively inherited complex trait that is strongly influenced by interacting genetic and environmental factors. The identification of major quantitative trait loci (QTL) for plant height (PH) and yield component traits (YCT) is important for improving yield potential through wheat breeding. We performed a QTL analysis for PH and YCT in the Tugela-DN × Elands doubled haploid (DH) population using a genotype-by-sequence single nucleotide polymorphism and a silicoDArT-based genetic map. Field trials were conducted under rain-fed conditions across five environments in the Free State Province of South Africa during the 2017–2018 and 2018–2019 cropping seasons. Analysis of variance revealed significant differences (p < 0.001) among DH lines and the environments. However, for G × E interactions, significant differences (p < 0.05) were only observed for spikelet number per spike. Broad-sense heritability estimates of all traits ranged between 0.44 and 0.81. Nine QTL, viz. QPh.sgi-6A.2 and QPh.sgi-4D for PH, QSl.sgi-6A.2 and QSl.sgi-7A for spike length, QGns.sgi-3B for grain number per spike (GNS), QGwps.sgi-7B for grain weight per spike (GWPS), QGw.sgi-2A and QGw.sgi-7A for grain width, and QGl.sgi-3B for grain length (GL), were identified on chromosomes 2A, 3B, 4D, 6A, 7A, and 7B, in two or more environments. Some of these QTL exhibited pleiotropic effects. The QPh.sgi-6A.2 QTL for PH and QGwps.sgi-7B for GWPS appear to be novel QTL, while the rest of the reported QTL validated previously identified QTL for PH and YCT. The study also reported a trade-off between GL and GNS. The findings of this study will be useful in elucidating the genetic architecture of yield component traits contributing to the development of new dryland wheat varieties with high and stable yield.

1. Introduction

Bread wheat (Triticum aestivum L.) is an important staple food crop that provides one-fifth of the global population’s total caloric needs [1]. Food insecurity is becoming a serious global concern for the future and this is due to the negative impacts of a growing global population, continually decreasing production area, natural resources depletion, and predicted climate change on crop yields [1]. Furthermore, the ongoing war between Russia and Ukraine has negatively impacted wheat supplies and ultimately food security in many parts of the world, and this has had a significant effect on food prices [2,3]. Thus, efforts to achieve affordable food are needed on regional and global levels. The release of high-yielding varieties is an objective of modern wheat breeding programs worldwide. Wheat grain yield (GY) is a complex quantitative trait with low heritability which has delayed progress in understanding the genetic architecture underlying this trait [4,5]. To expedite its study, total GY has been partitioned into three components, including thousand kernel weight (TKW), grain number per spike (GNS), and spike number per unit area as these components often have higher heritability estimates [6]. Moreover, several other yield component traits (YCT), play significant roles in the determination of wheat GY, such as plant height (PH), spike length (SL), spikelet number per spike (SPS), grain weight per spike (GWPS), grain length (GL), and grain width (GW). These also affect GY through photosynthetic influences, grain filling, and dry matter translocation [7,8]. Yield component traits are quantitative, regulated by many genes that interact with each other, and are influenced by environmental factors [9,10,11].
An important advance of modern wheat breeding was the introduction of reduced height (Rht) genes, which made the Green Revolution successful [12]. So far, 25 major dwarfing genes that influence PH in wheat have been detected [13,14]. Out of these, Rht1 (Rht-B1b), Rht2 (Rht-D1b), and Rht8 are the dwarfing genes most widely used in wheat breeding programs around the world [14,15,16,17,18,19]. Rht1 and Rht2 are located at homologous loci on chromosomes 4BS and 4DS, with a great influence on stem growth, elongation, and the vegetative dry matter [20,21]. Rht8 and Rht24 are dwarfing genes located on chromosomes 2DS and 6A, respectively, which do not have negative effects on coleoptile length and seedling vigour [17,18,19,22,23,24,25].
Grain weight and size, both important yield components, are complex quantitative traits regulated by several genes and significantly influenced by the environment. These traits can further be broken down into a number of components including TKW, GL, GW, and grain thickness [11,26,27,28,29]. GWPS is another essential yield component that is more stably inherited than the final grain yield and is determined by GNS and grain weight. GNS is determined by the number of fertile spikelets and florets per spike and is positively correlated with SL and SPS [30].
The discovery of genetic loci regulating agronomic traits can provide valuable information and tools for grain yield improvement. Advances in the next-generation sequencing (NGS) technologies has enabled high-throughput genotyping using GBS- and array-based single nucleotide polymorphism (SNP) and silicoDArT markers, which has facilitated the study of complex quantitative traits in wheat [31]. Quantitative trait loci (QTL) mapping is a key approach that has been widely used to successfully understand the genetic architecture and identify novel genomic regions associated with important agronomic traits in wheat [16,32,33,34,35]. This approach, however, heavily relies on high-density genetic maps constructed from polymorphic markers [36]. The DArT sequencing has the advantage of discovering millions of SNPs and was reported to be the best platform for genomic prediction in wheat [37]. However, the lower marker density, low sequencing coverage (which considerably reduces the number of informative SNPs), and the subsequent low-density linkage maps associated with DArT-sequencing SNPs are attributable to the high rate of missing data and this is a concern for many applications, such as QTL mapping [37,38,39,40,41]. Nevertheless, many studies have identified QTL using the DArT sequencing platform [42,43,44,45,46]. Through the use of QTL analysis and genome-wide association studies, many QTL have been reported to influence PH, SL, SPS, GNS, GWPS, TKW, GL, and GW on all 21 wheat chromosomes using different mapping populations [11,17,30,33,36,47,48,49,50,51,52,53,54,55,56,57]. All these studies used germplasm adapted to various production systems around the world and little is reported about the germplasm adapted to Sub-Saharan Africa. It is hypothesised that there is a significant difference within the parental lines and the Tugela-DN × Elands doubled haploid (DH) population. Therefore, the objective of this study was to identify and validate QTL for PH and YCT using SNP GBS-based and silicoDArT markers in the existing DH wheat population derived from Tugela-DN × Elands.

2. Materials and Methods

2.1. Plant Materials, Field Trials, and Phenotypic Evaluations

Tugela-DN and Elands are well-adapted cultivars with high yielding potential (Figure S1) and are extensively used for dryland wheat production in South Africa [58]. Released 30 and 24 years ago, respectively, Tugela-DN and Elands exhibit contrasting phenotypes for grain morphological characteristics; important agronomic traits such as aluminum tolerance and pre-harvest sprouting tolerance; and resistance to rust diseases [59,60], Table S1. Tugela-DN is a winter cultivar that meets the requirements of all the quality subclasses and is well adapted to all potential conditions. It has a shorter plant stature and has bigger seeds in contrast to Elands. Elands is a facultative cultivar with exceptional bread-making quality; it is used as a standard check for bread quality in the South African wheat industry. The DH population was developed by crossing Tugela-DN and Elands cultivars. After the production of F1 or F2 progeny, chromosome doubling was induced on plants that had undergone embryo rescue and the regeneration using tissue culture technique. The Tugela-DN × Elands DH population was developed specifically to evaluate for pre-harvest sprouting tolerance. However, it was noted that the population also segregated for plant height and yield component traits. Thus, the DH population was phenotyped in multiple environments to explore the genetic variability driving the performance and adaptation of these cultivars.
A total of 138 DH lines derived from the Tugela-DN × Elands cross and their parental lines (serving as checks) were planted in single one-meter rows, with an inter-row spacing of 0.45 cm, using a modified augmented design following Federer [61]. The genotypes were evaluated for PH, SL, SPS, GNS, GWPS, TKW, GL, and GW in five environments in the Free State Province of South Africa during the 2017–2018 and 2018–2019 cropping seasons. The environments selected for this study were Arlington (ARL 2018), Bethlehem (BHM 2018), Clarens (CLAR 2018), Harrismith (HAR 2018), and BHM 2019, designated as E1, E2, E3, E4, and E5, respectively. The weather description and geographic location of the field trials were previously described [59,62]. The phenotypic evaluations were also described in an earlier publication [62], except for plant height. The YCT were evaluated in E1, E2, E4, and E5, and PH was evaluated in E1, E2, E3, and E4. In brief, SL was measured (in cm) from the base of the rachis to the tip of the terminal spikelet excluding the awns. SPS was determined by counting spikelets per spike, excluding the bottom and the top spikelets. GWPS was measured (in g) from the GNS, which were initially hand-threshed and counted. TKW was measured (in g) by weighing 1000 seeds per line. For grain size, only grain length (GL in mm) and weight (GW in mm) were selected for this study. PH was measured (in cm) from the ground to the tip of the ear on fully mature plants, excluding awns.

2.2. Statistical Analysis

Statistical analyses were performed using Genstat 18th Edition [63]. The phenotypic data were tested for normality using the Shapiro–Wilk test and Wilcoxon matched-pairs test. The analysis of variance (ANOVA) and the Kruskal–Wallis test were used to examine the significant effects of genotypes, environments, and the genotype × environment interaction (G × E). The G × E was estimated from the error mean square (MSge) of the replicated parents within environments according to an augmented design defined by Federer [61]. Broad-sense heritability (H2) estimates were calculated following Tsilo et al. [64]:
H 2 = 1 − M S g e M S g
where MSge and MSg represent the genotype × environment and the genotype mean squares, respectively. Pearson’s correlation coefficients (r2) were calculated to estimate the relationships between the traits.

2.3. Genotyping and Construction of Genetic Map

The genomic DNA extraction and linkage map construction were described in an earlier publication [65]. In short, the genomic DNA was isolated from fresh seedling leaves of DH lines and parents. The extracted DNA was genotyped by genotype-by-sequence (GBS) at Diversity Arrays Technologies (Yarralumla, Australia) with the DArT-sequencing 1.0 assay. Non-informative markers (redundant and distorted markers (p < 0.05), markers with switched alleles, and markers with missing data (≥50%)) were evaluated using RStudio version 1.1.463 [66] and JoinMap version 4.1 [67] and were removed from further analysis. The regression mapping algorithm [68] was used to determine the order of markers within the linkage group. The map distances were calculated from recombination frequencies and converted to centimorgan (cm) using the Kosambi mapping function [69].

2.4. QTL Analysis

QTL analysis was performed using Windows QTL Cartographer version 2.5 [70]. Composite interval mapping (CIM) was used to screen for significant QTL using individual mean scores per environment and average mean scores across all environments. The forward regression model was used with a window size of 10 cm, a walking speed of 2 cm, and five control markers. QTL were named according to the International Rules of Genetic Nomenclature adapted for wheat (http://wheat.pw.usda.gov/ggpages/wgc/98/Intro.htm, accessed on 24 March 2022). An LOD threshold of 2.5 was used to declare significance of the QTL. A QTL was declared when it was detected in two or more environments.

3. Results

3.1. Phenotypic Performance of DH Population

Eight agronomic traits, including PH, SL, SPS, GNS, GWPS, TKW, GL, and GW, were evaluated using the 138 DH lines. There were significant differences between the parental lines for most of the traits, except for PH, SL, and TKW (Figure 1). Moreover, transgressive segregation existed among the DH lines, with the population showing considerable variation for all traits (Table S2; Figure 1). There were significant differences among the 138 DH lines (p < 0.001) for all traits (Table S3). The G × E interaction only showed a significant effect (p ≤ 0.05) on SPS, suggesting a strong environmental influence on this trait. The broad sense heritability estimates ranged from 0.44 for GW to 0.81 for PH.

3.2. Correlations between Traits

Pearson’s correlation coefficients (r2) among the eight traits were calculated based on the average values across the five environments (i.e., E1, E2, E3, E4, and E5) (Table 1). Significant and positive correlations (p ≤ 0.01) were observed among six agronomic traits, which suggested that these traits are associated with each other. An exception was observed for GL, which only correlated positively with GW (r2 = 0.312, p ≤ 0.01). The highest and the weakest associations were observed between GNS and GWPS (r2 = 0.854, p ≤ 0.01), and GNS and GW (r2 = 0.211, p ≤ 0.01), respectively. PH was highly positively correlated with SL, SPS, and GWPS with r2 ≥ 0.759 at p ≤ 0.01, and moderately correlated with GNS and TKW with r2 ≥ 0.572 at p ≤ 0.01. Interestingly, GW was moderately correlated with TKW and GWPS with r2 ≥ 0.494 at p ≤ 0.01, suggesting an association between grain width and weight.

3.3. Linkage Map Construction

Among the 3204 SNP and 9117 silicoDArT markers, only 483 SNP and silicoDArT polymorphic markers with ≥50% of genotypic data were used to construct the linkage map spanning 1516.57 cm across all 21 chromosomes (Table 2). The 21 chromosomes were represented by 23 linkage groups (LG). The average chromosome length was 65.94 cm, ranging from 34.15 cm (5D) to 126.62 cm (7A). The number of markers on each chromosome varied from seven on 6D to 40 on 2B. The B sub-genome had the most (51.76%) markers, followed by the A sub-genome (24.84%), and the D sub-genome with the least number of markers (23.40%) (Table 2).

3.4. QTL Mapping Analysis

Across all environments, a total of 59 individual additive effect QTL influencing all eight agronomic traits were identified in the Tugela-DN × Elands DH population (Table 3 and Table S4). These QTL explained a phenotypic variation ranging between 7.05 and 36.56%, and were distributed on 19 chromosomes, excluding 6D and 7D. Fewer QTL were located on the D sub-genome (12) than on the B (26) and A (21) sub-genomes. The QTL that were detected in two or more environments were declared as QTL in this study. Among the identified QTL, nine QTL for PH, SL, GNS, GW, and GL were declared on chromosomes 2A, 3B, 4D, 6A, 7A, and 7B, and three of them influenced multiple traits (Figure 2; Table 3). All QTL for TKW and SPS were detected in only one environment and were therefore not discussed further. The details of QTL detected in multiple environments for each trait are as follows:

3.4.1. Plant Height (PH)

Two QTL for PH were detected on chromosomes 4D and 6A (Figure 2; Table 3). The QPh.sgi-6A.2 QTL explained phenotypic variation of 24.91%, and Tugela-DN contributed favourable alleles with additive effects of 6.59 cm. QPh.sgi-6A.2 co-located with the major QTL for SL (QSl.sgi-6A.2). The second QTL (QPh.sgi-4D) explained up to 10.74% of the total phenotypic variation with favourable alleles from Elands, having additive effects of 3.90 cm for PH.

3.4.2. Spike Length (SL)

Two QTL for SL were detected on chromosomes 6A and 7A (Figure 2; Table 3). The major QTL identified on chromosome 7A (QSl.sgi-7A) accounted for up to 25.05% of the phenotypic variation. This QTL was co-located with a QTL for GW (QGw.sgi-7A). QSl.sgi-6A.2, another major QTL that was identified on chromosome 6A, explained 17.50% of the phenotypic variation. The favourable alleles for SL at both these loci were contributed by Tugela-DN.

3.4.3. Grain Number Per Spike (GNS)

One major QTL for GNS was mapped on chromosome 3B (QGns.sgi-3B) and it accounted for up to 12.87% of the phenotypic variation. This QTL was co-located with the major QTL for GL (QGl.sgi-3B) (Figure 2; Table 3). Tugela-DN contributed favourable alleles to QGns.sgi-3B, with additive effects of 2.13 (PVE = 7.23%, LOD = 2.57), while Elands contributed unfavourable alleles, with additive effects of 2.91 for GNS.

3.4.4. Grain Weight Per Spike (GWPS)

A QTL for GWPS was mapped on chromosome 7B (QGwps.sgi-7B) and it explained up to 15.56% of the total phenotypic variation (Figure 2; Table 3). The favourable alleles were contributed by Tugela-DN, with an additive effect of 0.26 g (PVE = 15.56%, LOD = 3.97) for GWPS.

3.4.5. Grain Length (GL)

One major QTL, QGl.sgi-3B, was detected in two environments and explained 17.23% of the total phenotypic variation in GL (Figure 2; Table 3). The positive alleles at this locus were contributed by Tugela-DN, with additive effects of 0.26 mm.
Table
Table 3. QTL detected in two (2) or more environments and/or combined QTL analysis in the Tugela-DN × Elands doubled haploid population for plant height and yield component traits.
Table 3. QTL detected in two (2) or more environments and/or combined QTL analysis in the Tugela-DN × Elands doubled haploid population for plant height and yield component traits.
Trait aChromosome (Position) bMarker Interval/Flanking MarkerQTL cDetected Environments dQTL Effects eReference
LODAddPVE (%)LODAddPVE (%)
PH6A (18–22)3025617|F|0–30:C>T; 3222505|F|0–31:C>G; 4910650|F|0–18:C>TQPh.sgi-6A.2+E1, E2, E3, C3.303.4110.452.814.458.15[7,23,33,72]
9.946.5924.917.575.2220.47
4D (17–32)3957744|F|0–35:C>T; ALMT1; 1254717QPh.sgi-4D+E2, E3, C2.76−4.677.053.94−4.328.61[10,15,30,33,73]
4.30−3.9010.74
SL6A (18)3025617|F|0–30:C>TQSl.sgi-6A.2+E1, E4, C4.680.5114.116.100.5417.50[7,74,75,76,77]
4.570.3411.56
7A (51–84)4911195|F|0–8:T>C; 3952934|F|0–27:G>A; 3064815|F|0–27:A>GQSl.sgi-7A+E3, E4, E52.740.4915.663.040.3610.47[34,78,79]
4.390.6025.05
GNS3B (7–50)3025468|F|0–18:T>G; 3948041QGns.sgi-3B+E4, E54.19−2.9112.872.572.137.23[10,80,81]
GWPS7B (54–98)3222513|F|0–21:T>C; 1004245|F|0–36:G>AQGwps.sgi-7B+E1, E23.94−0.1914.963.970.2615.56
GL3B (4–55)3950390|F|0–5:G>C; 4539741; 5969907|F|0–9:C>A; 4910077|F|0–9:G>CQGl.sgi-3B+E1, E44.580.2617.233.340.2512.82[82]
GW7A (27–62)4394765|F|0–8:C>G; 1164940|F|0–59:G>CQGw.sgi-7A+E2, E53.020.1611.562.940.1611.11[83]
2A (42–83)3064828|F|0–5:G>C; 4993789|F|0–5:C>AQGw.sgi-2A+E4, E55.100.2225.174.360.2117.58[28,77,84]
a PH, plant height; SL, spike length; GNS, grain number per spike; GWPS, grain weight per spike; GL, grain length; GW, grain width. b Chromosome (Position) indicates the chromosome and exact position (in cm) where the QTL was mapped. c Underlined QTL influence more than one trait. d E1, E2, E3, E4 and E5 represent Arlington 2018, Bethlehem 2018, Clarens 2018, Harrismith 2018, and Bethlehem 2019, respectively. “C” indicates the combined QTL analysis based on an average trait score across all evaluated environments. e QTL Effects describe the logarithm of the odds (LOD) score; the additive effect (Add); and the phenotypic variation explained by the QTL in percentage (PVE (%)). A negative additive effect indicates that contributing alleles were inherited from Elands, and the positive effect shows that the alleles were contributed by Tugela-DN.

3.4.6. Grain Weight (GW)

Two QTL (QGw.sgi-2A and QGw.sgi-7A) were mapped on chromosomes 2A and 7A for GW, and explained 25.17 and 11.56% of the total phenotypic variation, respectively (Figure 2; Table 3). The favourable alleles for both QTL were contributed by Tugela-DN. QGw.sgi-7A was identified in the similar genomic interval (27–62 cm) as the QTL for SL (QSl.sgi-7A).

4. Discussion

4.1. Phenotypic Variations

The phenotypic characterization of agronomic traits across different environments is important for evaluating the stability of traits and contributes to the accurate detection of stable genomic regions [33]. Grain yield exhibits lower heritability than YCT and PH, which also show significant associations with GY. They are thus used to dissect the genetics of wheat grain yield and facilitate breeding for varieties with high yield potential [5,51,85]. The present study evaluated the performance of a DH population derived from Tugela-DN × Elands across multi-environments and identified the genomic regions associated with PH and YCT. The DH lines showed high phenotypic variation across environments (Figure 1), with moderate to high heritability estimates and desirable significant associations among evaluated traits (Table 1 and Table S3), which concurred with the findings of other studies [29,33,78,83,86,87]. A significant (p < 0.05) deviation from the normal distribution of the DHs was observed with PH, SPS, GWPS, TKW, and GW (Table S2; Figure 1), indicating the uneven performance response of DHs for these traits across the environments. However, the skewness of the data was fairly symmetrical (−0.5–0.5) (Table S2). This could be attributed to the difference in the environments used to evaluate these traits, which showed a significant effect (Table S3).

4.2. QTL Analysis for Agronomic Traits

A total of nine QTL were detected for PH and YCT in multiple environments and explained a PVE range of 7.05–25.17%. These QTL included (i) grain size QTL identified on chromosomes 2A (QGw.sgi-2A), 3B (QGl.sgi-3B) and 7A (QGw.sgi-7A); (ii) QTL regulating spike morphological traits–grain number per spike on 3B (QGns.sgi-3B), grain weight per spike on 7B (QGwps.sgi-7B), and spike length on 6A (QSl.sgi-6A.2) and 7A (QSl.sgi-7A); and (iii) QTL controlling plant height on 4D (QPh.sgi-4D) and 6A (QPh.sgi-6A.2) (Figure 2, Table 3). Tugela-DN contributed favourable alleles for two-thirds of the QTL for yield component traits. This agrees with the findings of our previous study that Tugela-DN had a higher yield potential when compared to Elands [62]. All detected QTL were of major effects, explaining the phenotypic variation of more than 10.00%.
Over the past decades, QTL (and genes) for PH and YCT have been identified on all bread wheat chromosomes using various wheat materials tested across different environments [5,10,23,30,33,72,81]. QTL with major effects controlling these traits were mainly detected on chromosomes 1B, 2A, 2B, 2D, 3A, 4A, 4B, 4D, 5A, 6A, and 7A. The present study intended to identify the QTL for PH and YCT in a wheat population, derived from two widely used and well-adapted South African cultivars (Tugela-DN and Elands), that had available GBS-SNP and silicoDArT markers. Some of the well-known loci with major effects on PH and YCT were validated on wheat chromosomes 2A, 4D, and 7A, in addition to potentially new QTL identified on chromosomes 6A and 7B. The results of the study indicated that these loci are expressed when the environmental conditions are conducive.
Grain size, which constitutes of GL and GW, remains an important trait in the improvement of grain yield in wheat [28,82,88,89]. A GW QTL (QGw.sgi-2A) mapped on chromosome 2A. This locus coincides with QGw.cib-2A detected by Li et al. [77] and QGw.haaf-2AS (PVE = 8.9–14.3%) that was reported by Li et al. [5]. QGw.sgi-2A was discovered in two environments and showed a relatively high phenotypic variance ranging between 17.58 and 25.17% (Table 3). Wu et al. [28] also reported a QTL for GW on this chromosome. All these previous findings, together with the current study, prove that QGw.sgi-2A is a valid QTL, which has been repeatedly detected across diverse sets of wheat populations and genetic materials in multiple environments. The additive effect of QGw.sgi-7A on GW was smaller (PVE = 11.56%, Table 3) than that of QGw.sgi-2A. QGw.sgi-7A mapped on chromosome 7A in a similar region to QGw.cau-7A-2 (PVE = 4.03%), a QTL with minor effect as reported by Chen et al. [90]. There have been no other GW QTL (especially with major QTL effect) reported on this chromosome, suggesting that QGw.sgi-7A is a valid QTL. On the same region, most studies previously reported GL instead of GW QTL on chromosome 7A [28,91,92].
A GL QTL was identified on chromosome 3B (QGl.sgi-3B). Several other studies [26,28,80,93] have reported QTL for GL on chromosome 3B, validating the existence of this locus (QGl.sgi-3B) on 3B. This locus (QGl.sgi-3B) can be used in the improvement of grain yield potential in wheat as it has been validated in different genetic backgrounds and under different environments. QGl.sgi-3B coincided with QGns.sgi-3B on chromosome 3B. QGns.sgi-3B mapped in a similar region as a QTL (Gns.cau-3B.1) with a minor effect on grain number per spike that was reported by Wen et al. [81]. The GL and GW QTL did not overlap in the present study; however, a significant positive correlation was observed between the two traits. The results of Pearson’s correlation analysis (Table 1) proved a high contribution of GW towards TKW and GWPS as compared to GL. This observation indicates that GW can reflect grain weight better than GL [5], which concurs with results by Cui et al. [82], who reported that GL contributed less, especially toward the grain weight. This could be because, unlike GL, GW and other grain size components such as grain thickness are more sensitive to the environment as a result of their formation in the later stage of grain development [57,94,95]. Therefore, a favourable environment would contribute to more filled grains (increased grain width) in contrast to harsh environments, which would compromise grain width and thus result in reduced grain weight and yield.
Plant height is an important agronomic trait indirectly affecting grain yield. Increases in wheat yield during the Green Revolution were successfully attained through the introduction of reduced height (Rht) genes [73,96]. Wheat varieties with reduced plant height efficiently utilize available nutrients and water to produce more reproductive organs, i.e., tillers, thereby maximizing their yield potential. In contrast, taller varieties use the available resources for straw development rather than grain yield. However, these plants mostly lodge leading to a reduction in grain yield [97]. In the present study, a major QTL controlling PH (QPh.sgi-6A.2) was identified in three out of five environments and this QTL was located on chromosome 6A. QPh.sgi-6A.2 is similar to qPH-6A reported by Chen et al. [90]. Previous studies have reported the PH QTL on chromosome 6A as likely to be the Rht24 gene [17,24,72,98]. Tian et al. [17] verified this QTL even though the genetic distance between the markers was slightly greater than previously reported. This could be attributed to the high recombination frequency of markers in the Rht24 region and the difference in the genetic material used. The major gene, Rht24, not only reduces PH but was also reported to increase grain weight. However, the QTL (QPh.sgi-6A.2) identified in the present study showed no effect on grain weight, suggesting that it may be a novel or different QTL. To date, five reduced height genes (Rht14, Rht16, Rht18, Rht24, and Rht25) have been reported on chromosome 6A and show varying effects on grain weight [90]. We anticipate that screening our parental cultivars (Tugela-DN and Elands) and the Tugela-DN × Elands DH lines with Rht24 markers or markers linked with other Rht genes reported on chromosome 6A will confirm if QPh.sgi-6A.2 is a new QTL, Rht24 or another Rht gene previously identified on chromosome 6A.
QPh.sgi-6A.2 was discovered in the same genomic region as SL (QSl.sgi-6A.2) on chromosome 6A, and the favourable alleles resulting in the reduced plant stature and longer spikes in these QTL were both contributed by Tugela-DN. This was consistent with the phenotypic correlation analysis (Table 1). Many studies have reported strong positive associations between PH and SL [10,16,87,99], suggesting that these two traits can be selected together in a breeding program. The pleiotropic effect between these two traits is mainly attributed to the Q locus among the three well-known loci (Q, C, and S) that were recruited for the domestication of wheat spike morphology [100,101]. Another major QTL (QPh.sgi-4D) for PH was detected on chromosome 4D, and showed an influence of the taller parent (Elands), which extended plant height by 4.67 cm. QPh.sgi-4D should be avoided in a breeding program as it contributes negatively toward the improvement of grain yield and differs from the commonly identified Rht2 gene/QTL [15,24,32,33,72,73,83,89].
The QGw.sgi-7A, which regulated grain width coincided with a locus (QSl.sgi-7A) controlling spike length, and the favourable alleles contributed by Tugela-DN increased GW and SL. QSl.sgi-7A had a major effect on SL, explaining 10.47–15.66% of the phenotypic variance. Pretini et al. [78] recently identified a stable QTL of major effect (PVE = 8.5–17.0%) for SL on chromosome 7A. This result is comparable to our finding, suggesting it might be the same QTL.
The QTL for GL (QGl.sgi-3B) identified on chromosome 3B in our study concurs with the findings of Wu et al. (2015) who reported a stable QTL at the same locus. QGl.sgi-3B co-located with QGns.sgi-3B on chromosome 3B. Wen et al. [28] reported this QTL as a novel locus for GNS on this chromosome. The source (Tugela-DN) of favourable alleles for QGl.sgi-3B was opposite to that of QGns.sgi-3B (contributed by Elands), indicating a trade-off between GL and GNS, which was consistent with the phenotypic correlation analysis (Table 1). To the best of our knowledge, no trade-off has previously been reported for these two traits and should be further investigated.
A QTL on chromosome 7B (QGwps.sgi-7B) was identified for grain weight per spike in our study. Loci associated with grain size have been reported on chromosome 7B, including a GW QTL identified by Li et al. [77] and a TGW QTL mapped by Wang et al. [102]. No supporting literature on the identified QTL for GWPS on chromosome 7B could be found, suggesting that this might be a new QTL. Therefore, more studies are needed to validate this finding.

5. Conclusions

This study identified nine QTL associated with PH, SL, GNS, GWPS, GL, and GW within six genomic regions on chromosomes 2A, 3B, 4D, 6A, 7A, and 7B, in the Tugela-DN × Elands DH population across five different environments. These results of the present study validated the presence of well-known QTL regulating PH and YCT in wheat. Potentially new QTL influencing PH (QPh.sgi-6A.2) and GWPS (QGwps.sgi-7B) were identified on chromosome 6A and 7B, respectively. The study also reported a trade-off between GL and GNS on chromosome 3B, which should be further investigated. The use of a high-density genetic linkage map is anticipated to detect more loci and improve the precision of QTL detection. Overall, these results will contribute to our understanding of the genetic basis of PH and YCT and could be used to improve grain yield in wheat through marker-assisted selection breeding.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agronomy12102283/s1, Figure S1 Long-term and year-specific climatic conditions of Arlington, Bethlehem, Clarens, and Harrismith, (a) average temperatures and (b) average rainfall; Table S1. Description of parental wheat cultivars used to develop doubled haploid lines population; Table S2. Descriptive statistics and the data normality test for plant height and yield component traits across five study environments. a PH, plant height; SL, spike length; SPS, spikelet number per spike; GNS, grain number per spike; GWPS, grain weight per spike; TKW, thousand kernel weight; GL, grain length; GW, grain width. b SD, standard deviation. * Data significantly deviates from normal distribution at p ≤ 0.05. *** Data significantly deviates from normal distribution at p ≤ 0.001. Table S3. Mean squares from the combined ANOVA of plant height and yield component traits in Tugela-DN × Elands doubled haploid mapping population evaluated at Free State province of South Africa. PH, plant height; SL, spike length; SPS, spikelet number per spike; GNS, grain number per spike; GWPS, grain weight per spike; TKW, thousand kernel weight; GL, grain length; GW, grain width. DF, degrees of freedom; G × E, genotype × environment interaction. ** Means are significantly different at p < 0.01; *, Means are significantly different at p < 0.05; ns, non-significant difference in means. Table S4. Summary of QTL for PH and yield component traits in the 138 doubled haploid wheat population. a PH, plant height; SL, spike length; SPS, spikelet number per spike; GNS, grain number per spike; GWPS, grain weight per spike; TKW, thousand kernel weight; GL, grain length; and GW, grain width. b Position indicates the chromosome and exact position (in cm) where the QTL was mapped. c QTL shown in bold and with + are stable QTL detected in two (2) or more environments and/or combined QTL analysis. Underlined QTL influence more than one trait. d E1, E2, E3, E4, and E5 represent Arlington 2018, Bethlehem 2018, Clarens 2018, Harrismith 2018, and Bethlehem 2019, respectively. “C” indicates the combined QTL analysis based on an average trait score across all evaluated environments. e QTL effects describe the logarithm of the odds (LOD) score; the additive effect (Add); and the phenotypic variation explained by the QTL in percentage (PVE (%)). A negative additive effect indicates that contributing alleles were inherited from Elands, and the positive effect shows that the alleles were contributed by Tugela-DN.

Author Contributions

Conceptualization, M.C.L., T.P.K. and T.J.T.; data curation, M.C.L. and T.P.K.; formal analysis, M.C.L., T.P.K., V.L.T. and T.J.T.; methodology, M.C.L. and T.P.K.; project administration, M.C.L., V.L.T. and T.J.T.; supervision, V.L.T. and T.J.T.; funding, T.J.T.; writing—original draft, M.C.L. and T.P.K.; writing—review and editing, M.C.L., T.P.K., V.L.T., E.D. and T.J.T. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Research Foundation (NRF–RTF) under Project number UID: 98599, Agricultural Research Council under Project number P15000036 and Winter Cereal Trust under Project number P15000031.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

The data presented in this study are available in this article and Supplementary Material, and further inquiries can be directed to the corresponding author.

Acknowledgments

The authors would like to thank ARC-SGI Germplasm Department for providing the doubled haploid population used in this study. ARC-SGI technical staff (Timmy Baloyi, Tsepiso Hlongoane, Tebogo Oliphant, Samuel Tsotetsi, and Esther Tsotetsi) are acknowledged for developing the DH population, assisting with the management of field trials, and the evaluation of data.

Conflicts of Interest

The authors declare no conflict of interest.

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Agronomy 12 02283 g001 550
Figure 1. Frequency distribution of agronomic traits in the 138 doubled haploid lines.
Figure 1. Frequency distribution of agronomic traits in the 138 doubled haploid lines.
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Agronomy 12 02283 g002 550
Figure 2. The chromosome regions harbouring QTL for plant height (PH), spike length (SL), grain number per spike (GNS), grain weight per spike (GWPS), grain length (GL), and grain width (GW). Vertical bars represent the confidence interval and LOD peak position of each QTL identified using the linkage map of Tugela-DN x Elands DH population.
Figure 2. The chromosome regions harbouring QTL for plant height (PH), spike length (SL), grain number per spike (GNS), grain weight per spike (GWPS), grain length (GL), and grain width (GW). Vertical bars represent the confidence interval and LOD peak position of each QTL identified using the linkage map of Tugela-DN x Elands DH population.
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Table
Table 1. Correlation coefficients between plant height and yield component traits.
Table 1. Correlation coefficients between plant height and yield component traits.
Trait aPHSLGNSSPSGWPSTKWGL
SL0.764 **-
GNS0.660 **0.604 **-
SPS0.811 **0.756 **0.823 **-
GWPS0.759 **0.611 **0.854 **0.764 **-
TKW0.572 **0.351 **0.449 **0.444 **0.786 **-
GL−0.414 **−0.201 **−0.340 **−0.371 **−0.242 **−0.120 *-
GW0.297 **0.235 **0.211 **0.212 **0.447 **0.494 **0.312 **
a PH, plant height; SL, spike length; SPS, spikelet number per spike; GNS, grain number per spike; GWPS, grain weight per spike; TKW, thousand kernel weight; GL, grain length; GW, grain width. Values highlighted in red, green, and bold indicate significant high, moderate, and weak correlations, respectively, following Hinkle et al. [71]. ** Significantly different at p ≤ 0.01; *, p ≤ 0.05, respectively.
Table
Table 2. Summary of the genetic linkage map of the Tugela-DN × Elands DH population.
Table 2. Summary of the genetic linkage map of the Tugela-DN × Elands DH population.
ChromosomeNo. of MarkersMap Length (cm)Marker Density (cm/Marker) a
1A1949.332.60
1B3090.053.00
1D2576.503.06
2A1796.055.65
2B4075.981.90
2D1173.566.69
3A849.806.22
3B2958.842.03
3D ‡1589.9012.33
4A848.356.04
4B2382.903.60
4D1574.354.96
5A1453.353.81
5B3176.912.48
5D1534.152.28
6A2057.272.86
6B ‡5893.603.38
6D755.747.96
7A34126.623.72
7B39110.632.84
7D2542.671.71
A sub-genome120480.774.01
B sub-genome250588.922.36
D sub-genome113446.883.96
Total4831516.573.87
‡ Chromosomes with two separate linkage groups; a cm/marker was calculated by dividing the genetic length by the number of markers.
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Lephuthing, M.C.; Khumalo, T.P.; Tolmay, V.L.; Dube, E.; Tsilo, T.J. Genetic Mapping of Quantitative Trait Loci Associated with Plant Height and Yield Component Traits in a Wheat (Triticum aestivum L.) Doubled Haploid Population Derived from Tugela-DN × Elands. Agronomy 2022, 12, 2283. https://doi.org/10.3390/agronomy12102283

AMA Style

Lephuthing MC, Khumalo TP, Tolmay VL, Dube E, Tsilo TJ. Genetic Mapping of Quantitative Trait Loci Associated with Plant Height and Yield Component Traits in a Wheat (Triticum aestivum L.) Doubled Haploid Population Derived from Tugela-DN × Elands. Agronomy. 2022; 12(10):2283. https://doi.org/10.3390/agronomy12102283

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Lephuthing, Mantshiuwa Christinah, Thobeka Philile Khumalo, Vicki Louise Tolmay, Ernest Dube, and Toi John Tsilo. 2022. "Genetic Mapping of Quantitative Trait Loci Associated with Plant Height and Yield Component Traits in a Wheat (Triticum aestivum L.) Doubled Haploid Population Derived from Tugela-DN × Elands" Agronomy 12, no. 10: 2283. https://doi.org/10.3390/agronomy12102283

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