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Mutation Breeding of a N-methyl-N-nitrosourea (MNU)-Induced Rice (Oryza sativa L. ssp. Indica) Population for the Yield Attributing Traits

by 1,*, 1, 2, 3,4 and 5
Graduate School for International Development and Cooperation, Hiroshima University, Hiroshima 739-8529, Japan
Department of Biotechnology, NTT Institute of Hi-Technology, Nguyen Tat Thanh University, Ho Chi Minh 72820, Vietnam
Department of Genetic Engineering, Agricultural Genetics Institute, Hanoi 122300, Vietnam
Center for Expert, Vietnam National University of Agriculture, Hanoi 131000, Vietnam
Khai Xuan International Co. Ltd., Ha Dong District, Duong Noi Ward, LK20A-20B, Khai Xuan Building, Hanoi 152611, Vietnam
Author to whom correspondence should be addressed.
Sustainability 2019, 11(4), 1062;
Received: 5 January 2019 / Revised: 15 February 2019 / Accepted: 15 February 2019 / Published: 18 February 2019
(This article belongs to the Special Issue Sustainable Crop Production Systems)


Difficulties in breeding new rice cultivars that have a high yield, are acceptable quality, and are tolerant to environmental stresses have been the major constraint of rice production in many developing countries, as these traits are determined by multiple genes associated with complicated and uncontrollable gene segregations.Furthermore, the gene/QTL (quantitative trait locus) introduced to the cultivar is unstable due to the interaction among the active genes, which determine the phenotypic performance, not yet been well understood or controllable. In this study, the N-methyl-N-nitrosourea (MNU)-induced mutation was applied to the heterozygote of the F1 generation from the cross between TBR1 (female) and KD18 (male parent). The phenotype and genotype of the M2 and M3 generations were evaluated and showed that the mutant population phenotypes, including the plant height, semi-dwarfism, amylose content, protein content, gel consistency, grain yield, and spikelet fertility, varied. Interestingly, no segregation among the genotypes in the M2 and M3 generations was observed, while the genotypes of the control population were either paternally inherited or indeterminable when using 28 polymorphism simple sequence repeat (SSR) markers that were identified on parental lines from 200 markers. The MNU-induced mutation caused maternal inheritance in the segregating populations, as primarily important agronomic traits were maternally succeeded from the female line TBR1. The findings of this study indicated that, through the use of MNU, the breeding of rice cultivars with close genetic backgrounds (similarity coefficient = 0.52) could be shortened by the maternal control of important qualities, such as pest and disease resistance and high yield, thus contributing to sustainable rice production for rice farmers. Further examination of rice cultivars with a greater difference in the genetic background should be subsequently conducted.
Keywords: MNU mutation; SSR marker; maternal inheritance; rice; important agronomic trait; rice breeding; sustainable rice production MNU mutation; SSR marker; maternal inheritance; rice; important agronomic trait; rice breeding; sustainable rice production

1. Introduction

Food security and sustainability have focused on rice (Oryza sativa L.), as it is a staple crop for more than half of the world’s population. Furthermore, the demands of humans necessitate an increase in rice production despite the challenges of climate change, environmental stress, and pest and disease infestation [1]. The development of new rice cultivars with a high yield, strong resistance, acceptable quality, and wide adaptation is urgently needed [2], but this commonly takes 8–10 years or more because of the great recombination in phenotypes and genotypes required, with laborious work and a huge expenditure involved. Therefore, the lack of new rice cultivars has caused unsustainable rice production in many developing countries. It has been reported that a few traits inherited from the recurrent parent (female cultivar) exhibited cytoplasmic effects in rice, such as a low temperature [3], a low yield and width of the flag leaf [4], a low grain weight [5], a low protein content [6,7], chalkiness [8], a low cooking quality [9], and low nutrient levels [7]. A genetic investigation of the inherited effects indicated that the genetic variation in the cytoplasmic effects was low (2.41%–20.80%), whereas the maternal influence on the lysine content was greater than on the protein content and index [7].
Most agronomic traits in rice are controlled by multiple genes that show complex and quantitative inheritance [10]. Here, molecular breeding is advantageous to identify the quantitative trait loci (QTLs) associated with the desired traits, as quantitative trait locus (QTL) alleles with agronomic value have been tagged by DNA markers and introduced into elite cultivars by marker-assisted selection (MAS) [10]. To date, more than 8000 QTLs have been detected and published in the Gramene-QTL database [11]. Some effective QTLs have been cloned through map-based strategies, but the precise mapping of QTL alleles with small effects has been the principal challenge [10]. The complete sequence of the rice genome has facilitated the genetic analyses of the interesting traits [12], of which simple sequence repeats (SSRs) are currently one of the main sources of genetic markers. However, the interaction among QTLs and the corresponding genes are yet to be understood. The introgression of elite traits to target rice cultivars by molecular breeding has achieved limited success, as they have linkage-drag, where the elite QTLs tightly link with unnecessary traits. Furthermore, the influence of the genotype and its interaction with the environment on the agronomic traits has caused difficulties in rice breeding because these tightly-linked traits are paternally inherited. Moreover, the newly developed cultivars should be grown in areas which have a similar climate and growing conditions as the donor parent’s (male variety), otherwise, the introgressed agronomic traits may be lost after successive generations.
Induced mutation (physical or chemical) has been documented as an impressive method to effectively improve food production in rice, sunflower, wheat, and many other crop varieties [13,14]. Previous works have assessed the efficacy of mutants and indicated that induced mutation showed intensive impacts on rice production, especially in the Asia–Pacific area [15,16]. Classical mutagenesis has been recorded in more than 3000 mutant varieties in more than 200 plant species over the past six decades [17]. Many mutant varieties have been created by radiation, as direct mutants, with gamma rays (Brassica juncea; Onobrychis vicifolia; Musa spp.; Ananas comosus; Solanum tuberosum), x-rays (Nelumbo nucifera; Zea mays; Hordeum vulgare; Triticum aestivum), fast neutrons (Lotus japonicas; Medicago truncatula; Glycine soja; Hordeum vulgare; Arabidopsis thaliana), and other radiation sources [16]. Many mutants induced with chemicals have been applied to plant mutation breeding and distributed for genetic studies (Oryza sativa; Zea mays; Sorghum bicolor), transgenic technologies (Zea mays; Gossypium spp.) [18], and for plant reproduction via sexual breeding (Musa spp.) [19]. According to the Mutant Varieties Database (, many characteristics of rice (828 cultivars) have been improved by mutation, resulting in a high yield, colored grain rice, culinary quality, disease resistance, drought tolerance, a shorter duration, a shorter height, and being slightly aromatic [20]. Among the chemical mutagens, N-methyl-N-nitrosourea (MNU) obtained biological effects from the cells since it transfers a methyl group to the oxygen and nitrogen atoms of the nucleotide bases [20]. In rice (Oryza sativa L.), MNU-activated mutant populations were discovered to have high-frequency mutations which were valuable for genetic approaches [21]; consequently, numerous rice mutant varieties have been directly developed from mutated populations (RD6 and RD15 in Thailand; Zhefu 802 in China; PNR-102 and PNR-381 in India, Amaroo in Australia, and Camago 8 in Costa Rica; TNDB100 and THDB in Vietnam, Shwewatun in Myanmar, and 18 varieties in Japan) [14].
Many kinds of DNA molecular markers (RFLP, RAPD, AFLP, ISSR, SSR, SNP, DArT, and Retrotransposons) have been developed and shown to be efficient in crop improvement [22]. Out of all of them, SSR markers are feasible for identifying their derivation and for classifying important inducing mutations [23]. The uses of SSR markers are convenient and effective in breeding new rice cultivars to obtain elite important agronomic traits, such as plant height, maturity, seed shattering, amylose content, yield, and resistance to disease and environmental stresses (153 hybrid lines bred from 18 parents) [23]. In previous studies, several mutant lines developed from the MNU-induced mutation showed potential for breeding new rice cultivars with a high yield and good quality (DT84DB and DT84DB x Baothai) [24,25].
Considering all of these points, to simplify the breeding of new rice cultivars that may contribute to sustainable rice production in developing countries, the current study was conducted to evaluate the phenotypic and genotypic segregation of mutant populations generated by the MNU-induced mutation and the efficacy, for the development of improving rice cultivars.

2. Materials and Methods

2.1. Rice Materials

TBR1 is a cultivar with a high yield, resistance to pests and diseases, and is widely cultivated in southern Vietnam. Khang dan 18 (KD18) is broadly planted in the northern and central regions of Vietnam with average quality and yield, but weak resistance to pests and diseases. Both TBR1 and KD18 were provided by the Agricultural Genetics Institute, Hanoi, Vietnam. In this study, TBR1 was used as the female parent and KD18 was used as the male parent. Both are Indica subtypes, where the plant height (PH) of TBR1 (100–110 cm) is relatively taller than that of KD18 (95–100 cm). The field experiment was conducted near Hiroshima University (85°E, 34°23′41″N 132°43′5″E, Higashihiroshima-shi, 270–280 m elevation; 33/25 °C day/night; humidity: 60–65%; precipitation average: 1485 mm), Higashi–Hiroshima, Hiroshima, Japan. The fertilizers, weeding, water, and pesticides were provided by conditional methods in Japan. The F1 population, derived from TBR1 (female) x KD18 (male) crossing, was treated with the MNU mutation, as described previously by Anh et al. [24] with some slight modifications. The heterozygosity of the F1 generation was identified by SSR markers, then these F1 seeds were further soaked in 150 nM MNU for 3 hours, dried, and kept in the dark for 3 months in a hermetic condition before being stored at 4 °C. The mutated F1 (M1) (200 seeds) was self-pollinated to yield the mutated F2 (M2) population and was grown in 2016 (May–September, wet season). Subsequently, the M2 population (200 seeds) was self-pollinated in paddy fields to provide the F3 generation (M3) (200 seeds) and was cultivated in 2017 (May–September, wet season). A control (without mutation) from the TBR1 (female) x KD18 (male) was also used as a check for comparison. Paddy rice seeds of each M1, M2, and M3 were collected, the debris was removed, and the seeds were unhusked, dried, and stored at 5 °C in the dark until planting. The rice seedlings were transplanted by hand at 20 days after the sowing. The weeds were controlled by hand and fertilizers (N: 270 kg, P: 145 kg, K: 180 kg ha−1) were applied at three stages: One week after the seedlings were transplanted, at the active tillering stage (30 days after transplantation), and at the panicle initiation (45 days after transplantation). A pesticide (Sutakuru, active compound: dinotefuran) (Mitsui Chemicals, Tokyo, Japan) was applied at 2.5 kg ha−1.
30 plants from the three replications were randomly selected to evaluate the phenotypic characteristics. The plant height (PH) (cm) was calculated by using the length of the tallest tiller number from the soil surface to the tip of the panicles. The panicle length (PL) was calculated by using the length (cm) from the neck to the tip of the panicles. The grain yield (tons per ha) was recorded from the grain yield per square meter. The spikelet fertility was calculated using the ratio of the filled grains to the total grains of each plant.
The harvest rice grains were dried with an oven to maintain the seed moisture at about 12%. The 1000 seed weight was calculated in grams. Grain quality traits, including amylose, protein, and lipid contents, were measured by a PGC Shizuoka Seiki PS-500 (version 2-12, Shizuoka Seiki Co. Ltd.; Shizuoka, Japan) [24].

2.2. Genotypic Analysis

2.2.1. DNA Extraction

The total DNA from each sample was extracted by the cetyltrimethylammonium bromide (CTAB) method with some modifications [24]. A total of 0.5 g of the young leaves of each of the individual parents, M2, M3, and the control (without mutation) populations, were collected at 60 days after transplantation, cut into small pieces, put into a 2.0 mL centrifuge tube with balls, and kept at −80 °C for at least two hours. These were broken up into a fine powder by a Tissue Lyser II machine (Qiagen, Germany). After that, 500 µL of hot CTAB buffer (2% CTAB, 1 M Tris-HCl, 0.5 M EDTA, 5 M NaCl, PVP) was added into each tube and vigorously vortexed. These mixtures were incubated at 65 °C for 15 mins by a Block Bath Shaker (MyBL-100S, AS ONE Corp. Japan, Osaka, Japan) and centrifuged at 12,000 rpm for 5 mins at 4 °C. The supernatant was mixed with 250 µL of chloroform: isoamyl alcohol (24:1 ratio) and kept at room temperature for 5 mins. Then, these mixtures were centrifugated at 12,000 rpm for 5 min at 4 °C and the supernatant was transferred to a new 1.5 mL microfuge tube, supplemented with 500 µL of isopropanol, and stored at −20 °C for at least 1 hour. The supernatant was discarded after centrifugation at 12,000 rpm at 4 °C for 5 mins and the pellet was then washed with 500 µL of 70% ethanol. The pellet was dried for 15 mins at 37 °C by an Iwaki Vacuum Centrifugal Evaporator HVC 500 machine (Iwaki Co., Ltd., Tokyo, Japan) and dissolved by a TE (Tris-EDTA) buffer. The DNA concentrations were determined by a Thermo Scientific Multiskan Go, μDrop™ Plate (Thermo Fisher Scientific, Finland) and were optimized to a final concentration of 100 ng/µL with the TE buffer (10 mM Tris-HCl, pH = 8, 1 mM EDTA, pH = 8). The DNA was stored at −20 °C for genetic analysis.

2.2.2. Polymorphism Screening and Mutant Population Genotyping by SSR Markers

A set of SSR markers linked with quality traits, including amylose content, gel consistency, gelatinization temperature, protein content, and plant height, were selected from the Gramene database [26]. The SSR markers linked with a semi-dwarfism gene were chosen, as described by Liang et al. [27]. They were screened among the parents on all 12 chromosomes to privilege the polymorphism markers for each population.
The progenies from the M2, M3, and control (without mutation) populations were genotyped with the SSR polymorphic markers to explore their genotypic segregation. The Polymerase Chain Reaction (PCR) analysis was performed on a Thermal Cycler Gene Atlas S (ASTEC Co. Ltd; Fukuoka, Japan) [24]. The thermal program was as follows: An initial denature at 94 °C for 5 min, followed by 35 cycles at 94 °C for 1 min, 55 °C for 1 min, and 72 °C for 2 min, with a final extension at 72 °C for 5 min. Each PCR mixture had a final volume of 10 µL, containing 100 ng of genomic DNA, 1 × PCR buffer, 2 mM of MgCl2, 100 µM of dNTPs (dideoxynucleotides), 0.5 µM of forward and reserve primer, and 1.5 U of Taq DNA Polymerase. The PCR products were separated by electrophoresis in 3% agarose in TBE 0.5× (Tris-base, Boric acid, 0.5 M EDTA) with Safeview™ classic (Applied Biological Materials Inc., Richmond, BC, Canada).

2.3. Data Analysis

The phenotypic parameters were evaluated in three replications in a completely randomized block design. The data were analyzed by the SPSS 20.0 package (SPSS Inc.; Chicago, IL, USA) and Minitab version 16 software (Minitab Inc., Stage College, PA, USA). Normal distributions of the phenotypic traits in the parents and the M2 and M3 populations were calculated by descriptive statistics. The mean square value of two-way ANOVA (analysis of variance) was measured by the general linear model univariate procedure to determine the genotypic and environmental variations among the phenotypic characters [28]. During this analysis, the year was considered as the environment, and the correlation analysis among the phenotypic characters in two years (two environments) was analyzed using the Pearson correlation coefficient test [24].

3. Results

3.1. Agronomic and Crop Quality Trait Performance of the Mutant Populations

As per Table 1, the summary statistics are presented for the phenotypic traits of the parent and rice mutant populations from 2016 to 2017. Among the 13 evaluated phenotypes of the parental lines, nine characteristics of the TBR1 (female parent) had a significantly higher value than those of the KD18 (male parent) in 2016, including the plant height (cm), panicle length (cm), grain length (mm), grain width (mm), grain length-to-width ratio (mm), 1000-grain weight (g), grain yield, amylose content (%), and lipid content (%), although the values of the total grain weight per plant, grain yield, and amylose content in 2017 were non-significant. In contrast, the values of the number of panicles and the grain width of KD18 were significantly greater than those of TBR1. The values of the spikelet fertility and protein content in the two parent cultivars were similar between the 2016 and 2017 cropping seasons (Table 1). In the mutated population, the following phenotypes followed the recurrent parent: The number of panicles (2016), grain length (2016, 2017), grain length-to-width ratio (2016, 2017), total grain weight per plant (2017), spikelet fertility (2017), protein content (2017), and lipid content (2017), whereas the grain width (2016, 2017), total grain weight (2016), and grain yield (2016, 2017) followed the donor parent (KD18). Other phenotypes were either average or greater than the values of the TBR1 and KD18 (Table 1). As can be seen from Table 1, the investigated phenotypes were principally trended towards the recurrent parents, rather than the donor parents, although their values varied between the two cropping seasons in 2016 and 2017. Meanwhile, in the control population, the phenotypes were complicated and distorted the segregation of either the male or female cultivars (Table 1).

3.2. Analysis of Variance among Phenotypes

Table 2 presents the variances of the phenotype, the genotypes, and the interaction between the genotype and environment (G × E) of the parents and mutant progenies using two-way ANOVA (Analysis of the Variance). It can be seen from this table that the genotypic variances for the plant height, grain length, number of panicles, grain yield, amylose content, and lipid content were significantly different at p < 0.001, with total ratios of the variance of 1.62%, 55.56%, 1.04%, 1.08%, 2.2 %, and 0.16%, respectively. However, the genotypic coefficient of protein was 1.37% (p < 0.01), whereas the other traits were not significant (Table 2).
The variance from the environment for the plant height, number of panicles, grain width, grain length-to-width ration, total grain weight, grain yield, spikelet fertility, 1000-grain weight, protein content, amylose content, and lipid content were highly significant at p < 0.001 (Table 2) and were 99.7%, 99.16%, 98.07%, 98.00%, 97.33%, 97.06%, 96.02%, 96.00%, 95.94%, and 91.18%, respectively. In contrast, the interaction between the genotype and the environment for the investigated agronomic traits was small. Significant interactions were found in six characteristics, including the amylose content (1.86%, p < 0.001), total grain weight (0.89%, p ˂ 0.001), grain yield (0.92%, p ˂ 0.001), plant height (1.32%, p < 0.05), protein content (1.30%, p < 0.05), and lipid content (0.13%, p < 0.05).

3.3. Correlations of Phenotypic Parameters

The correlations between the phenotypes of the parental lines and the mutant populations were also analyzed (p < 0.05 and 0.01) (Table 3). Highly significant and positive correlations were found between the grain yield (GY) and the total grain weight per plant (FG) (r = 0.89 and r = 0.97, respectively) (p < 0.01) in both years. A positive correlation was also detected between the grain yield (GY) and the spikelet fertility (SP) (r = 0.32, p < 0.01) in 2016. In addition, significant but negative correlations were observed between the grain width (GW) and the grain length-to-width ratio (LWR) (r = –0.92, r = –0.75, respectively) (p < 0.01).
An inconsistent association between the plant height (PH) and other traits was observed. In 2016, the plant height (PH) showed no correlation with the number of panicles (NP), but in 2017, the PH exerted a positive correlation with the SP (r = 0.44, p < 0.01). Negative correlations of the PH and the grain length (GL) (r = −0.21, p < 0.05) in 2016 with the grain length-to-width ratio (LWR) (r = −0.22, p < 0.05) in 2017 were found. In addition, the PH displayed an inconstant and negative correlation with the protein content (PC) in 2016 (r = −0.22, p < 0.01), but a positive correlation (r = 0.32, p < 0.01) in 2017.
The correlation between the amylose content (AC) and the other phenotypes in the mutant populationswas also analyzed (Table 2). The amylose content showed a significant positive correlation with the protein content in 2016 (r = 0.60, p < 0.01); however, a negative significant correlation was found in 2017 (r = −0.38, p < 0.01). The AC had a positive correlation with the lipid content (LC) in 2016 (r = 0.35, p < 0.01), but no correlation was found in 2017 (Table 2). Similarly, the AC showed unfavorable correlations with the LWR and the NP (r = −0.25 and r = 0.22, respectively, p < 0.05) in 2017. A negligible and inconsistent correlation between the PC and other traits was found. In addition, the PC exerted a high and remarkable correlation with the lipid content (LP) (r = 0.51, p < 0.01) only in 2016 (Table 3).

3.4. Identification of Marker Polymorphism and Genotypic Segregation of the Phenotypic Characteristics in M2 and M3 Populations

To analyze the genotypes of the mutated M2 and M3 populations, as well as the control populations, 200 SSR markers correlated with the quality traits, plant height, and semi-dwarfism were screened on the parental cultivars to identify polymorphic markers. A total of 28 SSR markers were found to be polymorphic, which were distributed on 10 different chromosomes of the parent rice varieties TBR1 and KD18. The polymorphic SSR markers were distributed on chromosomes 1, 2, 3, 4, 5, 6, 7, 8, 9, and 11, with five markers being located on chromosomes 6 and 9. Chromosomes 2 and 1 possessed four markers, three markers were found on chromosomes 1 and 4, and only one marker was observed on chromosomes 5, 7, and 8 (Table 4).
Among the 28 polymorphic SSR markers, the number of markers belonging to the amylose content, plant height, gel consistency, blast disease resistance, spikelet fertility, grain yield, brown planthopper, spikelet number, semi-dwarfism, and gelatinization temperature were subsequently 14, 8, 5, 4, 3, 2, 1, 1, and 1, respectively (Table 4). Basically, these were all nucleus-mediated agronomic traits and were inherited from the donor (male) parent. These polymorphic SSR markers were genotyped on the mutated M2, M3, and the control populations (Table 4). Interestingly, all of the mutant progenies carried only female alleles. None of the mutants carried male alleles (Supplementary Materials, Figures S1–S56). In contrast, the evaluation of the genotypes of the control populations based on these markers showed that the control populations carried paternal, maternal, and heterozygosity alleles (Supplementary Materials, Figures S57–S63).

4. Discussion

For the improved genotype development, it is important to evaluate the phenotypic performance of the new cultivars in various conditional environments (either in different locations during the same season or in different seasons/years in the same location) [29]. To identify the genetic stability of a trait in a mutant population, the evaluation of the genotype of the modified traits, using molecular markers, should be conducted after phenotypic evaluation [30]. Furthermore, an induced mutation has more advantages for the variation of a few elite traits than conventional breeding [14].
The assessment of the phenotype of the parents indicated that the parental lines TBR1 and KD18 were suitable for studying a wide range of diversity in the selected traits. The current study found that the parents presented stability, whereas the mutant progenies expressed the variations in two years (2016 and 2017). It was indicated that the variation may be due to the environmental influence on most phenotypic characteristics associated with genotypes [31,32]. However, due to an inconsistency between the parents and mutant progenies, a possible explanation for this variation may be that the mutation affected the rice materials and led to variations in phenotype.
The traits, including the plant height, grain length, total grain weight number per plant, grain yield, and the content of protein, amylose, and lipids, were reported to be sensitively affected by the environment [31]. Additionally, the evaluation of the interaction between the genotype and the environment was important for the improvement of agronomic traits in crops [33,34]. In this study, slight and inconsistent correlations between the phenotypic traits of mutants were identified in the two years (Table 3), which might be due to the interaction between the genotypes and their environments [32], as well as influences of the environment, such as the climate and soil [32,35]. In a previous study on transfer DNA (T-DNA) insertional mutagenesis, Lo et al. [35] stated that it was necessary to screen the developed rice mutants in a favorable environment. In MNU-induced mutant research, Suzuki et al. [21] observed that visible mutant phenotypes were segregated and numerous mutants had multiple abnormalities, out of which each phenotype was segregated independently. The current study strengthened the evidence of MNU-induced mutation and was associated with phenotypic variations. Several lines of evidence suggest that MNU, a strong chemical mutagen, can change the genomic base-pair constitution [17,21] that leads to phenotype change, as the phenotype is a result of the interaction between the genotype and the environment [36].
Polymorphism molecular markers, as an effective tool in molecular breeding, were used to measure the genetic diversity in this study [37]. The low polymorphic markers on TBR1/KD18 may be due to their close relationship. The evaluation of the mutant genotypes in the two generations by polymorphic markers showed that all of the mutants possessed female parent alleles without male parent alleles. The mutants completely inherited different traits from the female parent (TBR1) without segregation (Supplementary Materials, Figures S1–S56). This finding showed non-Mendelian inheritance (Supplementary Materials, Figures S1–S56), whilst the genotypes of the control population were uncontrollable (Supplementary Materials, Figures S57–S63). Non-Mendelian inheritance has been documented in cultivated rice (Oryza sativa) and wild rice (O. rufipogon [37,38,39,40,41], O. sativa × O. glumaepatula [42], Indica × Japonica [43], O. sativa × O. longistaminata [44], and Indica × Javanica [45,46]). A possible explanation for this may be due to the genetic distances of the parents [37,44,47]. The importance of genetic distance has been noted, as it creates unpredictable recombination that leads to segregation. However, in a previous study, we observed that the high genetic similarity of the female and male parents (TBR1 and KD18) was identified, with a similarity coefficient of 0.52 (Supplementary Materials, Table S1) [24]. Therefore, lesser recombination between the parent cultivars can be made in comparison to rice cultivars with a greater difference in their genetic background. Furthermore, the literature indicates that maternal inheritance usually occurs in mitochondrial and chloroplast genes [48,49]. In this study, the use of MNU mutation was successfully conducted on the maternal control of important agronomic traits (Table 1 and Table 2), which were nuclear-mediated to reduce the breeding time. However, the further examination of cultivars with a greater difference in their genetic background should be conducted to affirm the success of MNU mutation on rice breeding.
In our previous research, the effectiveness of MNU-induced mutation was demonstrated on rice genotypes [24]. As an immensely strong chemical mutagen [17], MNU resulted in a higher frequency mutation than ethyl methanesulfonate (EMS) [21,50] and created high-density mutations with a random distribution via the radiation of a single nucleotide [51]. The MNU-induced mutation altered nucleotides that caused a point mutation [17]. In addition, the ratio of amino acids was increased, a total of 360 variations were detected in the protein coding regions of the genome, and phenotypic characterization of the mutants showed abnormal levels [21].
In crops including rice, it commonly takes many years and may require up to 20 cropping cycles and heavy laborious work to breed a new cultivar with desirable agronomic characteristics. Therefore, shortening the breeding time is an essential requirement. This study demonstrated the role of MNU-induced mutation and supports the fact that conventional mutation by MNU can reduce the breeding time in rice cultivars with a close genetic background. The MNU-mutated populations, M2 and M3, expressed the phenotypes and genotypes that correlated to the MNU effects. Therefore, a putative gene linkage with an unknown mechanism may be induced from MNU mutation, to eliminate the complete segregation of genotypes in the M2 and M3 populations of TBR1 and KD18 cultivars. The MNU-induced mutation probably generated a novel maternal inheritance of important agronomic traits in rice, such as quality, plant height, protein content, and pest and disease resistance, so further elaboration on the subsequent generations should be conducted to ensure the stability of the maternal inheritance. In addition, the unknown mechanism of this putative gene linkage needs clarification and examination in different populations of rice cultivars with a greater difference in their genetic background.

5. Conclusions

This study revealed that, although the phenotypic performances of both the mutant and control populations were variable, few important agronomic traits were maternally inherited in the two rice cultivar, TBR1 and KD18. The experimental findings of this study confirmed the application of MNU, in that it may assist in more effective mutation breeding for the development of various traits in new rice genotypes, which is not possible through conventional breeding. However, the efficacy of MNU mutation on different rice cultivars with a diverse genetic background requires further investigation. The search for the presence of genes related to the novel maternal inheritance should be targeted in continuous research. More tests of the MNU mutation on rice cultivars with a greater difference in their genetic background should be examined to confirm the efficacy of this induced mutation on maternal inheritance in rice breeding. It thus contributes to the sustainability of rice cultivation in many developing countries.

Supplementary Materials

The following are available online at, Table S1, Figures S1–S63.

Author Contributions

T.D.X. and T.D.D. conveyed the idea of this research; T.T.T.A. conducted the experiments and wrote the manuscript under the supervision of T.D.X.; T.D.K. and T.D.D. created the mutant materials; T.T.T.A. analyzed the research data; T.D.X., H.-D.T., and T.D.K. corrected the manuscript; T.D.X. revised and approved the final version.


This research received no external funding.


The authors thank the Ministry of Agriculture and Rural Development and the Ministry of the Socialist Republic of Vietnam for providing a scholarship to Truong Thi Tu Anh. Thanks are also given to Can Thu Huong for her assistance in this research.

Conflicts of Interest

The authors declare no conflicts of interest. The rice cultivars TBR1 and KD18 were preliminarily released by the Thai Binh seed company and are acknowledged as national rice varieties of Vietnam, which can be freely used as materials for scientific research, according to the 190th Term, Intellectual Property Law (36/2009/QH12) of Vietnam. Both TBR1 and KD18 seeds used in this study were provided by the Agricultural Genetics Institute, Vietnam.


  1. Xuan, T.D.; Khang, D.T. Effects of exogenous application of protocatechuic acid and vanillic acid to chlorophylls, phenolics and antioxidant enzymes of rice (Oryza sativa L.) in submergence. Molecules 2018, 23, 620. [Google Scholar] [CrossRef] [PubMed]
  2. Xing, Y.; Yang, Q. Genetic and molecular bases of rice yield. Annu. Rev. Plant Biol. 2010, 61, 421–442. [Google Scholar] [CrossRef]
  3. Ratho, S.N.; Pradhan, S.B. Cytoplasmically controlled cold tolerance in a cytoplasmic-genetic male sterile line of rice. Euphytica 1992, 58, 241–244. [Google Scholar] [CrossRef]
  4. Tao, D.; Hu, F.; Yang, J.; Yang, J.; Yang, G.; Yang, Y.; Xu, P.; Li, J.; Ye, C.; Dai, L. Cytoplasm and cytoplasm-nucleus interactions affect agronomic traits in Japonica rice. Euphytica 2004, 135, 129–134. [Google Scholar] [CrossRef]
  5. Chandraratna, M.F.; Sakai, K.I. A biometrical analysis of matroclinous inheritance of grain weight in rice. Heredity 1960, 14, 365–373. [Google Scholar] [CrossRef]
  6. Chang, T.T.; Liu, F.H. Diallel analysis of protein content in rice. Agron. Abstr. 1974, 65, Chicago, Illinois. [Google Scholar]
  7. Shi, C.H.; Xue, J.M.; Yu, Y.G.; Yang, X.E.; Zhu, J. Analysis of genetic effects on nutrient quality traits in Indica rice. Theor. Appl. Genet. 1996, 92, 1099–1102. [Google Scholar] [CrossRef]
  8. Xu, Y.B.; Shen, Z.T. Maternal effect on chalkiness in rice kernels. Rice Genet. Newsl. 1988, 5, 111–113. [Google Scholar]
  9. Shi, C.H.; Zhu, J. Analysis of seed and maternal genetic effects for characters of cooking quality in Indica rice. Chin. J. Rice Sci. 1994, 8, 129–134. [Google Scholar]
  10. Fukuoka, S.; Ebana, K.; Yamamoto, T.; Yano, M. Integration of genomics into rice breeding. Rice 2010, 3, 131–137. [Google Scholar] [CrossRef]
  11. Jaiswal, P.; Ware, D.; Ni, J.; Chang, K.; Zhao, W.; Schmidt, S.; Pan, X.; Clark, K.; Teytelman, L.; Cartinhour, S.; et al. Gramene: Development and integration of trait and gene ontologies for rice. Comp. Funct. Gen. 2002, 3, 132–136. [Google Scholar] [CrossRef] [PubMed]
  12. IRGSP. The map-based sequence of the rice genome. Nature 2005, 436, 793–800. [Google Scholar] [CrossRef][Green Version]
  13. Riaz, A.; Gul, A. Plant mutagenesis and Crop Improvement. In Crop Production and Global Environment Issues; Hakeem, K., Ed.; Springer International Publishing AG: Cham, Switzerland, 2015; pp. 181–209. ISBN 9783319231624. [Google Scholar]
  14. Ahloowalia, B.S.; Maluszynski, M.; Nichterlein, K. Global impact of mutation-derived varieties. Euphytica 2004, 135, 187–204. [Google Scholar] [CrossRef]
  15. Maluszynski, M.; Szarejko, I. Induced mutations in the Green and Gene Revolutions. In Proceedings of the International Congress: In the Wake of the Double Helix: From the Green Revolution to the Gene Revolution, Bologna, Italy, 27–31 May 2003; Avenue Media: Bologna, Italy, 2005; pp. 403–425. [Google Scholar]
  16. Maluszynski, M.; Gustafson, P.; Maluszynska, J.; Szarejko, I. Advanced breeding for germplasm enhancement and yield improvement. In Yield Gap and Productivity Decline in Rice Production; FAO: Rome, Italy, 2001; pp. 191–224. [Google Scholar]
  17. Kurowska, M.; Labocha-Pawłowska, A.; Gnizda, D.; Maluszynski, M.; Szarejko, I. Molecular analysis of point mutations in a barley genome exposed to MNU and Gamma rays. Mutat. Res 2012, 738–739, 52–70. [Google Scholar] [CrossRef]
  18. Greene, E.A.; Codomo, C.A.; Taylor, N.E.; Henikoff, J.G.; Till, B.J.; Reynolds, S.H.; Henikoff, S. Spectrum of chemically induced mutations from a large-scale reverse-genetic screen in Arabidopsis. Genetics 2003, 164, 731–740. [Google Scholar] [PubMed]
  19. Oladosu, Y.; Rafii, M.Y.; Abdullah, N.; Hussin, G.; Ramli, A.; Rahim, H.A.; Miah, G.; Usman, M. Principle and application of plant mutagenesis in crop improvement: A review. Biotechnol. Biotechnol. Equip. 2016, 30, 1–16. [Google Scholar] [CrossRef]
  20. Mutant Varieties Database. The Joint FAO/IAEA (Food of Agriculture Organization of the United Nations and International Atomic Energy Agency). Available online: (accessed on 21 December 2018).
  21. 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]
  22. Nadeem, M.A.; Nawaz, M.A.; Shahid, M.Q.; Doğan, Y.; Comertpay, G.; Yıldız, M.; Hatipoğlu, R.; Ahmad, F.; Alsaleh, A.; Labhane, N.; et al. DNA molecular markers in plant breeding: Current status and recent advancements in genomic selection and genome editing. Biotechnol. Biotechnol. Equip. 2018, 32, 261–285. [Google Scholar] [CrossRef]
  23. Xie, F.; He, Z.; Esguerra, M.Q.; Qiu, F.; Ramanathan, V. Determination of heterotic groups for tropical Indica hybrid rice germplasm. Theor. Appl. Genet. 2014, 127, 407–417. [Google Scholar] [CrossRef]
  24. Anh, T.T.T.; Khanh, T.D.; Dat, T.D.; Xuan, T.D. Identification of phenotypic variation and genetic diversity in rice (Oryza sativa L.) mutants. Agriculture 2018, 8, 30. [Google Scholar] [CrossRef]
  25. Xuan, T.D.; Bach, D.T.; Dat, T.D. Involvement of phenolics, flavonoids, and phenolic acids in high yield characteristics of rice (Oryza sativa L.). Int. Lett. Nat. Sci. 2018, 68, 19–26. [Google Scholar] [CrossRef]
  26. Gramene Website Data Base. Available online: (accessed on 1 July 2016).
  27. Liang, G.; Cao, X.; Sui, J.; Zhao, X.; Yan, C.; Yi, C.; Gu, M. Fine mapping of a semi dwarf gene sd-g in Indica rice (Oryza sativa L.). Chin. Sci. Bull. 2004, 49, 900–904. [Google Scholar]
  28. Tong, C.; Chen, Y.; Tang, F.; Huang, Y.; Chen, H.; Bao, J. Genetic diversity of amylose content and RVA pasting parameters in 20 rice acessions grown in Hainan, China. Food Chem. 2014, 161, 239–245. [Google Scholar] [CrossRef]
  29. Ogunbayo, S.A.; Sie, M.; Ojo, D.K.; Popoola, A.R.; Oduwaye, O.A.; Daniel, I.O.; Sanni, K.A.; Akinwale, M.G.; Toulou, B.; Shittu, A.; et al. Comparative Performance of Forty-Eight Rice Genotypes in Diverse Environments Using the Ammi and GGE Biplot Analyses. Int. J. Plant Breed. Genet. 2014, 8, 139–152. [Google Scholar] [CrossRef]
  30. 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]
  31. Krishnan, P.; Rao, A.S. Effects of genotype and environment on seed yield and quality of rice. J. Agric. Sci. 2005, 143, 283–292. [Google Scholar] [CrossRef]
  32. Krishnan, P.; Ramakrishnan, B.; Reddy, K.R.; Reddy, V.R. High-temperature effects on rice growth, yield, and grain quality. In Advances in Agronomy; Sparks, D.L., Ed.; Elsevier/Academic Press: Cambridge, MA, USA, 2011; Volume 111, pp. 87–206. ISBN 978-0-12-387689-8. [Google Scholar]
  33. Doust, A.N.; Lukens, L.; Olsen, K.M.; Mauro-Herrera, M.; Meyer, A.; Rogers, K. Beyond the single gene: How epistasis and gene-by-environment effects influence crop domestication. Proc. Natl. Acad. Sci. USA 2014, 111, 6178–6183. [Google Scholar] [CrossRef] [PubMed][Green Version]
  34. Sharifi, P.; Aminpanah, H.; Erfani, R.; Mohaddesi, A.; Abbasian, A. Evaluation of Genotype× Environment Interaction in Rice Based on AMMI Model in Iran. Rice Sci. 2017, 24, 173–180. [Google Scholar] [CrossRef]
  35. Lo, S.F.; Fan, M.J.; Hsing, Y.I.; Chen, L.J.; Chen, S.; Wen, I.C.; Liu, Y.L.; Chen, K.T.; Jiang, M.J.; Lin, M.K.; et al. Genetic resources offer efficient tools for rice functional genomics research. Plant Cell Environ. 2016, 39, 998–1013. [Google Scholar] [CrossRef] [PubMed]
  36. Marri, P.R.; Sarla, N.; Reddy, L.V.; Siddiq, E.A. Identification and mapping of yield and yield related QTLs from an Indian accession of Oryza rufipogon. BMC Genet. 2005, 6, 33. [Google Scholar] [CrossRef] [PubMed]
  37. Russel, P.J. Mendenlian genetics. In Genetics: A Molecular Approach; Willbur, B., Ed.; Pearson Banjamin Cummings: San Francisco, CA, USA, 2010; pp. 297–319. ISBN 032569768. [Google Scholar]
  38. Xiong, L.Z.; Liu, K.D.; Dai, X.K.; Xu, C.G.; Zhang, Q. Identification of genetic factors controlling domestication-related traits of rice using an F2 population of a cross between Oryza sativa and O. rufipogon. Theor. Appl. Genet. 1999, 98, 243–251. [Google Scholar] [CrossRef]
  39. Moncada, P.; Martinez, C.P.; Borrero, J.; Châtel, M.; Gauch, J.H.; Guimaraes, E.; Tohme, J.; McCouch, S.R. Quantitative trait loci for yield and yield components in an Oryza sativa × Oryza rufipogon BC2F2 population evaluated in an upland environment. Theor. Appl. Genet. 2001, 102, 41–52. [Google Scholar] [CrossRef]
  40. Septiningsih, E.M.; Prasetiyono, J.; Lubis, E.; Tai, T.H.; Tjubaryat, T.; Moeljopawiro, S.; McCouch, S.R. Identification of quantitative trait loci for yield and yield components in an advanced backcross population derived from the Oryza sativa variety IR64 and the wild relative O. rufipogon. Theor. Appl. Genet. 2003, 107, 1419–1432. [Google Scholar] [CrossRef] [PubMed]
  41. Thomson, M.J.; Tai, T.H.; McClung, A.M.; Lai, X.H.; Hinga, M.E.; Lobos, K.B.; Xu, Y.; Martinez, C.P.; McCouch, S.R. Mapping quantitative trait loci for yield, yield components and morphological traits in an advanced backcross population between Oryza rufipogon and the Oryza sativa cultivar Jefferson. Theor. Appl. Genet. 2003, 107, 479–493. [Google Scholar] [CrossRef] [PubMed]
  42. Brondani, C.; Rangel, P.; Brondani, R.; Ferreira, M. QTL mapping and introgression of yield-related traits from Oryza glumaepatula to cultivated rice (Oryza sativa) using microsatellite markers. Theor. Appl. Genet. 2002, 104, 1192–1203. [Google Scholar] [CrossRef] [PubMed]
  43. Lu, C.F.; Shen, L.S.; Tan, Z.; Xu, Y.; He, P.; Chen, Y.; Zhu, L. Comparative mapping of QTLs for agronomic traits of rice across environments using a doubled haploid population. Theor. Appl. Genet. 1996, 93, 1211–1217. [Google Scholar] [CrossRef] [PubMed]
  44. Causse, M.A.; Fulton, T.M.; Cho, Y.G.; Ahn, S.N.; Chunwongse, J.; Wu, K.; Xiao, J.; Yu, Z.; Ronald, P.C.; Harrington, S.E.; et al. Saturated molecular map of the rice genome based on an interspecific backcross population. Genetics 1994, 138, 1251–1274. [Google Scholar] [PubMed]
  45. McCouch, S.R.; Kochert, G.; Yu, Z.H.; Wang, Z.Y.; Khush, G.S.; Coffman, W.R.; Tanksley, S.D. Molecular mapping of rice chromosomes. Theor. Appl. Genet. 1988, 76, 815–829. [Google Scholar] [CrossRef] [PubMed]
  46. Aluko, G.; Martinez, C.; Tohme, J.; Castano, C.; Bergman, C.; Oard, J.H. QTL mapping of grain quality traits from the interspecific cross Oryza sativa × O. glaberrima. Theor. Appl. Genet. 2004, 109, 630–639. [Google Scholar] [CrossRef] [PubMed]
  47. Grandillo, S.; Tanksley, S.D. QTL analysis of horticultural traits differentiating the cultivated tomato from the closely related species Lycopersicon pimpinellifolium. Theor. Appl. Genet. 1996, 92, 935–951. [Google Scholar] [CrossRef] [PubMed]
  48. Russel, P.J. Extension of and deviations from Mendelian genetic principles. In IGenetics A Molecular Approach, 3rd ed.; Wilbur, B., Ed.; Pearson Benjamin Cummings: San Francisco, CA, USA, 2010; pp. 363–400. ISBN 0-3256-976-8. [Google Scholar]
  49. Sato, M.; Sato, K. Maternal inheritance of mitochondrial DNA by diverse mechanisms to eliminate paternal mitochondrial DNA. Biochimica et Biophysica Acta-Mol. Cell Res. 2013, 1833, 1979–1984. [Google Scholar] [CrossRef] [PubMed][Green Version]
  50. Till, B.J.; Cooper, J.; Tai, T.H.; Colowit, P.; Greene, E.A.; Henikoff, S.; Comai, L. Discovery of chemically induced mutations in rice by TILLING. BMC Plant Biol. 2007, 7, 1–12. [Google Scholar] [CrossRef] [PubMed]
  51. Wang, N.; Long, T.; Yao, W.; Xiong, L.; Zhang, Q.; Wu, C. Mutant resources for the functional analysis of the rice genome. Mol. Plant 2013, 6, 596–604. [Google Scholar] [CrossRef] [PubMed]
Table 1. The descriptive statistics of the phenotypic traits in the parent (M2 and M3) populations in 2016 (upper values) and 2017 (lower values).
Table 1. The descriptive statistics of the phenotypic traits in the parent (M2 and M3) populations in 2016 (upper values) and 2017 (lower values).
TraitsParents (Mean ± SE)Mutated PopulationsControl Populations
Female (TBR1)Male (KD18)Mean ± SEMean ± SE
PH117.27 ± 0.50a a114.20 ± 0.60b a108.78 ± 0.60c b113.07 ± 0.49b b
117.90 ± 0.85a a114.20 ± 0.60b a114.80 ± 0.76b a116 ± 0.43ab a
NP4.90 ± 0.16b a5.57 ± 0.22a a4.93 ± 0.23b b6.33 ± 0.26a a
4.80 ± 0.00c a5.57 ± 0.22b a6.53 ± 0.00a a5.13 ± 0.18bc b
PL26.25 ± 0.29a a23.19 ± 0.13c a24.62 ± 0.21b a23.90 ± 0.16c a
26.24 ± 0.29a a23.19 ± 0.13c a24.27 ± 0.16b a24.55 ± 0.21b a
GL6.03 ± 0.02b a5.53 ± 0.03c a6.15 ± 0.02a a5.99 ± 0.02b a
6.03 ± 0.02b a5.53 ± 0.03c a6.14 ± 0.02a a6.01 ± 0.01b a
GW2.10 ± 0.02b a2.18 ± 0.01a a2.17 ± 0.01a a2.17 ± 0.03a a
2.10 ± 0.02b a2.18 ± 0.01a a2.13 ± 0.01ab b2.17 ± 0.01ab a
LWR2.88 ± 0.03a a2.54 ± 0.02b a2.84 ± 0.01a b2.76 ± 0.07b a
2.88 ± 0.03a a2.54 ± 0.02b a2.88 ± 0.01a a2.78 ± 0.01b a
FG21.60 ± 0.65a b19.66 ± 0.42b b18.69 ± 0.56b b17.53 ± 0.84c a
26.22 ± 1.16ab a25.55 ± 0.91b a27.91 ± 1.23a a18.24 ± 0.71c a
SP0.90 ± 0.01a b0.89 ± 0.01a b0.75 ± 0.01b b0.72 ± 0.01c a
0.92 ± 0.00b a0.98 ± 0.01a a0.90 ± 0.01b a0.74 ± 0.01c a
KWG19.19 ± 0.07b a18.54 ± 0.06c a20.01 ± 0.09a a19.99 ± 0.09a a
19.35 ± 0.06b a18.63 ± 0.07c a19.86 ± 0.08a a19.31 ± 0.07b b
GY7.13 ± 0.22a b6.49 ± 0.14b b6.17 ± 0.18b b5.59 ± 0.28c a
8.65 ± 0.38b a7.77 ± 0.30ab a9.21 ± 0.41a a6.02 ± 0.23c a
PC *6.22 ± 0.19ab a5.62 ± 0.10b a6.87 ± 0.33a a6.28 ± 0.28ab b
7.62 ± 2.50a a6.29 ± 0.1.27a a5.04 ± 0.10a b7.07 ± 0.22a a
AC22.72 ± 0.07c a23.60 ± 0.06a a23.19 ± 0.17b a23.40 ± 0.15ab a
22.46 ± 0.09ab b23.41 ± 0.06a b20.98 ± 0.93b b23.07 ± 0.16a a
LC9.32 ± 0.20b a8.60 ± 0.16c a15.10 ± 0.24a a9.77 ± 0.36b a
9.27 ± 0.21a a8.33 ± 0.15b a9.23 ± 0.13a b10.14 ± 0.23a a
Notes: a, b, c: Different letters in the same row indicate significant differences at the p < 0.05 level by Fisher pairwise comparisons; *: Different letters in the same row indicate significant differences at the p < 0.05 level by Turkey pairwise comparisons; a, b: Different letters indicate significant differences in each trait over two years (2016 and 2017) at the p < 0.05 level by the t-test; SE: Standard error; PH: Plant height; NP: Number of panicles per plant; PL: Panicle length; GL: Grain length; GW: Grain width; LWR: Grain length-to-width ratio; FG: Total grain weight per plant; SP: Spikelet fertility; KWG: 1000-grain weight; GY: Grain yield; PC: Protein content; AC: Amylose content; LC: Lipid content.
Table 2. The mean square values from two-way ANOVA among the phenotypic traits.
Table 2. The mean square values from two-way ANOVA among the phenotypic traits.
SourcesMean Square Values
GenotypesEnvironmentsG × E
PH20.93 ***1255.63 ***17.11 *
NP2.1542.19 ***1.93
GL0.05 ***0.020.02
GW0.010.48 ***0.01
LWR0.020.76 ***0.02
FG47.20 ***4467.76 ***40.59 ***
SP0.011.18 ***0.00
KWG0.2613.52 ***0.30
GY5.14 ***468.13 ***4.42 ***
PC2.36 **167.31 ***2.23 *
AC0.71 ***30.98 ***0.60 ***
LC2.46 ***1578.39 ***2.05 *
Notes: ANOVA: Analysis of the variance. *, **, ***: Indicate significance at the p < 0.05, 0.01, and 0.001 levels, respectively. df: Degree of freedom; G × E: The interaction between the genotype and environment, respectively; PH: Plant height; NP: Number of panicles per plant; PL: Panicle length; GL: Grain length; GW: Grain width; LWR: Grain length-to-width ratio; FG: Total grain weight per plant; SP: Spikelet fertility; KWG: 1000-grain weight; GY: Grain yield; PC: Protein content; AC: Amylose content; LC: Lipid content.
Table 3. The correlation among the phenotypic traits from 2016 (upper values) to 2017 (lower values).
Table 3. The correlation among the phenotypic traits from 2016 (upper values) to 2017 (lower values).
PH0.17−0.03−0.29 **0.09−−−0.22 *0.07−0.35 **
0.44 **0.13−0.140.15−0.22 *−0.11− **−0.19−0.14
−− *−0.21 *−0.09
PL 10.12−0.050.08−0.010.00−0.02−0.07−0.07−0.01−0.020.00−0.03
GL 1−0.26 *0.61 **−0.10−0.08−0.03−0.05−0.05−0.100.04−0.090.13
0.010.65 **−0.05−−0.050.02−0.180.06
GW 1−0.92 **−0.090.05−0.05
−0.75 **−0.07−0.110.22 *−0.07−0.07−−0.09
LWR 1−0.08−0.05−0.07−0.10−0.10−0.080.08−0.090.09
0.010.03−−0.25 *0.11
FG 10.89 **0.31 **−0.13−0.131.00 **−0.050.00−0.16
0.97 **0.06−0.16−0.161.00 **−0.07−0.110.03
TGP 1−0.16−0.06−0.060.89 **−
−0.16−0.17−0.170.97 **−0.08−0.140.05
SP 1−0.19−0.190.32 **−0.03−0.17−0.30 **−0.06
THG 11.00 **−0.13−0.13−0.110.07
1.00 **−0.160.00−0.060.00
KWG 1−0.13−0.13−0.110.07
GY 1−0.050.00−0.16
PC 10.60 **0.51 **
−0.38 **0.20
AC 10.35 **
Notes: *, **: Indicates that the correlation is significant at the 0.05 and 0.01 levels, respectively; PH: Plant height; NP: Number of panicles per plant; PL: Panicle length; GL: Grain length; GW: Grain width; LWR: Grain length-to-width ratio; FG: Total grain weight per plant; TGP: Total grain per plant; SP: Spikelet fertility; THG: Weight of 200 grains; KWG: 1000-grain weight; GY: Grain yield; PC: Protein content; AC: Amylose content; LC: Lipid content.
Table 4. The polymorphic markers, their distribution on the chromosomes, and the correlated traits.
Table 4. The polymorphic markers, their distribution on the chromosomes, and the correlated traits.
NoMarkersChr.SequencesMapped LocationMap SetCorrelated Traits
Notes: Chr.: Chromosome; cM: Centimorgan; PC: Protein content; AC: Amylose content; PH: Plant height; GY: Grain yield; SP: Spikelet fertility; GC: Gel consistency; BDR: Blast disease resistance; GT: Gelatinization temperature; BPR: Brown planthopper resistance; SN: Spikelet number, sources: [26,27].
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