Next Article in Journal
Using Simple Sequence Repeats in 9 Brassica Complex Species to Assess Hypertrophic Curd Induction
Previous Article in Journal
Biochar Improves Sustainability of Green Roofs via Regulate of Soil Microbial Communities
Previous Article in Special Issue
Evaluation of a Novel Molecular Marker Associated with the Tan Spot Disease Response in Wheat
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Prospects of Developing Novel Genetic Resources by Chemical and Physical Mutagenesis to Enlarge the Genetic Window in Bread Wheat Varieties

1
Plant Genomics and Molecular Breeding Laboratory, National Institute for Biotechnology and Genetic Engineering (NIBGE), P.O. Box 577, Jhang Road Faisalabad, Faisalabad 38000, Pakistan
2
Department of Biotechnology, Pakistan Institute of Engineering and Applied Sciences (PIEAS), Nilore, Islamabad 45650, Pakistan
3
Department of Biotechnology, Faculty of Life Sciences, University of Okara, Pakistan
4
Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstraße 3, D-06466 Stadt Seeland, OT Gatersleben, Germany
5
Institute for Molecular Plant Sciences School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3BF, UK
6
Present address: European Wheat Breeding Center, BASF Agricultural Solutions GmbH, Am Schwabeplan 8, D-06466 Stadt Seeland, OT Gatersleben, Germany
*
Authors to whom correspondence should be addressed.
Agriculture 2021, 11(7), 621; https://doi.org/10.3390/agriculture11070621
Submission received: 26 May 2021 / Revised: 16 June 2021 / Accepted: 24 June 2021 / Published: 1 July 2021
(This article belongs to the Special Issue Genetics, Genomics, and Genetic Improvement of Wheat)

Abstract

:
Sustainable production and improved genetic gains can be achieved by broadening the genetic window of elite wheat germplasm. Here, we induced mutations in two spring wheat varieties, viz., NN-Gandum-1 (NN-1) and Punjab-11 (Pb-11), by exposing their seeds to ethyl methane sulfonate (EMS) and γ-rays, respectively. We characterized >3500 lines of each NN-1 and Pb-11 derived population in three consecutive generations, viz., M5, M6, and M7, for important traits, e.g., plant height, heading date, spike morphology and rust resistance. We observed significant genetic variation and correlations in both populations for all investigated traits. We observed differences in terms of number of mutants between NN-1 (22.76%) and Pb-11 (26.18%) which could be ascribed to the genotype-by-mutagen interaction. High broad-sense heritability (H2) estimates, that are vital for higher genetic gains, were observed for all of the investigated traits in both populations (H2 = 0.69–0.91 in NN-1 and 0.84–0.98 in Pb-11). Particularly, to breed for rust resistance, we selected a subset (n = 239) of M7 lines that also showed phenotypic variation for other traits. Our studies (1) show the relevance to artificial mutagenesis to create genetic variation in elite germplasm for their immediate use in current breeding programs, and (2) provide material for downstream identification of genes associated with traits of high agronomic importance.

1. Introduction

Bread wheat (Triticum aestivum L.), a major source of daily caloric intake, is grown in more than 60 countries on 221.86 million ha with an annual production of 775.8 million metric tons (https://apps.fas.usda.gov/psdonline/circulars/production.pdf accessed on 1 June 2021) during 2020-21. The most cultivated wheat is allohexaploid (2n = 6x, AABBDD) that harbors a large genome of the size ~17 Gb [1], and was originated recently through polyploidization and hybridization of Triticum turgidum (tetraploid, AABB genome) and Aegilops tauschii (diploid, DD genome) [2].
Significant gains in grain yield were achieved by introducing dwarfing (Rht) genes into wheat varieties using conventional breeding approaches that laid a firm foundation for the onset of the Green Revolution [3]. Improved germplasm harboring the Rht genes was introduced in several countries including Pakistan and India, and was extensively used for developing new wheat varieties, which consequently led to a significant production increase worldwide. Nevertheless, there are still many biotic and abiotic stress-associated challenges which result in decreased wheat production worldwide [4,5,6].
Among biotic stresses, rust diseases (e.g., yellow, leaf, and stem rusts) are the most detrimental to wheat production. Leaf rust, caused by Puccinia triticina f. sp. tritici, is distributed widely throughout the world [7] and reduces yield up to 70% by negatively impacting on the number of spikes per plant and grain weight [8,9,10]. Similarly, yellow rust, caused by Puccinia triiformis f. sp. tritici (Pst), may reduce yield up to 60% [11]. Stem rust, caused by Puccinia graminis f. sp. triticiis, is a disease of relatively warmer climates, which is prevalent in many wheat growing areas and, therefore, countries such as Pakistan and India, are at a high risk. Yield losses due to stem rust range usually from 10 to 45% but can be catastrophic under extreme conditions [12]. Additionally, evolution of new virulent strains may overcome the resistance of the cultivated varieties; this further complicates the situation [13,14]. Abiotic stresses, e.g., drought and salinity, on the other hand, cause wheat reduction by 50–90% and 10–90%, respectively [15,16,17]. These stresses impede plant growth and result in premature leaf senescence, reduced tillering and, consequently, reduced grain yield [5,18,19].
The challenges associated with both biotic and abiotic stresses can be addressed by developing diverse genetic resources that are required not only for developing resilient wheat cultivars using conventional breeding approaches, but also can be used for understanding the genetics of yield and yield component traits using modern genomic technologies [20,21]. The extent of genetic variations was found to be low among cultivated wheat varieties largely due to the development of varieties by repeatedly intercrossing of highly adaptive germplasm [4,22].
Mutagenesis is a widely used, effective, and economical means to induce genetic variations [23,24]. Chemical mutagens have been widely used for undertaking forward and reverse genetic studies for understanding the function of genes. The most commonly used chemical mutagens are ethyl methane sulphonate (EMS) and dimethyl sulfoxide (DMS), which generate random point mutations in the entire genome [25]. Physical mutagens, e.g., γ-rays, have also been used to create variations in various plant species. Optimal dose of the radiation is the key to achieving the maximum mutagenesis rate with an acceptable fertility rate of M1 plants. However, low to moderate doses of the mutagen ensures the maximum frequency of beneficial mutants [26]. A high dose of radiations, on the other hand, may depress the viability of plants due to a greater ratio of deletions and aberrations in genes. Large deletions were, nevertheless, not carried to the M2 generation of Arabidopsis, possibly due to loss of many genes controlling the development of the gamete and seed [27]. However, tolerance to large number of mutations, including deletions, was reported high in polyploids [28]. The definition of an effective mutagen relates to the production of relatively less biological damage (seedling injury, sterility, etc.) than the number of induced mutations in the genome. Thus, the choice of mutagen is important for inducing a high frequency of mutations.
In the present study, three generations (i.e., M5, M6 and M7) of two mutant populations were developed by exposing the kernels of NN-Gandum-1(NN-1) and Punjab-11 (Pb-11) with EMS and γ-rays, respectively. We investigated the mutagenesis efficiency of chemical (EMS) and physical mutagens (γ-rays), genetic variability, and correlation among the investigated traits and generations. Lastly, we compared the effectiveness of EMS and γ-rays in the M7 generations. The newly developed mutant germplasm is expected to (1) develop resilient wheat varieties, and (2) serve as a potential source to detect new alleles which can be used to provide insights into several mechanisms controlling traits of high agronomic and disease importance.

2. Materials and Methods

2.1. Genetic Material

Two stable bread wheat varieties NN-Gandum-1 (NN-1, approved for cultivation in 2016) and Punjab-11 (Pb-11, approved for cultivation in 2011) were exposed to chemical and physical mutagens. NN-1 is a high-yielding variety, but moderately-resistant moderately-susceptible (MRMS) to leaf rust (LR) and yellow rust (YR). It was developed by the Plant Genomics and Molecular Breeding Laboratory (NIBGE) Faisalabad Pakistan. Pb-11 is resistant to lodging and tolerant to leaf rust and was developed by the Wheat Research Institute (AARI), Faisalabad, Pakistan. Pb-11 is also susceptible to yellow rust and aphids, and is not suitable for late sowing, especially after cotton and rice.

2.2. Development of Mutant Populations

Breeder seeds of NN-1 were exposed to an optimized concentration of 0.8% (v/v) of ethyl methane sulphonate (EMS) for 2 h at 35 °C. For mutagenesis with γ-rays, the breeder seeds of Pb-11 were exposed to an optimum dose of 250k rad, Cs 137 for 25 min at room temperature (unpublished data, Figure 1). Seeds were sterilized with 5% sodium hypochlorite and 70% ethanol followed by three washings to remove their residues.
A total of ~7500 seeds of NN-1 and ~7000 seeds of Pb-11 were exposed to the optimized concentration of EMS and γ-rays, respectively. The M0 seeds of both the parental genotypes were manually sown in the experimental fields of NIBGE (Faisalabad, Pakistan) in 2012 by keeping 10 cm hill-to-hill distance and 30 cm between the rows. Standard agronomic practices were applied from sowing up to harvesting. In total, ~4000 and 3700 M1 plants of NN-1 and Pb-11 were germinated, respectively. At maturity, one main spike of each plant was bagged and harvested separately. The harvested spikes were threshed followed by sowing of M2 rows of each M1 spike in 2013. The next year, M3 populations were sown followed by harvesting a single spike from the main tiller. The M4 seed of these mutant populations were planted in June 2014 at Naran and Kaghan (off-season nursery) for advancing generation. A single plant with 3–5 tillers from each row was harvested separately. In 2014–2015, M4 seeds were sown and harvested. In 2015–2018, the M5 populations of NN-1 (3634 lines) and Pb-11 (3600 lines), M6 populations of NN-1 (3533 lines) and Pb-11 (3483 lines) and M7 populations of NN-1 (3502 lines) and Pb-11 (3453 lines) were sown in NIBGE fields in a completely randomized design. The first and every 10th row was planted with the universal susceptible variety “Morocco” to ensure the provision of maximum inoculum.

2.3. Rust Inoculation

For producing the rust inoculum, uredospore (5 g) of leaf rust were taken in small petri plates from the infected leaves of the universal susceptible check “Morocco”. These spores were thoroughly mixed in a solution (1 L distilled water with ~1 ppm Tween20). Concentration of the uredospore was estimated using a Hemocytometer. Final concentration of the solution was adjusted to 5 × 104 by adding distilled water. The solution was sprayed using a sprayer fixed with a fine nozzle on each plant at 16:00 h followed by the showering of water twice a day for two weeks. The same procedure was adopted for inoculating yellow rust spores. All generations (M2 to M7) were inoculated with the uredospore of yellow and leaf rust.

2.4. Collection of Data from Mutant Generations

Data on response to leaf and yellow rusts (LR and YR), plant height (PH; cm), tillers per plant (TPP), days to heading (DTH) after germination, and spike length (SL; cm) were recorded. Data on disease response were collected at maturity by following a 0–9 rating system [29] of all the mutant generations. Heading date (days after germination) was observed when 50% of the total spikes in each row emerged. Similarly, plant height was measured from the base to the top of a spike of each plant. Spike length were taken by measuring the length of spike from bottom to tip without awns.

2.5. Selection Strategy

Two selection strategies were used to select mutant lines from the individual populations. First, lines were selected on the basis of their reaction to LR and YR. This process resulted in selection of 239 lines from both populations (17 LR and YR resistant lines of NN-1 and 222 LR and YR resistant lines of Pb-11). Secondly, the experimental field was divided into small blocks (containing 100 lines in each) for minimizing the impact of micro-environment. Then the means and standard deviations were estimated for each of the subplots. After this, Z scores were computed for each of the 239 selected mutant lines as:
Z = ( x u ) / s d
where x, u and sd denote the disease score, mean value and the standard deviation, respectively. The lines exhibiting the maximum Z score values for DTH, SL, PH and TPP were selected. This process resulted in the selection of lines with traits such as DTH (9 NN-1, 14 Pb-11), SL (12 NN-1, 84 Pb-11), PH (12 NN-1, 137 Pb-11) and TPP (6 NN-1, 52 Pb-11). In 2017–2018, the selected mutant lines were grown along with the wild type on 15 December 2017 in a randomized complete block design with three replications at two locations viz., NIAB and NIBGE, in Faisalabad. The plot size was 64 ft2 ( 8 × 8 ft). The rust inoculum collected from the field was sprayed two times in January to March. Standard agronomic practices were applied from sowing to harvesting. At maturity, data regarding disease response and severity of DTH, PH, SL, LR, YR, and TPP were collected.

2.6. Mutagen Effectiveness

For studying the effectiveness of both mutagens (EMS and γ radiation), traits of each M7 line of both populations were compared with the corresponding traits of the respective non-mutagenized (wild type) parent. For example, the EMS-treated population of NN-1 was compared with the non-treated NN-1, while the γ radiation population was compared with the non-treated Pb-11 population. We compared six traits including DTH, PH, SL, TPP, LR and YR. The percentage of the number of mutants for a particular trait of both populations was calculated as:
Mutant   % = Total   number   of   mutants   for   a   traits Total   size   of   mutant   population × 100
Gain over of each mutagen for a particular trait was calculated by subtracting the mutant % of NN-1 for each trait from the mutant % of the corresponding trait of the Pb-11 mutant population. Then we summed up the gain for each of the traits for both populations. The bigger value (of g-treated population) was subtracted from the smaller value of EMS-treated NN-1 population.

2.7. ANOVA, Heritability, and Correlation Analyses

We performed ANOVA to calculate the individual variance components of the genotype, environment, and the residuals. Pearson’s product moment correlation was also performed for all investigated traits by using Statistix 8.1 and SPSS 16 software. Similarly, differences among the means of DTH, SL, PH and TPP were calculated using least significant difference. Plot-based broad-sense heritability of the investigated traits was calculated as
  H 2 = σ G 2 σ G 2 + σ e 2
where σ G 2 and σ e 2 represent the genotypic and the residual variance, respectively.

3. Results

In the present studies, significant variations in DTH, SL, PH and TPP were observed in both mutagenized populations. These differences were significantly higher than variations that could occur by chance. For example, in 2015–2016, M5 lines derived from NN-1 showed variations in DTH (−4.43 to +4.57 standard deviation (SD)), SL (−6.07 to +2.33 SD), PH (−16.04 to +10.96 SD) and TPP (−0.94 to +1.06 SD) (Table 1). Likewise, in 2015–2016, Pb-11-derived mutant lines (M5) showed high variations for DTH (−1.97 to +4.03 SD), SL (−5.27 to +2.01 SD), PH (−8.62 to +4.49 SD) and TPP (−0.92 to +1.08 SD) (Table 1). Similar fluctuations in variations for each trait in both mutagenized populations were also recorded in 2016–2017 and 2017–2018 (Table 1). Relatively higher number of plants conferring resistance to LR and YR were observed in all mutant populations of Pb-11 than those of the mutant populations derived from NN-1. For instance, 17 (0.49%) and 222 (6.43%) resistant mutants of NN-1 and Pb-11 were observed, respectively. A large number of disease-susceptible mutants were found in the mutant population derived from NN-1.

3.1. Prospects of NN-Gandum-1 Derived Population

A total of 3634 M5, 3533 M6, and 3502 M7 mutant lines derived from NN-1 were observed for morphological traits. In total 659 M5 (18.14%), 597 M6 (16.9%) and 590 M7 (16.85%) mutant lines showed substantial variations (85–97 days) in days-to-heading (DTH) at both the extremes (early and late) than those of the wild type (Table S1). Similarly, 929 M5 (25.56%), 841 M6 (23.8%) and 813 M7 (23.21%) mutant lines expressed variations in plant height (PH) which ranged from 81.56 to 104.34 cm versus 89.26 cm for the wild type. Similarly, variations in spike length (SL) ranged from 7.85 to 14.65 cm. For example, the SL of 396 M5 (10.9%), 352 M6 (9.96%) and 335 M7 (9.57%) mutant lines was shorter than the wild type (9.25 cm), while SL of 110 M5 (3.03%), 106 M6 (3%) and 95 M7 (2.71%) mutant lines was larger than the wild type (Table S1). Regarding tillers per plant (TPP), 512 M5 (14.09%), 505 M6 (14.29%) and 498 M7 (14.22%) mutant lines produced four TPP in comparison to five TPP in the wild type. On the other hand, 726 M5 (19.98%), 712 M6 (20.15%) and 692 M7 (19.76%) mutant lines exhibited six TPP (Table S1). Since the data regarding response to leaf and yellow rust were also collected, in total, 33 M5 (0.91%), 28 M6 (0.79%) and 17 M7 (0.49%) mutant lines showed high resistance to LR, whereas 1383 M5 (38.06%), 1356 M6 (38.38%) and 1349 M7 (38.52%) mutant lines showed response like wild type (moderately resistant to moderately susceptible). A total of 80 M5 (2.20%), 76 M6 (2.15%) and 75 M7 (2.14%) mutant lines were found highly susceptible to LR (Table S1). Regarding YR, in total, 39 M5 (1.07%), 25 M6 (0.71%) and 17 M7 (0.49%) mutant lines were found resistant to YR, while 1343 M5 (36.96%), 1330 M6 (37.65%) and 1324 M7 (37.81%) mutant lines showed responses like the wild type (moderately resistant), while 74 M5 (2.04%), 68 M6 (1.92%) and 67 M7 (1.91%) mutant lines showed high susceptibility to YR (Table S1).

3.2. Prospects of Punjab-11-Derived Population

In a mutant population derived by exposing the seed of Punjab-11 (Pb-11) with γ radiation, a total of 3600 M5, 3483 M6 and 3453 M7 mutant lines were studied for changes in morphological traits. A total of 762 M5 (21.16%), 688 M6 (19.76%) and 662 M7 (19.17%) mutant lines showed substantial variations (86–99 days) in DTH at both the extremes compared to the wild type (Table S1). Similarly, 1208 M5 (33.55%), 1125 M6 (32.3%) and 1102 M7 (31.91%) mutant lines expressed PH ranging from 80.65 to 100.36 cm as compared to the wild type (87.65 cm). The SL of 629 M5 (17.47%), 515 M6 (14.79%) and 506 M7 (14.65%) mutant lines was shorter than the wild type (8.86 cm), while the SL of 417 M5 (11.58%), 407 M6 (11.69%) and 406 M7 (11.76%) mutant lines was larger than the wild type (8.86 cm, Table S1). In total, 594 M5 (16.5%), 511 M6 (14.67%) and 495 M7 (14.34%) mutant lines consisted of three TPP versus four TPP for wild type), while 579 M5 (16.08%), 553 M6 (15.88%) and 552 M7 (15.99%) mutant lines were comprised of five TPP (higher than the wild type). The remaining population (~67%) was similar to the wild type (four TPP, Table S1). In Pb-11, as for NN-1, the phenotypic notes were also taken for leaf and yellow rust incidence. In total, 341 M5 (9.47%), 263 M6 (6.78%) and 222 M7 (6.43%) mutant lines showed high resistance to LR, whereas 1401 M5 (38.92%), 1398 M6 (40.14%) and 1393 M7 (40.34%) mutant lines were found similar to the wild type (moderately resistant). However, none of the mutant lines were found highly susceptible in all of the three generations (Table S1). A total of 301 M5 (8.36%), 231 M6 (6.63%) and 222 M7 (6.43%) mutant lines showed resistance to YR, whereas 1405 M5 (39.03%), 1390 M6 (39.91%) and 1385 M7 (40.11%) exhibited disease reactions like the wild type (resistant to moderately resistant). None of the mutant lines were found susceptible to the disease in all three generations (Table S1).

3.3. Correlations among Generations

Correlation coefficients studies were performed on whole mutant population for all the traits except LR and YR. Correlation coefficients of the traits among the mutant generations of NN-1 ranged from 0.36 to 0.69, while they were 0.79 to 0.99 for Pb-11 (Table 2). Correlation coefficients among generations for all the traits were found to be significant. In both the mutant populations, correlation values for different traits in M6 versus M7 were higher than that of the M5 versus M6 (Table 2).
In the present study, DTH, SL, PH and TPP showed significantly positive correlations among three generations of both mutant populations. DTH showed strong significant positive correlations with TPP and PH with SL (Figure 2). In total, 239 resistant mutant lines were selected to study Z-score analysis. The Z-score of 17 selected mutant lines of NN-1 for DTH, SL, PH and TPP ranged from −1.42 to +1.54, −1.14 to +2.98, and −0.62 to +1.60 and −0.12 to +1.58, respectively. (Table S2), while the Z score of 222 selected mutant lines of Pb-11 for DTH, SL, PH and TPP was in the range of −1.65 to +1.36, −1.97 to +2.12, −1.46 to +2.38 and −1.82 to +1.40, respectively (Table S3).

3.4. Comparison of the Selected Mutant Lines with the Wild-Type Parents

3.4.1. Days to Heading

In total, nine M7 and 14 M7 lines were selected from 17 M7 and 222 M7 mutant lines of NN-1 and Pb-11 for DTH, respectively. The mean of selected lines of NN-1 for DTH was 93.05 days versus 89 days for the wild type (Table 3). However, only two lines, i.e., “#701” and “#1621” started flowering at 85 and 88 days after germination, respectively (Table S2). For Pb-11, the mean of the selected mutant lines for DTH was 92.06 days versus 92 days for the wild type (Table 3). A total of 12 lines (P-684, P-3339, P-646, P-539, P-572, P-21, P-685, P-3078, P-3080, P-3081, P-3243 and P-3326) headed two days earlier than the wild type (Table S3).

3.4.2. Spike Length

In total, 12 M7 and 81 M7 lines were selected from 17 M7 and 222 M7 mutant lines of NN-1 and Pb-11, respectively. For NN-1, the mean SL of the selected lines was 9.81 cm versus 9.25 cm for the wild type (Table 3). Among these, the SL of three lines (i.e., 121, 1621, and 1929) were 11.45 cm, 11.45 cm and 12.25 cm versus 9.25 cm for the wild type, respectively (Table S2). For the mutant lines derived from Pb-11, the mean SL of the selected mutant lines was 9.09 cm while the wild type was 8.86 cm (Table 3). Mean SLs expressed by mutant lines P-684, P-685 and P-3077 were 11.25 cm, 11.69 cm and 11.58 cm, respectively (Table S3).

3.4.3. Plant Height

A total of 12 M7 and 137 M7 lines were selected from 17 M7 and 222 M7 mutant lines of NN-1 and Pb-11, respectively. Mean PH of the selected NN-1 mutant lines was 90.24 cm versus 89.26 cm for the wild type (Table 3). Two lines (1621 and 1875) were 95.65 cm and 92.23 cm tall, respectively (Table S2). For Pb-11, the PH mean of the selected mutant lines was 88.37 cm versus 87.65 cm for the wild type (Table 3). Among these, three mutant lines (P-684, P-3144 and P-3163) were 95.65 cm, 95.45 cm and 94.65 cm tall, respectively (Table S3).

3.4.4. Tillers per Plant

In total, six M7 mutant lines of NN-1 and 52 M7 mutant lines of Pb-11 were selected. For NN-1, the mean of selected mutant lines for TPP was 5.23 tillers versus 5.0 TPP for wild type (Table 3) or 16.66% greater than the control lines. A total of three lines (284, 320 and 1621) produced six TPP each (Table S2). For Pb-11, the mean of selected lines was 4.68 TPP versus four for the wild type (Table 3). As a result, lines P-684, P-3144, P-3175, P-3176, P-3263, P-3192, P-3231, P-3279, P-2943, P-3146 and P-3215 produced higher number of tillers (5) versus four for the wild type (Table S3).

3.5. Correlation among Traits of the Selected Mutant Lines

Mutant lines derived from NN-1 showed a significant positive correlation between DTH and PH, and strong positive correlation with SL and TPP, whereas mutant lines of Pb-11 showed a strong positive correlation of PH with SL and TPP. In both mutant populations, SL showed a highly significant positive correlation with TPP (Figure 3).

3.6. Mutagen Effectiveness

Mutagenic efficiency of EMS and γ rays was compared for both the mutant populations. We found that EMS generated 10.47% more resistant/susceptible LR variants compared to γ rays, while γ rays produced an 8.09% greater number of mutants conferring resistance/susceptibility to YR than EMS. For other traits such as PH, SL and DTH, γ rays were 15.55%, 8.70% and 2.32% more effective than EMS, respectively. While in the case of TPP, EMS generated 3.66% more mutants than γ rays. Cumulatively, γ rays produced 34.66% more mutants for all the studied traits (Table 4).

3.7. Heritability

In the present study, a high heritability for NN-Gandum-1 was observed for the characters like plant height (91.66%) and spike length (89.62%) in all three generations (Figure 4). Across all the three generations, the lowest heritability was observed in leaf and yellow rust disease of 21.92% and 27.98%, respectively (Figure 4).
Similarly, high heritability for Punjab-11 was observed for characters such as plant height, e.g., 98.54% in M5 generation, 97.00% in M6 generation and 99.50% in M7 generation, followed by spike length (96.21% in M5, 94.23% in M6 and 92.3% in M7 generations) (Figure 4). Across all the three generations, the highest heritability was observed in plant height (98.35%) and the lowest in leaf rust disease (20.63%) (Figure 4).

4. Discussion

A successful breeding program is largely driven by the genetic diversity found in the germplasm which can be utilized for transferring desirable genes/alleles into the improved cultivars. There are several ways of improving the genetic variability of a given germplasm including collection of new genetic resources from the natural habitats, inducing mutations in germplasm through artificial means, and using modern genomic tools for adding alien genes and editing genes. In the present study, we used a noncontroversial procedure for inducing mutations in wheat genome using chemical as well as physical mutagens. Success of the mutation breeding in creating genotypic as well as phenotypic diversity was demonstrated by developing a first mutant variety of tobacco “Chlorina” [30]. Later on, approximately 3365 crop varieties were released using various mutagens (https://mvd.iaea.org accessed 28 June 2021).
In the present study, 3706 and 3700 M1 plants were generated of both mutagenized populations. It is difficult to estimate the size of a mutant population for launching a successful mutation breeding program. Previously, a varying number of M1/M2 plants were generated even for the same crop species. For example, M2 populations of T. aestivum of the sizes 1532, 1610, 2296 and 3100 plants were developed for identifying desirable mutants [31,32,33,34]. In multiple mutagenesis experiments, M2 population sizes ranging from 1632 to 4568 in O. sativa were reported [35,36,37,38]. Similarly, 4185 and 9575 M2 plants were developed for S. bicolor and H. vulgare, respectively [39,40].
Here, we assessed >3500 lines of NN-1 and Pb-11 populations in three consecutive generations, viz., M5-M7 in three years. For NN-1, the frequency of phenotypic variation was 22.73%. In multiple investigations, 0.2 to 38.1% phenotypic variants were reported in different wheat mutant populations developed by exposure to EMS [41,42,43,44]. In other crop species, mutant populations developed through exposure to chemical mutagens resulted in several mutants. For instance, 33% variants in barley [45], 50% variants in rice [46] and 5% variants in sunflower mutant populations were reported [47]. Chemical mutagens usually induce point mutations, and a few impact the plant phenotype [48].
The other class of mutagens, i.e., physical mutagens, tend to induce larger deletions in genomes, and thus can easily be scored. Here, the proportion of mutants were 26.18% for the mutant population derived from Pb-11. In several investigations, fluctuations in the frequency of mutants were reported. For example, in a wheat mutant population developed through exposing with γ-radiation, a wide range of phenotypic variants for M2 (9.59–11.86%) and its M3 (4–4.87%) populations were reported [48]. Similarly, 8.35–44% phenotypic variations were identified in an M3 Brachypodium mutant population developed through γ-radiation [45]. These variations in the frequency of mutants may be attributed to the type of genotype, dose of a mutagen, and several environmental factors [48,49].
The mutagenized populations (NN-1 and Pb-11) showed extensive diversity in morphological and agronomic traits as compared to their respective parent genotypes (wild type). In M5 versus M6 of the selected mutant lines of NN-1 (in total 17) and Pb-11 (in total 222), days to heading (DTH) showed a significant positive correlation with plant height (PH), spike length (SL) and tillers per plant (TPP), while, in Pb-11-derived mutant lines, PH showed a strong significant (p < 0.0001) positive correlation with SL and TPP. In both mutant populations, SL showed a strong significant positive correlation with TPP, while LR and YR were strongly correlated with each other and negatively with the other traits. Similar results were found in M5 versus M7 and M6 versus M7. In both mutant populations, correlation with the replicated M7 generation data was greater for M6 than M5 for all traits. Strong positive correlation coefficients revealed that selection based on PH, SL, TPP, LR, YR and DTH had an equal contribution towards increasing yield [50]. It was also reported that variation in correlation among different generations for all traits was because of segregation of the particular genes, environmental factors between different years and locations [51].
One of the major focuses of our study was to improve resistance to LR and YR in NN-1 and Pb-11, and, therefore, the plants were selected on the basis of their response to LR and YR. These resistant mutants can be used as varieties and or breeding material. Several wheat mutants were produced through mutagenesis in several countries including Pakistan, Ukraine, Russia, Kazakhstan, India, and China [14,52,53]. Previously, similar methods were adopted in cotton to select mutant lines showing improved fiber quality. Significant improvement in various quality traits, including micronaire, upper half mean fiber length, length uniformity index, bundle strength, reflectance, elongation, and yellowness were reported [51] by using various statistical tests, e.g., Z-scores.
We selected lines for one trait that also showed other desirable attributes contributing towards final yield. Improved wheat yield, the ultimate objective of all breeding programs, is heavily dependent upon various factors, including tillering capacity, heading, spike length, number of spikes per unit area, grain size and number. In the present study, 9 of 17, and 14 of 222 mutant lines were selected from the mutant populations of NN-1 and Pb-11 for DTH, respectively. The DTH varied from 88 to 97 days for NN-1 and 89 to 95 days for Pb-11. This trait is highly heritable and variable [50]. Similarly, high variability for DTH (13.23%) was reported in the M2 wheat mutant population [54]. In rice, DTH of the mutant population varied from 72 to 99 days and 76 to 112 days in another study under natural day length conditions [55,56,57].
Spike length (SL) is one of the most important traits which helps in accommodating a greater number of spikelets per spike. Of the selected mutant lines, in total 12 out of 17 mutant lines of NN-1 (7.25 to 15.65 cm) and 81 out of 222 mutant lines of Pb-11 (7.28 to 14.56 cm) expressed variations in SL. In another study, mean SLs of the selected 40 wheat mutant lines under normal and late planting were 12 and 10 cm when treated with EMS, respectively [54]. Differences in SL were also reported in other studies on wheat [54,58,59,60]. Similar kinds of variations were reported in rice panicle, barley spike, and sorghum tassel when treated with chemical mutagens [38,39,61].
A total of 12 out of 17 (70.5%) mutant lines derived from NN-1 and 137 out of 222 (21.62%) mutant lines of Pb-11 exhibiting variations for PH were selected. Variation in PH ranged from 75.25 to 102.36 cm for NN-1 and 83.54 to 96.65 cm for Pb-11. In previous studies, a 5–10% reduction in PH was observed [50,54,59,60,62]. Particularly, the highest variability was recorded for PH (69.90%) in a wheat mutant population [54].
The number of TPPs may impact final yield positively or negatively, largely depending upon the prevailing environment and inputs. Under high input environments, the number of productive tillers per unit area of the same genotype will be higher than that of the tillers produced under low input environments. Here, we selected in total, six out of 17 and 52 out of 222 mutant lines exhibiting variations for TPP from NN-1 and Pb-11, respectively. In previous reports, increase in TPP were reported in wheat M2 population [54].
Mutagenic efficiency is the proportion of the desirable mutation frequency in relation to damage associated with mutation [63]. It is dependent upon the type of mutagen, genotype and/or both [64]. In the present study, cumulatively, the γ rays-treated population produced 3.43% more mutants for all the traits than the EMS population. In support of this, γ rays produced higher number of chlorophyll mutants than in an EMS population [65]. In tomato, it was reported that γ rays (50–150 Gy) proved to be a more efficient and effective mutagen when followed by 0.05% to 10% EMS treatment [63]. In soybean, results indicated that three mutant populations i.e., two γ radiated (0.20 and 0.25 kGy) and one EMS (0.1%), showed good seedling emergence; while two of them viz., IR-JS-101 (0.25 KGy) and IR-DS-122 (0.20 KGy) performed better than EMS [66]. In a study on J. curcas, seeds treated with 5 Kr dose of γ rays, and 1% EMS showed a similar effect, while a 25 Kr dose of γ rays and 4% EMS treatment showed an inhibitory effect for all the characters studied, except seed germination, when compared to other treatments. The results concluded that treatments of γ rays were greater compared to those of EMS treatments [67]. Some findings, however, are not consistent with the above-mentioned reports. For example, high mutagenesis was reported in sesame (S. indicum L.) when treated with EMS, i.e., the average effectiveness of EMS was several times higher than that of γ rays [68]. Additionally, EMS was found to be seven times more effective in chickpea than γ rays [69]. In other crops, it was reported that EMS is better at producing useful mutations than γ rays, e.g., as seen in rice [70], lentil [71], and soybean [72]. Similarly, it was also found that EMS-mediated mutagenesis is more amenable to induce mutations in the wheat genome [73]. Hence it is concluded that genotype, as well as type of mutagen and the interaction of each, contribute towards fluctuation in mutagenic efficacy of the mutagen.
Due to their high heritability and correlation with grain yield, agronomic traits can be used as indirect selection criteria during breeding and cultivar development [32]. Though heritability of physiological traits is relatively low [32,74], their incorporation in breeding programs may be useful for cultivar development [75]. Therefore, to accelerate breeding aimed at improving grain yield genetic gains in wheat, it is important to dissect genomic regions influencing physiological traits and design associated markers for strategic breeding.

5. Conclusions

Multiple generations and replicated trials and treatments with different mutagens confirmed the genetic basis of the variations observed in different traits. These mutants were explored for variations in different rust resistant genes. For example, 104,779 SNPs were detected in 11 selected mutant lines in exome regions. Additionally, genetics of the observed phenotypic variations in several mutants were explored at length using TILLING, resequencing and/or by exome capturing [48]. The identified discrete genetic variations (alleles) can be introgressed into the cultivated wheat varieties using conventional breeding approaches, as well as by designing DNA markers. It may be concluded that γ ray mutagenesis can effectively be utilized in the development of desirable economical and quality traits along with some degree of tolerance against biotic stresses in wheat. The extent of mutations in the genome can also be estimated by resequencing and or sequencing the exonic regions of the wheat genome. The newly developed mutants are expected to be useful to provide insights into the genetics of several biochemical mechanisms, as well as identifying genes/alleles involved in controlling important traits in cultivated hexaploid wheat.

Supplementary Materials

The following are available online at https://www.mdpi.com/article/10.3390/agriculture11070621/s1, Table S1: Summary of all traits- year wise for both mutant population, Table S2: Z-scores of the selected lines of NN-Gandum-1 mutagenized population, Table S3: Z-scores of the selected lines of Punjab-11 mutagenized population.

Author Contributions

Conceptualization, M.-u.-R.; methodology, M.-u.-R. and Q.H.M.; formal analysis, M.H., R.K. and Q.H.M.; data curation, M.H., M.G., M.A.I., A.A. and S.Z.; writing—original draft preparation, M.H., M.-u.-R. and Q.H.M.; writing—review and editing, Q.H.M., R.K., S.Z., M.-u.-R. and M.S.R.; visualization, R.K., M.H. and Q.H.M.; funding acquisition, M.-u.-R. All authors have read and agreed to the published version of the manuscript.

Funding

The funds for the development of mutant wheat populations were provided by the International Atomic Energy Commission (IAEA), Vienna, Austria through an umbrella project entitled “Developing Germplasm through TILLING in Crop Plants Using Mutation and Genomic Approaches (PAK/5/047). These activities were continued through the support of another project entitled “Characterization of mutants derived from EMS-derived Gandum-1 for rust and drought tolerance for sustaining wheat yield in Pakistan, funded by Agriculture Linkage Program, Pakistan Agriculture Research Council (ALP-PARC, CS049). We are also grateful to the Higher Education Commission (HEC), Islamabad Pakistan for providing a part of the research work through a project entitled “Identification of disease and insect mutant using SSR and next generation sequencing tools (6103-NRPU/R&D/HEC-2016).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data is contained within the article and Supplementary Files.

Acknowledgments

The funds for the development of mutant wheat populations were provided by the International Atomic Energy Commission (IAEA), Vienna, Austria through an umbrella project entitled “Developing Germplasm through TILLING in Crop Plants Using Mutation and Genomic Approaches (PAK/5/047). These activities were continued through the support of another project entitled “Characterization of mutants derived from EMS-derived Gandum-1 for rust and drought tolerance for sustaining wheat yield in Pakistan, funded by Agriculture Linkage Program, Pakistan Agriculture Research Council (ALP-PARC, CS049). We are also grateful to the Higher Education Commission (HEC), Islamabad Pakistan for providing financial support to a part of the present research work through a project entitled “Identification of disease and insect mutant using SSR and next generation sequencing tools (6103-NRPU/R&D/HEC-2016).

Conflicts of Interest

The authors declare no conflict of interest. The funding agencies had no role in the design of the experiments, in the collection, analyses or interpretation of the data, in the writing of the manuscript or in the decision to publish the results. Q.H.M. is presently a member of a company. However, this does not limit the availability or sharing of data and material.

References

  1. Arumuganathan, K.; Earle, E. Nuclear DNA content of some important plant species. Plant. Mol. Biol Rep. 1991, 9, 208–218. [Google Scholar] [CrossRef]
  2. Marcussen, T.; Sandve, S.R.; Heier, L.; Spannagl, M.; Pfeifer, M.; Jakobsen, K.S.; Wulff, B.B.; Steuernagel, B.; Mayer, K.F.; Olsen, O.-A. Ancient hybridizations among the ancestral genomes of bread wheat. Science 2014, 345, 6194. [Google Scholar] [CrossRef] [PubMed]
  3. Conway, G. The Doubly Green Revolution: Food for all in the Twenty-First Century; Cornell University Press: Ithaca, NY, USA, 1998. [Google Scholar]
  4. Mukhtar, S.; Rahman, M.; Zafar, Y. Assessment of genetic diversity among wheat cultivars using random amplified polymorphic DNA (RAPD) analysis. Euphytica 2002, 128, 417–425. [Google Scholar] [CrossRef]
  5. Rahman, M.; Malik, T.; Chowdhary, M.; Iqbal, M.; Zafar, Y. Application of random amplified polymorphic DNA (RAPD) technique for the identification of markers linked to salinity tolerance in wheat (Triticum aestivum L.). Pak. J. Bot. 2004, 36, 595–602. [Google Scholar]
  6. Malik, R.; Tiwari, R.; Arora, A.; Kumar, P.; Sheoran, S.; Sharma, P.; Singh, R.; Tiwari, V.; Sharma, I. Genotypic characterization of elite Indian wheat genotypes using molecular markers and their pedigree analysis. Aust. J. Crop. Sci. 2013, 7, 561. [Google Scholar]
  7. Huerta-Espino, J.; Singh, R.; German, S.; McCallum, B.; Park, R.; Chen, W.Q.; Bhardwaj, S.; Goyeau, H. Global status of wheat leaf rust caused by Puccinia triticina. Euphytica 2011, 179, 143–160. [Google Scholar] [CrossRef]
  8. Beddow, J.M.; Pardey, P.G.; Chai, Y.; Hurley, T.M.; Kriticos, D.J.; Braun, H.-J.; Park, R.F.; Cuddy, W.S.; Yonow, T. Research investment implications of shifts in the global geography of wheat stripe rust. Nat. Plants 2015, 1, 15132. [Google Scholar] [CrossRef]
  9. Vergara-Diaz, O.; Kefauver, S.C.; Elazab, A.; Nieto-Taladriz, M.T.; Araus, J.L. Grain yield losses in yellow-rusted durum wheat estimated using digital and conventional parameters under field conditions. Crop. J. 2015, 3, 200–210. [Google Scholar] [CrossRef] [Green Version]
  10. Acevedo, M.; Zurn, J.D.; Molero, G.; Singh, P.; He, X.; Aoun, M.; Juliana, P.; Bockleman, H.; Bonman, M.; El-Sohl, M. The role of wheat in global food security. In Agricultural Development and Sustainable Intensification; Routledge: London, UK, 2018; pp. 81–110. [Google Scholar]
  11. Miedaner, T.; Schmid, J.E.; Flath, K.; Koch, S.; Jacobi, A.; Ebmeyer, E.; Taylor, M. A multiple disease test for field-based phenotyping of resistances to Fusarium head blight, yellow rust and stem rust in wheat. Eur. J. Plant. Pathol. 2018, 151, 451–461. [Google Scholar] [CrossRef]
  12. Barua, P.; You, M.; Bayliss, K.; Lanoiselet, V.; Barbetti, M. Extended survival of Puccinia graminis f. sp. tritici urediniospores: Implications for biosecurity and on-farm management. Plant. Pathol. 2018, 67, 799–809. [Google Scholar] [CrossRef]
  13. Hovmøller, M.S.; Walter, S.; Bayles, R.A.; Hubbard, A.; Flath, K.; Sommerfeldt, N.; Leconte, M.; Czembor, P.; Rodriguez-Algaba, J.; Thach, T. Replacement of the European wheat yellow rust population by new races from the centre of diversity in the near-Himalayan region. Plant. Pathol. 2016, 65, 402–411. [Google Scholar] [CrossRef] [Green Version]
  14. Riaz, A.; Athiyannan, N.; Periyannan, S.K.; Afanasenko, O.; Mitrofanova, O.P.; Platz, G.J.; Aitken, E.A.; Snowdon, R.J.; Lagudah, E.S.; Hickey, L.T. Unlocking new alleles for leaf rust resistance in the Vavilov wheat collection. Theor. Appl. Genet. 2018, 131, 127–144. [Google Scholar] [CrossRef] [PubMed]
  15. Wallace, J. Increasing agricultural water use efficiency to meet future food production. Agric. Ecosyst. Environ. 2000, 82, 105–119. [Google Scholar] [CrossRef]
  16. Skovmand, B.; Reynolds, M.; Delacy, I. Searching genetic resources for physiological traits with potential for increasing yield. In Application of Physiology in Wheat Breeding; CIMMYT: Mexico City, Mexico, 2001; pp. 17–28. [Google Scholar]
  17. Eynard, A.; Lal, R.; Wiebe, K. Crop response in salt-affected soils. J. Sustain. Agric. 2005, 27, 5–50. [Google Scholar] [CrossRef]
  18. Munns, R.; Tester, M. Mechanisms of salinity tolerance. Annu. Rev. Plant. Biol 2008, 59, 651–681. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  19. Roy, S.J.; Negrão, S.; Tester, M. Salt resistant crop plants. Curr. Opin. Biotechnol. 2014, 26, 115–124. [Google Scholar] [CrossRef]
  20. Krasileva, K.V.; Vasquez-Gross, H.A.; Howell, T.; Bailey, P.; Paraiso, F.; Clissold, L.; Simmonds, J.; Ramirez-Gonzalez, R.H.; Wang, X.; Borrill, P. Uncovering hidden variation in polyploid wheat. Proc. Natl. Acad. Sci. USA 2017, 114, E913–E921. [Google Scholar] [CrossRef] [Green Version]
  21. Wang, W.; Pan, Q.; He, F.; Akhunova, A.; Chao, S.; Trick, H.; Akhunov, E. Transgenerational CRISPR-Cas9 activity facilitates multiplex gene editing in allopolyploid wheat. CRISPR J. 2018, 1, 65–74. [Google Scholar] [CrossRef] [Green Version]
  22. Muqaddasi, Q.H.; Brassac, J.; Koppolu, R.; Plieske, J.; Ganal, M.W.; Röder, M.S. TaAPO-A1, an ortholog of rice ABERRANT PANICLE ORGANIZATION 1, is associated with total spikelet number per spike in elite European hexaploid winter wheat (Triticum aestivum L.) varieties. Sci. Rep. 2019, 9, 13853. [Google Scholar] [CrossRef] [Green Version]
  23. Bhat, R.S.; Upadhyaya, N.M.; Chaudhury, A.; Raghavan, C.; Qiu, F.; Wang, H.; Wu, J.; McNally, K.; Leung, H.; Till, B. Chemical-and irradiation-induced mutants and TILLING. In Rice Functional Genomics; Springer: Berlin/Heidelberg, Germany, 2007; pp. 148–180. [Google Scholar]
  24. Ponce-Molina, L.J.; Huerta-Espino, J.; Singh, R.P.; Basnet, B.R.; Lagudah, E.; Aguilar-Rincón, V.H.; Alvarado, G.; Lobato-Ortiz, R.; García-Zavala, J.; Lan, C. Characterization of adult plant resistance to leaf rust and stripe rust in Indian wheat cultivar ‘New Pusa 876’. Crop. Sci. 2018, 58, 630–638. [Google Scholar] [CrossRef]
  25. Brini, F.; Masmoudi, K. Biotechnology for drought and salinity tolerance of crops. In Physiological Mechanisms and Adaptation Strategies in Plants Under Changing Environment; Springer: Berlin/Heidelberg, Germany, 2014; pp. 97–113. [Google Scholar]
  26. Dumanovć, J.; Denić, M.; Ehrenberg, L.; Bergstrand, K. Radiation-induced heritable variation of quantiative characters in wheat. Hereditas 1969, 62, 221–238. [Google Scholar] [CrossRef]
  27. Naito, K.; Kusaba, M.; Shikazono, N.; Takano, T.; Tanaka, A.; Tanisaka, T.; Nishimura, M. Transmissible and nontransmissible mutations induced by irradiating Arabidopsis thaliana pollen with γ-rays and carbon ions. Genetics 2005, 169, 881–889. [Google Scholar] [CrossRef] [Green Version]
  28. Tsai, H.; Missirian, V.; Ngo, K.J.; Tran, R.K.; Chan, S.R.; Sundaresan, V.; Comai, L. Production of a high-efficiency TILLING population through polyploidization. Plant. Physiol. 2013, 161, 1604–1614. [Google Scholar] [CrossRef] [Green Version]
  29. Peterson, R.F.; Campbell, A.; Hannah, A. A diagrammatic scale for estimating rust intensity on leaves and stems of cereals. Can. J. Res. 1948, 26, 496–500. [Google Scholar] [CrossRef]
  30. Tollenaar, D. Untersuchungen ueber Mutation bei Tabak. Genetica 1934, 16, 111–152. [Google Scholar] [CrossRef]
  31. Chawade, A.; Sikora, P.; Bräutigam, M.; Larsson, M.; Vivekanand, V.; Nakash, M.A.; Chen, T.; Olsson, O. Development and characterization of an oat TILLING-population and identification of mutations in lignin and β-glucan biosynthesis genes. BMC Plant. Biol. 2010, 10, 86. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  32. Chen, L.; Huang, L.; Min, D.; Phillips, A.; Wang, S.; Madgwick, P.J.; Parry, M.A.; Hu, Y.-G. Development and characterization of a new TILLING population of common bread wheat (Triticum aestivum L.). PLoS ONE 2012, 7, e41570. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  33. Rawat, N.; Sehgal, S.K.; Joshi, A.; Rothe, N.; Wilson, D.L.; McGraw, N.; Vadlani, P.V.; Li, W.; Gill, B.S. A diploid wheat TILLING resource for wheat functional genomics. BMC Plant. Biol. 2012, 12, 205. [Google Scholar] [CrossRef] [Green Version]
  34. Joshi, A. Map-Based Cloning of the Hessian Fly Resistance Gene H13 in Wheat. 2018. Available online: https://www.proquest.com/openview/683f08ff0d8dd44fb6ebdf4a5ed95c0a/1?pq-origsite=gscholar&cbl=18750 (accessed on 1 July 2021).
  35. Chen, Y.L.; Liang, H.L.; Ma, X.L.; Lou, S.L.; Xie, Y.Y.; Liu, Z.L.; Chen, L.T.; Liu, Y.G. An Efficient Rice Mutagenesis System Based on Suspension-Cultured Cells. J. Integr. Plant. Biol. 2013, 55, 122–130. [Google Scholar] [CrossRef]
  36. Dorosti, H.; Zandi, P.; Basu, S.K.; Chalaras, S.K. Ethyl methane sulfonate and its effect on mutagenesis in rice crop improvement. In Proceedings of the VII International Scientific Agriculture Symposium, "Agrosym 2016", Jahorina, Bosnia and Herzegovina, 6–9 October 2016; pp. 1215–1220. [Google Scholar]
  37. Bora, A.; Choudhury, P.R.; Pande, V.; Mandal, A.B. Assessment of genetic purity in rice (Oryza sativa L.) hybrids using microsatellite markers. 3 Biotech 2016, 6, 50. [Google Scholar] [CrossRef] [Green Version]
  38. Burkart-Waco, D.; Tsai, H.; Ngo, K.; Henry, I.M.; Comai, L.; Tai, T.H. Next-generation sequencing for targeted discovery of rare mutations in rice. In Biotechnologies for Plant Mutation Breeding; Springer: Cham, Switzerland, 2017; pp. 323–340. [Google Scholar]
  39. Jiao, Y.; Burke, J.; Chopra, R.; Burow, G.; Chen, J.; Wang, B.; Hayes, C.; Emendack, Y.; Ware, D.; Xin, Z. A sorghum mutant resource as an efficient platform for gene discovery in grasses. Plant. Cell 2016, 28, 1551–1562. [Google Scholar] [CrossRef] [Green Version]
  40. Lababidi, S.; Mejlhede, N.; Rasmussen, S.K.; Backes, G.; Al-Said, W.; Baum, M.; Jahoor, A. Identification of barley mutants in the cultivar ‘Lux’at the Dhn loci through TILLING. Plant. Breed. 2009, 128, 332–336. [Google Scholar] [CrossRef]
  41. Caldwell, D.G.; McCallum, N.; Shaw, P.; Muehlbauer, G.J.; Marshall, D.F.; Waugh, R. A structured mutant population for forward and reverse genetics in Barley (Hordeum vulgare L.). Plant. J. 2004, 40, 143–150. [Google Scholar] [CrossRef]
  42. Slade, A.J.; Knauf, V.C. TILLING moves beyond functional genomics into crop improvement. Transgenic Res. 2005, 14, 109–115. [Google Scholar] [CrossRef]
  43. Rakszegi, M.; Kisgyörgy, B.; Tearall, K.; Shewry, P.; Láng, L.; Phillips, A.; Bedő, Z. Diversity of agronomic and morphological traits in a mutant population of bread wheat studied in the Healthgrain program. Euphytica 2010, 174, 409–421. [Google Scholar] [CrossRef]
  44. Guo, J.; Shi, W.; Zhang, Z.; Cheng, J.; Sun, D.; Yu, J.; Li, X.; Guo, P.; Hao, C. Association of yield-related traits in founder genotypes and derivatives of common wheat (Triticum aestivum L.). BMC Plant. Biol. 2018, 18, 38. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  45. Talamè, V.; Bovina, R.; Sanguineti, M.C.; Tuberosa, R.; Lundqvist, U.; Salvi, S. TILLMore, a resource for the discovery of chemically induced mutants in barley. Plant. Biotechnol. J. 2008, 6, 477–485. [Google Scholar] [CrossRef] [PubMed]
  46. Suzuki, T.; Eiguchi, M.; Kumamaru, T.; Satoh, H.; Matsusaka, H.; Moriguchi, K.; Nagato, Y.; Kurata, N. MNU-induced mutant pools and high performance TILLING enable finding of any gene mutation in rice. Mol. Genet. Genom. 2008, 279, 213–223. [Google Scholar] [CrossRef] [PubMed]
  47. Sabetta, W.; Alba, V.; Blanco, A.; Montemurro, C. sunTILL: A TILLING resource for gene function analysis in sunflower. Plant. Methods 2011, 7, 20. [Google Scholar] [CrossRef] [Green Version]
  48. Hussain, M.; Iqbal, M.A.; Till, B.J.; Rahman, M.-U. Identification of induced mutations in hexaploid wheat genome using exome capture assay. PLoS ONE 2018, 13, e0201918. [Google Scholar] [CrossRef] [Green Version]
  49. Cheng, X.; Chai, L.; Chen, Z.; Xu, L.; Zhai, H.; Zhao, A.; Peng, H.; Yao, Y.; You, M.; Sun, Q. Identification and characterization of a high kernel weight mutant induced by gamma radiation in wheat (Triticum aestivum L.). BMC Genet. 2015, 16, 127. [Google Scholar] [CrossRef] [Green Version]
  50. Mohsin, T.; Khan, N.; Naqvi, F.N. Heritability, phenotypic correlation and path coefficient studies for some agronomic characters in synthetic elite lines of wheat. J. Food Agric. Environ. 2009, 7, 278–282. [Google Scholar]
  51. Patel, J.D.; Wright, R.J.; Auld, D.; Chandnani, R.; Goff, V.H.; Ingles, J.; Pierce, G.J.; Torres, M.J.; Paterson, A.H. Alleles conferring improved fiber quality from EMS mutagenesis of elite cotton genotypes. Theor. Appl. Genet. 2014, 127, 821–830. [Google Scholar] [CrossRef]
  52. Gurung, S.; Mamidi, S.; Bonman, J.M.; Xiong, M.; Brown-Guedira, G.; Adhikari, T.B. Genome-wide association study reveals novel quantitative trait loci associated with resistance to multiple leaf spot diseases of spring wheat. PLoS ONE 2014, 9, e108179. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  53. Maccaferri, M.; Zhang, J.; Bulli, P.; Abate, Z.; Chao, S.; Cantu, D.; Bossolini, E.; Chen, X.; Pumphrey, M.; Dubcovsky, J. A genome-wide association study of resistance to stripe rust (Puccinia striiformis f. sp. tritici) in a worldwide collection of hexaploid spring wheat (Triticum aestivum L.). G3-Genes Genom. Genet. 2015, 5, 449–465. [Google Scholar]
  54. Hassani, I.; Marker, S.; Lal, G. Inter-relationship studies among grain yield and its component characters in wheat (Triticum aestivum L.). J. Pharmacog. Phytochem. 2017, 6, 186–191. [Google Scholar]
  55. Koo, B.-H.; Yoo, S.-C.; Park, J.-W.; Kwon, C.-T.; Lee, B.-D.; An, G.; Zhang, Z.; Li, J.; Li, Z.; Paek, N.-C. Natural variation in OsPRR37 regulates heading date and contributes to rice cultivation at a wide range of latitudes. Mol. Plant. 2013, 6, 1877–1888. [Google Scholar] [CrossRef] [Green Version]
  56. Hori, K.; Nonoue, Y.; Ono, N.; Shibaya, T.; Ebana, K.; Matsubara, K.; Ogiso-Tanaka, E.; Tanabata, T.; Sugimoto, K.; Taguchi-Shiobara, F. Genetic architecture of variation in heading date among Asian rice accessions. BMC Plant. Biol 2015, 15, 115. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  57. Yasmine, F.; Ullah, M.A.; Ahmad, F.; Rahman, M.A.; Harun, A.R. Effects of chronic gamma irradiation on three rice varieties. J. Sains Nukl. Malays. 2019, 31, 1–10. [Google Scholar]
  58. Kakar, K.; Kakar, Z.; Shawani, M. Varietal dynamics of yield stability in wheat. Online J. Biol. Sci. 2003, 3, 137–140. [Google Scholar]
  59. Adnan, M.; Muhammad, F.; Hussain, Q.; Hussain, I.; Ali, F. Heritability estimates and correlation analysis in bread wheat (Triticum aestivum L.) under normal and late plantings. Pure Appl. Biol. 2017, 6, 1151–1160. [Google Scholar] [CrossRef]
  60. Iqbal, A.; Khalil, I.H.; Shah, S.; Kakar, M.S. Estimation of heritability, genetic advance and correlation for morphological traits in spring wheat. Sarhad J. Agric. 2017, 33, 674–679. [Google Scholar]
  61. Szurman-Zubrzycka, M.E.; Zbieszczyk, J.; Marzec, M.; Jelonek, J.; Chmielewska, B.; Kurowska, M.M.; Krok, M.; Daszkowska-Golec, A.; Guzy-Wrobelska, J.; Gruszka, D. HorTILLUS—A rich and renewable source of induced mutations for forward/reverse genetics and pre-breeding programs in barley (Hordeum vulgare L.). Front. Plant. Sci. 2018, 9, 216. [Google Scholar] [CrossRef]
  62. Singh, N.; Balyan, H. Induced mutations in bread wheat (Triticum aestivum L.) CV.‘Kharchia 65’for reduced plant height and improve grain quality traits. Adv. Biol. Res. 2009, 3, 215–221. [Google Scholar]
  63. Sikder, S.; Ravat, V.; Basfore, S.; Hazra, P. Isolation of induced mutants using gamma ray and ethyl methane sulphonate in Tomato (Solanum lycopersicum L.). Electron. J. Plant. Breed. 2015, 6, 464–471. [Google Scholar]
  64. Banjare, C. Induction of Mutation through Physical and Chemical Mutagens and Characterization of Muatant Lines in Garlic (Allium sativum L.). Ph.D. Thesis, Indira Gandhi Krishi Vishwavidhyalaya, Raipur, India, 2017. [Google Scholar]
  65. Devi, A.S.; Mullainathan, L. Physical and chemical mutagenesis for improvement of chilli (Capsicum annuum L.). World Appl. Sci. J. 2011, 15, 108–113. [Google Scholar]
  66. Kumar, P.; Singh, G.; Kumar, S.; Kumar, A.; Ojha, A. Genetic analysis of grain yield and its contributing traits for their implications in improvement of bread wheat cultivars. J. Appl. Nat. Sci. 2016, 8, 350–357. [Google Scholar] [CrossRef]
  67. Dhakshanamoorthy, D.; Selvaraj, R.; Chidambaram, A. Physical and chemical mutagenesis in Jatropha curcas L. to induce variability in seed germination, growth and yield traits. Rom. J. Biol. Plant. Biol. 2010, 55, 113–125. [Google Scholar]
  68. Begum, T.; Dasgupta, T. A comparison of the effects of physical and chemical mutagens in sesame (Sesamum indicum L.). Genet. Mol. Biol. 2010, 33, 761–766. [Google Scholar] [CrossRef]
  69. Shah, T.M.; Mirza, J.I.; Haq, M.A.; Atta, B.M. Induced genetic variability in chickpea (Cicer arietinum L.). II. Comparative mutagenic effectiveness and efficiency of physical and chemical mutagens. Pak. J. Bot. 2008, 40, 605–613. [Google Scholar]
  70. Kaul, M.; Bhan, A. Mutagenic effectiveness and efficiency of EMS, DES and gamma-rays in rice. Theor. Appl. Genet. 1977, 50, 241–246. [Google Scholar] [CrossRef] [PubMed]
  71. Solanki, I.; Sharma, B. Mutagenic effectiveness and efficiency of gamma rays, ethylene imine and N-nitroso-N-ethyl urea in macrosperma lentil (Lens culinaris Medik.). Indian J. Genet. Plant. Breed. 1994, 54, 72–76. [Google Scholar]
  72. Khan, M.H.; Tyagi, S.D. Studies on effectiveness and efficiency of gamma rays, EMS and their combination in soybean [Glycine max (L.) Merrill.]. J. Plant. Breed. Crop. Sci. 2010, 2, 055–058. [Google Scholar]
  73. Sikora, P.; Chawade, A.; Larsson, M.; Olsson, J.; Olsson, O. Mutagenesis as a tool in plant genetics, functional genomics, and breeding. Int. J. Plant. Genom. 2011, 2011, 314829. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  74. Ogbonnaya, F.C.; Rasheed, A.; Okechukwu, E.C.; Jighly, A.; Makdis, F.; Wuletaw, T.; Hagras, A.; Uguru, M.I.; Agbo, C.U. Genome-wide association study for agronomic and physiological traits in spring wheat evaluated in a range of heat prone environments. Theor. Appl. Genet. 2017, 130, 1819–1835. [Google Scholar] [CrossRef]
  75. Lopes, M.; Reynolds, M.; Manes, Y.; Singh, R.; Crossa, J.; Braun, H. Genetic yield gains and changes in associated traits of CIMMYT spring bread wheat in a “historic” set representing 30 years of breeding. Crop. Sci 2012, 52, 1123–1131. [Google Scholar] [CrossRef]
Figure 1. Optimization of gamma radiation dose and development of Punjab-11 population. Experiment 5 was selected as an optimized dose on the basis of germination rate (45–55%). Yellow colored plants were tagged from each row to record morphological data and collection of spikes. Red colored plants were wild type plants (nontreated), whereas green colored plants were the mutant population (treated with 250 k rad, Cs 137). LR and YR represent leaf and yellow rusts. Each generation (from M2 to M7) was inoculated with rust to ensure maximum disease attack.
Figure 1. Optimization of gamma radiation dose and development of Punjab-11 population. Experiment 5 was selected as an optimized dose on the basis of germination rate (45–55%). Yellow colored plants were tagged from each row to record morphological data and collection of spikes. Red colored plants were wild type plants (nontreated), whereas green colored plants were the mutant population (treated with 250 k rad, Cs 137). LR and YR represent leaf and yellow rusts. Each generation (from M2 to M7) was inoculated with rust to ensure maximum disease attack.
Agriculture 11 00621 g001
Figure 2. Pearson’s product-moment correlation between traits between the years 2015–2018 for NN-1 (ac) and Punjab-11 (df). DTH = days to heading; SL = spike length; PH = plant height; TPP = tillers per plant; * and ** = significance of the correlation at 0.05 and 0.01 probability (p) level, respectively.
Figure 2. Pearson’s product-moment correlation between traits between the years 2015–2018 for NN-1 (ac) and Punjab-11 (df). DTH = days to heading; SL = spike length; PH = plant height; TPP = tillers per plant; * and ** = significance of the correlation at 0.05 and 0.01 probability (p) level, respectively.
Agriculture 11 00621 g002
Figure 3. Pearson’s product-moment correlation (r) between selected plants for NN-1 (a) and Pb-11 (b). DTH = days to heading; PH = plant height; SL = spike length; TPP = tillers per plant; * and ** = significance of the correlation at 0.05 and 0.01 probability (p) level, respectively.
Figure 3. Pearson’s product-moment correlation (r) between selected plants for NN-1 (a) and Pb-11 (b). DTH = days to heading; PH = plant height; SL = spike length; TPP = tillers per plant; * and ** = significance of the correlation at 0.05 and 0.01 probability (p) level, respectively.
Agriculture 11 00621 g003
Figure 4. Broad sense heritability of the investigated traits in (a) spring wheat mutant populations NN Gandum-1, and (b) Punjab-11 calculated across three consecutive mutagenized generations. DTH = days to heading; LR = leaf rust; YR = yellow rust; PH = plant height; SL = spike length; TPP = tillers per plant. Color code is given in the legend.
Figure 4. Broad sense heritability of the investigated traits in (a) spring wheat mutant populations NN Gandum-1, and (b) Punjab-11 calculated across three consecutive mutagenized generations. DTH = days to heading; LR = leaf rust; YR = yellow rust; PH = plant height; SL = spike length; TPP = tillers per plant. Color code is given in the legend.
Agriculture 11 00621 g004
Table 1. Generation-wise mean, range, and standard deviation (SD) for the investigated traits for NN-1 and Punjab-11.
Table 1. Generation-wise mean, range, and standard deviation (SD) for the investigated traits for NN-1 and Punjab-11.
TraitsPopulationMean (SD) of M5Mean (SD) of M6Mean (SD) of M7SD
NN-1
10 WT lines363435333502
DTH (days)8992.57 (1.68)92.82 (1.62)92.4 (1.96)−4.43 to +4.57
SL (cm)9.259.58 (0.68)9.67 (0.66)9.36 (0.97)−6.07 to +2.33
PH (cm)89.2689.32 (4.08)89.52 (5.06)86.27(5.58)−16.04 to +10.96
TPP55.06 (0.62)5.13 (0.57)5.07 (0.59)−0.94 to +1.06
Punjab-11
10 WT lines360034833453
DTH (days)9293.03 (1.30)93.12 (1.31)93.19 (1.33)−1.97 to +4.03
SL (cm)8.869.29 (1.05)9.3 (1.05)9.35 (1.05)−5.27 to +2.01
PH (cm)87.6588.03 (3.13)88.15 (3.15)88.21 (3.13)−8.62 to +4.49
TPP45.08 (0.63)5.1 (0.65)5.13 (0.62)−0.92 to +1.08
DTH = days to heading; SL = spike length; PH = plant height; TPP = number of tillers per plant.
Table 2. Correlation between generations among all four traits for NN-1 and Pujnab-11.
Table 2. Correlation between generations among all four traits for NN-1 and Pujnab-11.
GenotypesGenerationDTH (days)SL (cm)PH (cm)TPP
NN-1M5 versus M60.56 *0.65 *0.55 *0.36 *
M5 versus M70.52 *0.68 *0.5 *0.48 *
M6 versus M70.59 *0.69 *0.61 *0.53 *
Pb-11M5 versus M60.99 *0.92 *0.79 *0.96 *
M5 versus M70.99 *0.91 *0.79 *0.97 *
M6 versus M70.99 *0.93 *0.79 *0.97 *
DTH = days to heading; SL = spike length; PH = plant height; TPP = number of tillers per plant; * = significance at the 0.05 probability (p) level.
Table 3. Z-score values of selected mutant plants from the M7 generation of NN-1 and Pb-11 at NIBGE and NIAB.
Table 3. Z-score values of selected mutant plants from the M7 generation of NN-1 and Pb-11 at NIBGE and NIAB.
PopulationParameterDTH (Days) SL (cm)PH (cm)TPP
NN-1Wild type899.2589.265
Selected lines93.059.8190.245.23
SD1.81.25.30.7
Z score range−1.42 to +1.54−1.14 to +2.98−1.71 to +1.60−1.18 to +1.58
Lines with highest Z score912126
Pb-11Wild type928.8687.654
Selected lines92.069.0988.374.68
SD1.411.1330.82
Z score range−1.65 to +1.36−1.97 to +2.12−1.46 to +2.38−1.82 to +1.40
Lines with highest Z score148113752
DTH = days to heading; SL = spike length; PH = plant height; TPP = tillers per plant; SD = standard deviation.
Table 4. Comparisons of NN-1 (treated with EMS) and Pb-11 (treated with γ rays) in M7 populations.
Table 4. Comparisons of NN-1 (treated with EMS) and Pb-11 (treated with γ rays) in M7 populations.
TraitsVariants (%) in M7 Generation
NN-1 (n = 3502)Pb-11 (n = 3453)Gain over EMS/γ Rays
Variants%Variants%NN-1 (EMS)Pb-11 (γ Rays)
DTH59016.8566219.17 2.32
LR117233.4779422.9910.47
YR58716.7685824.85 8.09
SL81323.22110231.91 8.7
PH43012.2896127.83 15.55
TPP119033.98104730.323.66
Average 22.76 26.18
DTH = days to heading; LR = leaf rust; YR = yellow rust; SL = spike length; PH = plant height; TPP = tillers per plant; n = number of mutant lines.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Hussain, M.; Gul, M.; Kamal, R.; Iqbal, M.A.; Zulfiqar, S.; Abbas, A.; Röder, M.S.; Muqaddasi, Q.H.; Mehboob-ur-Rahman. Prospects of Developing Novel Genetic Resources by Chemical and Physical Mutagenesis to Enlarge the Genetic Window in Bread Wheat Varieties. Agriculture 2021, 11, 621. https://doi.org/10.3390/agriculture11070621

AMA Style

Hussain M, Gul M, Kamal R, Iqbal MA, Zulfiqar S, Abbas A, Röder MS, Muqaddasi QH, Mehboob-ur-Rahman. Prospects of Developing Novel Genetic Resources by Chemical and Physical Mutagenesis to Enlarge the Genetic Window in Bread Wheat Varieties. Agriculture. 2021; 11(7):621. https://doi.org/10.3390/agriculture11070621

Chicago/Turabian Style

Hussain, Momina, Maryyam Gul, Roop Kamal, Muhammad Atif Iqbal, Sana Zulfiqar, Ammad Abbas, Marion S. Röder, Quddoos H. Muqaddasi, and Mehboob-ur-Rahman. 2021. "Prospects of Developing Novel Genetic Resources by Chemical and Physical Mutagenesis to Enlarge the Genetic Window in Bread Wheat Varieties" Agriculture 11, no. 7: 621. https://doi.org/10.3390/agriculture11070621

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop