Next Article in Journal
Effects of Pterostilbene on Cardiovascular Health and Disease
Previous Article in Journal
The Potential Therapeutic Effects of Tadalafil on the Endothelium in a Subarachnoid Hemorrhage Animal Model: Insights from Immunohistochemical Staining
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
CIMB
Volume 46
Issue 9
10.3390/cimb46090568
cimb-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Genetic Analysis of Vitamin C Content in Rapeseed Seedlings by the Major Gene Plus Polygene Mixed Effect Model

by
Chao Wang
1,
Tao Wang
2,
Xinfa Wang
1,
Hanzhong Wang
1 and
Xiaoling Dun
1,*
1
Key Laboratory of Biology and Genetic Improvement of Oil Crops, Oil Crops Research Institute of the Chinese Academy of Agricultural Sciences, Ministry of Agriculture, Wuhan 430062, China
2
Guizhou Rapeseed Institute, Guizhou Academy of Agricultural Science, Guiyang 550007, China
*
Author to whom correspondence should be addressed.
Curr. Issues Mol. Biol. 2024, 46(9), 9565-9575; https://doi.org/10.3390/cimb46090568
Submission received: 6 August 2024 / Revised: 27 August 2024 / Accepted: 28 August 2024 / Published: 29 August 2024
(This article belongs to the Section Molecular Plant Sciences)
Browse Figures
Versions Notes

Abstract

Rapeseed (Brassica napus L.) seedlings are rich in vitamin C (Vc), which is beneficial for humans. Understanding the genetic variance in Vc content has practical significance for the breeding of “oil–vegetable dual-purpose” rapeseed. In this study, the joint segregation analysis of a mixed genetic model of the major gene plus polygene was conducted on the Vc content in rapeseed seedlings. Six generations, including two parents, P1 (high Vc content) and P2 (low Vc content), F1, and the populations of F2, BC1P1, and BC1P2 from two crosses were investigated. Genetic analysis revealed that the genetic model MX2-A-AD was the most fitting genetic model, which indicates that Vc content is controlled by two additive major genes plus additive and dominance polygenes. In addition, the whole heritability in F2 and BC1P1 was higher than that in BC1P2. The largest coefficient of variation for Vc content appeared in the F2 generation. Therefore, for Vc content, the method of single cross recross or single backcross are suggested to transfer major genes, and the selection in F2 would be more efficient than that in other generations. Our findings provide a theoretical basis for the quantitative trait locus (QTL) mapping and breeding of Vc content in rapeseed seedlings.

1. Introduction

Rapeseed, also known as Brassica napus L. (AACC, 2n = 38), is a crucial allotetraploid plant, derived from the hybridization of Brassica rapa (AA) and Brassica oleracea (CC,); it serves as the third-largest source of vegetable oil globally [1,2]. Although the rapeseed industry in China has made great progress in recent years, the economic benefits of rapeseed are not high. Except for oil, rapeseed also has multiple functions, such as a vegetable, flower, honey, forage, and fertilizer [3]. Moreover, the planting efficiency of rapeseed can be greatly improved through “oil–vegetable dual-purpose” (OVDP), which is one of the important examples of multifunctional utilization of rapeseed in China [4,5]. In addition, our previous research found that rapeseed seedlings and rapeseed flower stalks are rich in vitamin C (Vc), selenium (Se), zinc (Zn), and other nutrients that have high edible value.
Vc, commonly known as L-ascorbic acid (AsA), is an essential metabolite in both plants and animals [6]. Numerous studies have demonstrated the role of Vc in preventing various illnesses related to oxidative stress, including cancer [7,8], cardiovascular diseases [9,10], aging [11,12], and other inflammatory diseases [13,14]. In the Institute of Medicine regulations, (Bethesda, MD, USA), it is recommended that adults consume 90 mg of Vc daily for men and 75 mg for women [15]. As an essential micro-nutrient that humans cannot synthesize, it must be acquired through dietary uptake, primarily from fresh fruits and vegetables [16,17]. In plants, Vc possesses significant reactive oxygen species (ROS) scavenging capabilities and impacts several key plant functions, such as photosynthesis, respiration, cell expansion and division, growth regulation, plant development, hormone signaling, senescence, and abiotic stress responses (such as drought, high temperature, cold damage, and salinity) [18,19,20,21,22,23]. Understanding the biosynthetic pathway of Vc and identifying the factors that regulate its levels in edible plant organs is fundamental for the enhancement of Vc levels in fruits and vegetables.
In recent years, the investigation of Vc levels in crops has gained more attention. In studies in various plants, including Arabidopsis [24,25,26], tomato [27,28,29], kiwifruit [30,31,32], apple [33], and strawberry [34], the genetic basis of Vc has been determined, which contributed significantly to the molecular breeding of crop nutritional quality. However, the nutritional analysis of rapeseed as a new vegetable, particularly regarding the genetic mechanism of Vc content, has been lacking. Therefore, the foundation for the effective and high-quality production of edible rapeseed will be laid by carrying out research to fully comprehend the genetic metabolism mechanism of Vc in rapeseed seedlings.
Various methods, such as bulked segregant analysis (BSA), mutant analysis, genome-wide association studies (GWAS), and quantitative trait loci mapping (QTL), have been commonly utilized to reveal the genetic makeup of variations crucial for breeding purposes [35]. A method of analysis utilizing a model that combines major gene and polygenes effects has been formulated for plants [36]. This mixed model has been extensively applied to different crop species, such as rice [37], cotton [38], soybean [39], rapeseed [40], non-heading Chinese cabbage [41], tomato [42], eggplant [43], jujuba [44], bearded iris [45], crape myrtle [46], and wolfberry [47], for assessing superior agronomic traits through genetic analysis. The inferences obtained through the major genes plus polygenes model have shown consistency with those yielded by QTL analysis, signifying its utility as an effective and cost-effective approach for investigating intricate quantitative traits [48].
In the present study, we used high-Vc content varieties and low-Vc content varieties as parental lines in two breeding crosses. Through six generations (P1, P2, F1, F2, BC1P1, and BC1P2), a diverse genetic pool was obtained. The genetic model combining major gene effects with polygenic inheritance was employed to elucidate the genetic mechanisms governing the Vc content of rapeseed seedlings. Additionally, we assessed both major genetic contributions and overall genetic forces influencing Vc levels. These findings offer valuable insights for future QTL analysis, marker-assisted selection breeding programs, and the development of cultivars with enhanced Vc content in rapeseed.

2. Materials and Methods

2.1. Plant Material

The parent materials, B. rapus 8S079 and 8S243, selected for this study are high in Vc content, while B. rapus 8S007 and 8S084 are not. In October 2022, the parental materials were sown at the Yangluo Experimental Base of the Oil Crop Research Institute, Chinese Academy of Agricultural Sciences, Wuhan City, Hubei Province. In March 2023, two hybrid crosses were created through artificial emasculation and pollination: 8S079 (P1) × 8S084 (P2) (Cross A) and 8S243 (P1) × 8S007 (P2) (Cross B), also including their reciprocal crosses. In May 2023, the parental materials of the two hybrid crosses and their F1 generation were planted at the Ping’an Northern Propagation Experimental Base of the Chinese Academy of Agricultural Sciences in Qinghai Province. During the flowering period, F1 × P1 (BC1P1) and F1 × P2 (BC1P2) crosses were prepared. The F1 plant was used to self-cross to produce the F2 generation. Ultimately, seeds from six generations (P1, P2, F1, BC1P1, BC1P2, and F2) were obtained for the two hybrid crosses.

2.2. Growth Conditions

The six generations of the two hybrid crosses were cultivated in a greenhouse. Rapeseed seeds were initially placed on medical gauze within a germination device for 2 days in the dark at 24 °C, followed by growth under light conditions (180 µmol·m−2·s−1, 16/8 h) for 4 days in a greenhouse with 60–80% relative humidity and an air temperature of 24 ± 2 °C. One quarter of modified Hoagland’s nutrient solution was added to the germination device to maintain moisture and provide nutrients for seed germination. After six days, uniform seedlings were moved to a growth device with one quarter of Hoagland’s solution, which was later replaced with one half of Hoagland’s solution. Following 16 days of growth, samples of the above-ground tissue of rapeseed seedlings were collected to determine the Vc content.

2.3. Extraction and Determination of Vc Content

Samples of rapeseed seedlings were collected and promptly frozen in liquid nitrogen, and then ground into a fine powder. Approximately 1.0 g of ground sample was dissolved in a 25 mL solution of 0.1% hydrochloric acid solution and treated by a high shear dispersing emulsifier for 60 s. Next, the liquid was filtered through a 0.22 μm filter and 10.0 μL of the supernatant was injected into the HPLC-PDA system. The HPLC-PDA was performed using a Waters e2695 system (Milford, MA, USA) including a Waters e2695 Separations Module and a Waters 2998 UV-Vis Photo-diode Array Detector (PAD) with an autosampler. The HPLC-PDA system conditions were as follows: C18 column (250 mm × 4.6 mm × 5 μm, CNW) (Shanghai, China) with the temperature of 30 °C and detected at 245 nm wavelengths; methanol/20 mM ammonium acetate (3:97, v/v) as an affluent phase with the flow rate of 1.0 mL/min; retention time, 2.67 min (Figure S1). According to the standard curve of Vc content produced, it was used as a reference standard to quantify the Vc content in the rapeseed seedlings (Figure S2).

2.4. Statistical and Genetic Analysis

Statistical analysis was performed using SPSS 26.0 (IBM SPSS Statistics 26.0) statistical software. Frequency histograms were plotted using Origin 2024 (2024b, Origin Laboratory) software. In the figures, different letters above the bars indicate significant differences (p < 0.05) as determined by a one-way ANOVA test, the data represented the mean values, and the error bars represent the standard error of the mean. The R software package SEA v2.0 (https://cran.r-project.org/web/packages/SEA/index.html, accessed on 10 April 2024) was used to conduct a quantitative trait major gene plus polygene analysis on the Vc content of rapeseed seedlings across six generations [49]. The software for segregation analysis was provided by the team of the Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, China. The analysis included 24 types of genetic models, with maximum likelihood values and Akaike’s information criterion (AIC) calculated. Two alternative models were chosen based on minimum AIC value and subjected to suitability tests, including uniformity tests (U12, U22, U32), Smirnov’s statistics (nW2), and Kolmogorov’s statistics (Dn). If no significant difference was found, the model with the smaller parameter value will be selected. Using the least-squares method, we estimated the genetic parameters of the optimal genetic model in the first and second orders. Afterward, based on the estimates of component distributions in the optimal genetic model, the genetic parameters pertaining to the gene effects, genetic variances, and heritability of the major genes were computed.

3. Results

3.1. Statistical Analysis of Vc Content in Six Generations from Two Crosses

As shown in Figure 1, there were significant differences in the Vc content among the two parents, P1 (high Vc content) and P2 (low Vc content), and the hybrid offspring (F1) generations of crosses A and B (p < 0.05), which enabled the segregation of vitamin C content in their offspring. The Vc content of F1 was intermediate between the two parents, with a slight bias towards the high-value parent. There was no significant difference in the Vc content of F1 between reciprocal crosses.
The results in Table 1 show that in crosses A and B, the BC1P1 generation exhibited the highest Vc content (125.52 and 122.81 mg/100 g, respectively) compared to the other offspring generations. The coefficient of variation (CV) varied from 9.11% to 20.07% and from 7.31% to 25.05% in the offspring generations of crosses A and B, respectively, indicating a wide range of phenotypic performances among the generations. Notably, the F2 generation of the two crosses showed the largest CV (20.07 and 25.05%, respectively) for Vc content. This segregation pattern in Vc content among the offspring made them suitable for further genetic analysis.

3.2. Distribution of Vc Content in Segregated Populations of Two Crosses

In cross A, the skewness and kurtosis values of the Vc content in the generations of BC1P1, BC1P2, and F2 were all smaller than 1. A similar phenomenon was identified in cross B, except for the kurtosis values of F2 (Table 2). Generally, these data suggest that the distributions of the Vc content in the three hybrid offspring followed a normal pattern. Figure 2 shows the frequency distributions of the Vc content across the four offspring generations in the two crosses. The generation of BC1P1 showed a skewed content of Vc towards P1 in both crosses, while BC1P2 was skewed towards P2. These findings demonstrate that the genetic characteristics of Vc content in rapeseed seedlings follow a typical quantitative trait pattern, suggesting a genetic model of the major gene plus polygene interactions influencing Vc content, which could be valuable in selecting superior individuals in breeding programs.

3.3. Selection and Testing for the Best Genetic Model of Vc Content

The Vc content of rapeseed seedlings was analyzed for the six generations using a major gene plus polygene mixed genetic model. Then, 24 types of genetic models were evaluated by calculating values for the maximum likelihood method and Akaike’s information criterion (AIC) (Table 3). A lower AIC value indicates that the model offers a more precise representation of the data, implying that its predictive error is minimized. The model with the minimum AIC value was determined to be the best candidate model [41]. Among the 24 genetic models, two genetic models were chosen as potential candidate models based on their relatively low AIC values. In both crosses A and B, the genetic model of MX2-A-AD had the lowest estimated AIC value, followed by MX2-ADI-AD and MX2-ADI-ADI, respectively.
Furthermore, tests of goodness-of-fit were performed for the candidate models to determine the most suitable one. Then, five statistical parameters were evaluated, including equal distribution (U21, U22, and U23), Smirnov (nW2), and Kolmogorov tests (Dn). The results of the goodness-of-fit tests for the two crosses are presented in Table 4. The model that achieved significance with the smallest number of statistics (p < 0.05) was chosen as the most optimal model [42]. When no significant differences were observed among the candidate models, the preferred model was selected based on the lowest AIC value. The results suggest that the statistical comparison between the two models in crosses A and B did not show significant differences.
Based on the lowest AIC value, MX2-A-AD was chosen as the optimal genetic model for the Vc content in rapeseed seedlings. This suggests that the Vc content in rapeseed seedlings was affected by two major additive genes plus additive and dominant polygenic effects.

3.4. Estimation of Genetic Parameters for the Optimal Genetic Model of Vc Content

Table 5 lists the genetic models of the first-order parameters related to Vc content in the MX2-A-AD genetic model of crosses A and B. The study indicated that interactions between two pairs of major genes influenced the Vc content, with the additive effects of the first major gene being more significant than those of the second major gene. The additive effects of the two gene pairs were 22.43 and −6.34, and 30.72 and −9.61, respectively. The above results show that the inheritance of Vc content was primarily driven by the additive effect of the first pair of major genes, resulting in a positive synergistic effect. Furthermore, the additive and dominant effects of the polygenes were 19.65 and −0.52, and 15.95 and 3.20, respectively. The dominance of the additive effects of polygenes in the inheritance of Vc content was highlighted by the fact that the additive effect was greater than the dominant effect.
The genetic parameters in the second order of the optimal genetic model for Vc content in rapeseed seedlings were analyzed, as shown in Table 6. The heritabilities of the BC1P1, BC1P2, and F2 segregating generations resulting from crosses A and B are reported as follows: 86.60% and 82.83%, 66.97% and 76.69%, and 82.50% and 88.00%, respectively. Among these, the major genes contributed heritabilities of 42.05% and 74.15%, 54.92% and 51.52%, and 82.50% and 88.00%, respectively, while the polygenes accounted for 44.55% and 8.68%, 12.05% and 25.48%, and 0.00% and 0.00%, respectively. The major gene responsible for Vc content exhibited the highest heritabilities in the BC1P1 and F2 generations. Moreover, the environmental variance in the segregating generations of crosses A and B, including BC1P1, BC1P2, and F2, was estimated at 71.93 and 67.07, respectively. This estimation was based on P1, P2, and F1, with contributions of 13.40% and 17.17%, 33.03% and 23.31%, and 17.50% and 12.00%, respectively, to the phenotypic variance. These findings indicate that genetic factors play a significant role in controlling the Vc content of rapeseed seedlings, while environmental factors also have a substantial impact.

4. Discussion

Rapeseed (B. napus L.) evolved from the hybridization and doubling of two basic species, B. rapa and B. oleracea [50]. Brassica species have been associated with many beneficial health effects recently, such as potential anti-carcinogenic properties and the ability to protect against cardiovascular illnesses, aging, prenatal disorders, etc. [51,52,53,54,55]. The abundance of health-promoting phytochemicals such as carotenoids, phenolic compounds, glucosinolates, vitamins, and minerals is responsible for these advantages [56,57,58,59]. Vc is widely favored by the general public for its antioxidant properties. Cruciferous plants contain a variety of health-promoting compounds and are recognized as rich sources of Vc [60]. Studies have shown that Vc content is a crucial quality and agronomic trait in non-heading Chinese cabbage, which is influenced by multiple genes, making it a quantitative trait with a complex genetic mechanism [61]. Furthermore, Vc content could serve as an indicator for the quality breeding of OVDP rapeseed. However, until now, no research on Vc in rapeseed has been reported.
In this study, joint segregation analysis was utilized to analyze the inheritance and gene effects on Vc content in rapeseed seedlings across six generations from two crosses (8S079 × 8S084 and 8S243 × 8S007). The results show that the Vc content in rapeseed seedlings is governed by two major genes with additive effects. Similar to the inheritance patterns observed in vegetables and fruits, rapeseed seedlings exhibit complex genetic patterns of Vc content. The analysis of inheritance for plant quantitative traits is often conducted using a combination of major gene and polygene inheritance. In recent years, this approach has been applied to analyze the vitamin E content in soybeans [62], the chlorophyll content in maize [63], the vitamin P content in eggplant [43], the Vc content in non-heading Chinese cabbage [64] and pepper [65], and the fruit length in cucumbers [66]. Some studies have shown that the Vc content of certain plants was found to involve a pair of major genes. For example, in non-heading Chinese cabbage, the Vc content was controlled by a pair of additive major genes and additive dominant polygenes, as revealed by the joint analysis of six generations [64]. Similarly, the optimum model for the Vc content in cucumber was determined to be a combination of additive major genes and additive dominant polygenes [67]. However, the genetic model for Vc content in rapeseed seedlings in this study is inconsistent with other crops, which could be attributed to variations in the genetic background and biological environment of the research materials, resulting in potential biases in the estimation of model parameters and genetic parameters. Additionally, it is possible that the observed inconsistency is influenced by more intricate genetic regulations.
The heritabilities of major genes for Vc content in the BC1P1, BC1P2, and F2 generations of two crosses were estimated to range from 42.05% to 82.50% and 51.21% to 88.00%, respectively. The environmental variance in phenotypic variance for the two crosses was estimated to range from 13.40% to 33.03% and 12.00% to 23.31%, respectively, indicating a significant role of environmental factors in the genetic Vc content of rapeseed seedlings. The results reveal that the overall heritability of Vc content in the F2 and BC1P1 generations was consistently higher than that in the BC1P2 generation for each cross. Moreover, the F2 generation of both crosses exhibited the largest coefficient of variation for Vc content, suggesting that selection in F2 could breed rapeseed germplasm with a higher Vc content. Based on the joint segregation analysis of phenotypic data, the study identified the impact of major genes, polygenes, and their interactions on the Vc content of rapeseed seedlings. This provides valuable insights for improving future Vc levels in the future OVDP rapeseed breeding programs. In spite of the advances made in this research, we could not locate the major genes and polygenes on a particular chromosome for now, which needs in depth investigations in future studies.

5. Conclusions

Rapeseed (B. napus L.) seedlings are a rich source of Vc, with potential benefits for human health. However, the inheritance patterns of this nutritional trait have not yet been fully investigated. An analysis using a major gene plus polygene mixed inheritance model suggests that the Vc content in rapeseed seedlings conforms to the MX2-A-AD model, indicating that it is controlled by two additive major genes plus additive and dominance polygenes. To enhance the Vc content in rapeseed seedlings, a recommended approach involves the use of single cross recross or single backcross methods to transfer major genes, with a more efficient selection expected in the F2 generation. In addition, environmental conditions should also be considered in the breeding process. The results of this study provide a foundational basis for the future QTL mapping of nutritional traits and offer valuable insights for targeted breeding to enhance Vc content in rapeseed seedlings. All in all, cultivating high-Vc rapeseed varieties could elevate the nutritional value of “oil–vegetable dual-purpose” rapeseed, which provides consumers with a healthy and nutritious vegetable.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/cimb46090568/s1: Figure S1: Chromatogram of vitamin C in rapeseed seedlings; Figure S2: Standard operating curve of vitamin C.

Author Contributions

C.W. wrote the manuscript and analyzed the data; C.W. and T.W. performed the experiments; X.W. contributed the plant materials; X.D. and H.W. conceived the paper and edited the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Science and Technology Major Program of Hubei Province (2022ABA001), the Talented Scientist Project of Qinghai Province (2023-NK-145), and the Central Public-interest Scientific Institution Basal Research Fund (1610172021002).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data are contained within the article.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Ihien Katche, E.; Mason, A.S. Resynthesized rapeseed (Brassica napus): Breeding and genomics. Crit. Rev. Plant Sci. 2023, 42, 65–92. [Google Scholar] [CrossRef]
  2. Zheng, M.; Terzaghi, W.; Wang, H.; Hua, W. Integrated strategies for increasing rapeseed yield. Trends Plant Sci. 2022, 27, 742–745. [Google Scholar] [CrossRef]
  3. Xiao, Z.; Pan, Y.; Wang, C.; Li, X.; Lu, Y.; Tian, Z.; Kuang, L.; Wang, X.; Dun, X.; Wang, H. Multi-functional development and utilization of rapeseed: Comprehensive analysis of the nutritional value of rapeseed sprouts. Foods 2022, 11, 778. [Google Scholar] [CrossRef]
  4. Wu, X.; Chen, F.; Zhao, X.; Pang, C.; Shi, R.; Liu, C.; Sun, C.; Zhang, W.; Wang, X.; Zhang, J. QTL mapping and GWAS reveal the genetic mechanism controlling soluble solids content in Brassica napus shoots. Foods 2021, 10, 2400. [Google Scholar] [CrossRef] [PubMed]
  5. Shi, R.; Pang, C.; Wu, X.; Zhao, X.; Chen, F.; Zhang, W.; Sun, C.; Fu, S.; Hu, M.; Zhang, J.; et al. Genetic dissection and germplasm selection of the low crude fiber component in Brassica napus L. shoots. Foods 2023, 12, 403. [Google Scholar] [CrossRef] [PubMed]
  6. Smirnoff, N. Ascorbic acid metabolism and functions: A comparison of plants and mammals. Free Radic. Biol. Med. 2018, 122, 116–129. [Google Scholar] [CrossRef] [PubMed]
  7. Zasowska-Nowak, A.; Nowak, P.J.; Ciałkowska-Rysz, A. High-dose vitamin C in advanced-stage cancer patients. Nutrients 2021, 13, 735. [Google Scholar] [CrossRef]
  8. Blaszczak, W.; Barczak, W.; Masternak, J.; Kopczyński, P.; Zhitkovich, A.; Rubiś, B. Vitamin C as a modulator of the response to cancer therapy. Molecules 2019, 24, 453. [Google Scholar] [CrossRef]
  9. Morelli, M.B.; Gambardella, J.; Castellanos, V.; Trimarco, V.; Santulli, G. Vitamin C and cardiovascular disease: An Update. Antioxidants 2020, 9, 1227. [Google Scholar] [CrossRef]
  10. Collins, B.J.; Mukherjee, M.S.; Miller, M.D.; Delaney, C.L. Effect of dietary or supplemental vitamin C intake on vitamin C levels in patients with and without cardiovascular disease: A systematic review. Nutrients 2021, 13, 2330. [Google Scholar] [CrossRef]
  11. Monacelli, F.; Acquarone, E.; Giannotti, C.; Borghi, R.; Nencioni, A. Vitamin C, aging and Alzheimer’s disease. Nutrients 2017, 9, 670. [Google Scholar] [CrossRef] [PubMed]
  12. Mumtaz, S.; Ali, S.; Tahir, H.M.; Kazmi, S.A.R.; Shakir, H.A.; Mughal, T.A.; Mumtaz, S.; Summer, M.; Farooq, M.A. Aging and its treatment with vitamin C: A comprehensive mechanistic review. Mol. Biol. Rep. 2021, 48, 8141–8153. [Google Scholar] [CrossRef] [PubMed]
  13. Ratajczak, A.E.; Szymczak-Tomczak, A.; Skrzypczak-Zielińska, M.; Rychter, A.M.; Zawada, A.; Dobrowolska, A.; Krela-Kaźmierczak, I. Vitamin C deficiency and the risk of osteoporosis in patients with an inflammatory bowel disease. Nutrients 2020, 12, 2263. [Google Scholar] [CrossRef] [PubMed]
  14. Xia, G.; Fan, D.; He, Y.; Zhu, Y.; Zheng, Q. High-dose intravenous vitamin C attenuates hyperinflammation in severe coronavirus disease 2019. Nutrition 2021, 91–92, 111405. [Google Scholar] [CrossRef] [PubMed]
  15. Fenech, M.; Amaya, I.; Valpuesta, V.; Botella, M.A. Vitamin C content in fruits: Biosynthesis and regulation. Front. Plant Sci. 2018, 9, 2006. [Google Scholar] [CrossRef] [PubMed]
  16. Bulley, S.; Laing, W. The regulation of ascorbate biosynthesis. Curr. Opin. Plant Biol. 2016, 33, 15–22. [Google Scholar] [CrossRef]
  17. Haroldsen, V.M.; Chi-Ham, C.L.; Kulkarni, S.; Lorence, A.; Bennett, A.B. Constitutively expressed DHAR and MDHAR influence fruit, but not foliar ascorbate levels in tomato. Plant Physiol. Biochem. 2011, 49, 1244–1249. [Google Scholar] [CrossRef]
  18. Zheng, X.; Gong, M.; Zhang, Q.; Tan, H.; Li, L.; Tang, Y.; Li, Z.; Peng, M.; Deng, W. Metabolism and regulation of ascorbic acid in fruits. Plants 2022, 11, 1602. [Google Scholar] [CrossRef] [PubMed]
  19. Ntagkas, N.; Woltering, E.; Nicole, C.; Labrie, C.; Marcelis, L.F.M. Light regulation of vitamin C in tomato fruit is mediated through photosynthesis. Environ. Exp. Bot. 2019, 158, 180–188. [Google Scholar] [CrossRef]
  20. Zhang, H.; Xiang, Y.; He, N.; Liu, X.; Liu, H.; Fang, L.; Zhang, F.; Sun, X.; Zhang, D.; Li, X.; et al. Enhanced vitamin C production mediated by an ABA-Induced PTP-like nucleotidase improves plant drought tolerance in Arabidopsis and Maize. Mol. Plant 2020, 13, 760–776. [Google Scholar] [CrossRef]
  21. Gallie, D.R. The role of l-ascorbic acid recycling in responding to environmental stress and in promoting plant growth. J. Exp. Bot. 2013, 64, 433–443. [Google Scholar] [CrossRef] [PubMed]
  22. Chaturvedi, S.; Khan, S.; Bhunia, R.K.; Kaur, K.; Tiwari, S. Metabolic engineering in food crops to enhance ascorbic acid production: Crop biofortification perspectives for human health. Physiol. Mol. Biol. Plants 2022, 28, 871–884. [Google Scholar] [CrossRef]
  23. Celi, G.E.A.; Gratão, P.L.; Lanza, M.; Reis, A.R.D. Physiological and biochemical roles of ascorbic acid on mitigation of abiotic stresses in plants. Plant Physiol. Biochem. 2023, 202, 107970. [Google Scholar] [CrossRef] [PubMed]
  24. Zhang, W.; Lorence, A.; Gruszewski, H.A.; Chevone, B.I.; Nessler, C.L. AMR1, an Arabidopsis gene that coordinately and negatively regulates the mannose/l-galactose ascorbic acid biosynthetic pathway. Plant Physiol. 2009, 150, 942–950. [Google Scholar] [CrossRef]
  25. Qin, C.; Qian, W.; Wang, W.; Wu, Y.; Yu, C.; Jiang, X.; Wang, D.; Wu, P. GDP-mannose pyrophosphorylase is a genetic determinant of ammonium sensitivity in Arabidopsis thaliana. Proc. Natl. Acad. Sci. USA 2008, 105, 18308–18313. [Google Scholar] [CrossRef] [PubMed]
  26. Hoeberichts, F.A.; Vaeck, E.; Kiddle, G.; Coppens, E.; Van de Cotte, B.; Adamantidis, A.; Ormenese, S.; Foyer, C.H.; Zabeau, M.; Inzé, D.; et al. A temperature-sensitive mutation in the Arabidopsis thaliana phosphomannomutase gene disrupts protein glycosylation and triggers cell death. J. Biol. Chem. 2008, 283, 5708–5718. [Google Scholar] [CrossRef]
  27. Hu, T.; Ye, J.; Tao, P.; Li, H.; Zhang, J.; Zhang, Y.; Ye, Z. The tomato HD-Zip I transcription factor SlHZ24 modulates ascorbate accumulation through positive regulation of the D-mannose/L-galactose pathway. Plant J. 2016, 85, 16–29. [Google Scholar] [CrossRef]
  28. Ye, J.; Li, W.; Ai, G.; Li, C.; Liu, G.; Chen, W.; Wang, B.; Wang, W.; Lu, Y.; Zhang, J.; et al. Genome-wide association analysis identifies a natural variation in basic helix-loop-helix transcription factor regulating ascorbate biosynthesis via D-mannose/L-galactose pathway in tomato. PLoS Genet. 2019, 15, e1008149. [Google Scholar] [CrossRef]
  29. Chen, W.; Hu, T.; Ye, J.; Wang, B.; Liu, G.; Wang, Y.; Yuan, L.; Li, J.; Li, F.; Ye, Z.; et al. A CCAAT-binding factor, SlNFYA10, negatively regulates ascorbate accumulation by modulating the D-mannose/L-galactose pathway in tomato. Hortic. Res. 2020, 7, 200. [Google Scholar] [CrossRef]
  30. Liu, X.; Wu, R.; Bulley, S.M.; Zhong, C.; Li, D. Kiwifruit MYBS1-like and GBF3 transcription factors influence l-ascorbic acid biosynthesis by activating transcription of GDP-L-galactose phosphorylase 3. New Phytol. 2022, 234, 1782–1800. [Google Scholar] [CrossRef]
  31. Liu, X.; Bulley, S.M.; Varkonyi-Gasic, E.; Zhong, C.; Li, D. Kiwifruit bZIP transcription factor AcePosF21 elicits ascorbic acid biosynthesis during cold stress. Plant Physiol. 2023, 192, 982–999. [Google Scholar] [CrossRef]
  32. Chen, Y.; Shu, P.; Wang, R.; Du, X.; Xie, Y.; Du, K.; Deng, H.; Li, M.; Zhang, Y.; Grierson, D.; et al. Ethylene response factor AcERF91 affects ascorbate metabolism via regulation of GDP-galactose phosphorylase encoding gene (AcGGP3) in kiwifruit. Plant Sci. 2021, 313, 111063. [Google Scholar] [CrossRef] [PubMed]
  33. Davey, M.W.; Kenis, K.; Keulemans, J. Genetic control of fruit vitamin C contents. Plant Physiol. 2006, 142, 343–351. [Google Scholar] [CrossRef] [PubMed]
  34. Zorrilla-Fontanesi, Y.; Cabeza, A.; Domínguez, P.; Medina, J.J.; Valpuesta, V.; Denoyes-Rothan, B.; Sánchez-Sevilla, J.F.; Amaya, I. Quantitative trait loci and underlying candidate genes controlling agronomical and fruit quality traits in octoploid strawberry (Fragaria × ananassa). Theor. Appl. Genet. 2011, 123, 755–778. [Google Scholar] [CrossRef]
  35. Du, X.; Wang, H.; Liu, J.; Zhu, X.; Mu, J.; Feng, X.; Liu, H. Major gene with polygene inheritance analysis of shoot architecture traits in Viola cornuta. Sci. Hortic. 2022, 303, 111204. [Google Scholar] [CrossRef]
  36. Gai, J.; Wang, J.K. Identification and estimation of a QTL model and its effects. Theor. Appl. Genet. 1998, 97, 1162–1168. [Google Scholar] [CrossRef]
  37. Zheng, W.; Liu, Z.; Zhao, J.; Chen, W. Genetic Analysis of stripe disease resistance in rice restorer line C224 using major gene plus polygene mixed effect model. Rice Sci. 2012, 19, 202–206. [Google Scholar] [CrossRef]
  38. Zhang, X.; Li, C.; Wang, X.; Chen, G.; Zhang, J.; Zhou, R. Genetic analysis of cryotolerance in cotton during the overwintering period using mixed model of major gene and polygene. J. Integr. Agric. 2012, 11, 537–544. [Google Scholar] [CrossRef]
  39. Korir, P.C.; Wang, J.; Zhao, T.; Gai, J. Genetic analysis of tolerance to aluminum toxin at seedling stage in soybean based on major gene plus polygene mixed inheritance model. Front. Agric. China 2010, 4, 265–271. [Google Scholar] [CrossRef]
  40. Zhang, S.; Ma, C.; Zhu, J.; Wang, J.; Wen, Y.; Fu, T. Genetic analysis of oil content in Brassica napus L. using mixed model of major gene and polygene. Acta Genet. Sin. 2006, 33, 171–180. [Google Scholar] [CrossRef]
  41. Cao, X.; Cui, H.; Li, J.; Xiong, A.; Hou, X.; Li, Y. Heritability and gene effects for tiller number and leaf number in non-heading Chinese cabbage using joint segregation analysis. Sci. Hortiic. 2016, 203, 199–206. [Google Scholar] [CrossRef]
  42. Sun, X.; Liu, L.; Zhi, X.; Bai, J.; Cui, Y.; Shu, J.; Li, J. Genetic analysis of tomato internode length via mixed major gene plus polygene inheritance model. Sci. Hortic. 2019, 246, 759–764. [Google Scholar] [CrossRef]
  43. Dong, R.; Yu, B.; Yan, S.; Qiu, Z.; Lei, J.; Chen, C.; Li, Y.; Cao, B. Analysis of vitamin P content and inheritance models in eggplant. Hortic. Plant J. 2020, 6, 240–246. [Google Scholar] [CrossRef]
  44. Yang, Z.; Zhang, C.; Yang, X.; Dong, M.; Wang, Z.; Yan, F.; Wu, C.; Wang, J.; Liu, M.; Lin, M. Genetic analysis of mixed models of fruit sugar-acid fractions in a cross between jujube (Ziziphus jujuba Mill.) and wild jujube (Z. acido jujuba). Front. Plant Sci. 2023, 14, 1181903. [Google Scholar]
  45. Fan, Z.; Gao, Y.; Liu, R.; Wang, X.; Guo, Y.; Zhang, Q. The major gene and polygene effects of ornamental traits in bearded iris (Iris germanica) using joint segregation analysis. Scie Hortic. 2020, 260, 108882. [Google Scholar] [CrossRef]
  46. Ye, Y.; Wu, J.; Feng, L.; Ju, Y.; Cai, M.; Cheng, T.; Pan, H.; Zhang, Q. Heritability and gene effects for plant architecture traits of crape myrtle using major gene plus polygene inheritance analysis. Sci. Hortic. 2017, 225, 335–342. [Google Scholar] [CrossRef]
  47. Ren, X.; Li, H.; Yin, Y.; Duan, L.; Wang, Y.; Liang, X.; Wan, R.; Huang, T.; Zhang, B.; Xi, W.; et al. Genetic analysis of fruit traits in wolfberry (Lycium L.) by the major gene plus polygene model. Agronomy 2022, 12, 1403. [Google Scholar] [CrossRef]
  48. Qi, Z.; Li, J.; Raza, M.A.; Zou, X.; Cao, L.; Rao, L.; Chen, L. Inheritance of fruit cracking resistance of melon (Cucumis melo L.) fitting E-0 genetic model using major gene plus polygene inheritance analysis. Sci. Hortic. 2015, 189, 168–174. [Google Scholar] [CrossRef]
  49. Wang, J.; Zhang, Y.; Du, Y.; Ren, W.; Li, H.; Sun, W.; Ge, C.; Zhang, Y. SEA v2.0: An R software package for mixed major genes plus polygenes inheritance analysis of quantitative traits. Acta Agron. Sin. 2022, 48, 1416–1424. [Google Scholar] [CrossRef]
  50. Lu, K.; Wei, L.; Li, X.; Wang, Y.; Wu, J.; Liu, M.; Zhang, C.; Chen, Z.; Xiao, Z.; Jian, H.; et al. Whole-genome resequencing reveals Brassica napus origin and genetic loci involved in its improvement. Nat. Commun. 2019, 10, 1154. [Google Scholar] [CrossRef] [PubMed]
  51. Kim, S.Y.; Yoon, S.; Kwon, S.M.; Park, K.S.; Lee-Kim, Y.C. Kale juice improves coronary artery disease risk factors in hypercholesterolemic men. Biomed. Environ. Sci. 2008, 21, 91–97. [Google Scholar] [CrossRef]
  52. Kataya, H.A.; Hamza, A.A. Red cabbage (Brassica oleracea) ameliorates diabetic nephropathy in rats. Evid. Based Complement. Alternat. Med. 2008, 5, 281–287. [Google Scholar] [CrossRef] [PubMed]
  53. Tiku, A.B.; Abraham, S.K.; Kale, R.K. Protective effect of the cruciferous vegetable mustard leaf (Brassica campestris) against in vivo chromosomal damage and oxidative stress induced by gamma-radiation and genotoxic chemicals. Environ. Mol. Mutagen. 2008, 49, 335–342. [Google Scholar] [CrossRef] [PubMed]
  54. Akhlaghi, M.; Bandy, B. Dietary broccoli sprouts protect against myocardial oxidative damage and cell death during ischemia-reperfusion. Plant Foods Hum. Nutr. 2010, 65, 193–199. [Google Scholar] [CrossRef]
  55. Raiola, A.; Errico, A.; Petruk, G.; Monti, D.M.; Barone, A.; Rigano, M.M. Bioactive compounds in Brassicaceae vegetables with a role in the prevention of chronic diseases. Molecules 2017, 23, 15. [Google Scholar] [CrossRef]
  56. Heimler, D.; Vignolini, P.; Dini, M.G.; Vincieri, F.F.; Romani, A. Antiradical activity and polyphenol composition of local Brassicaceae edible varieties. Food Chem. 2006, 99, 464–469. [Google Scholar] [CrossRef]
  57. Fernandes, F.; Valentao, P.; Sousa, C.; Pereira, J.; Seabra, R.; Andrade, P. Chemical and antioxidative assessment of dietary turnip (Brassica rapa var. rapa L.). Food Chem. 2007, 105, 1003–1010. [Google Scholar] [CrossRef]
  58. Yang, J.; Zhu, Z.; Wang, Z.; Zhu, B. Effects of storage temperature on the contents of carotenoids and glucosinolates in pakchoi (Brassica rapa L. ssp. Chinensis Var. Communis). J. Food Biochem. 2010, 34, 1186–1204. [Google Scholar] [CrossRef]
  59. Pérez-Balibrea, S.; Moreno, D.A.; García-Viguera, C. Genotypic effects on the phytochemical quality of seeds and sprouts from commercial broccoli cultivars. Food Chem. 2011, 125, 348–354. [Google Scholar] [CrossRef]
  60. Domínguez-Perles, R.; Mena, P.; García-Viguera, C.; Moreno, D.A. Brassica foods as a dietary source of vitamin C: A review. Crit. Rev. Food Sci. Nutr. 2014, 54, 1076–1091. [Google Scholar] [CrossRef]
  61. Zhan, Z.; Hou, X.; Cao, S. Genetic analysis of vitamine C and soluble sugar contents in nonheading Chinese cabbage (Brassica campestris ssp. chinensis Makino). Acta Hortic. Sin. 1999, 26, 170–174. [Google Scholar]
  62. Li, H.; Han, Y.; Zhao, X.; Li, W. Analysis of genetic model on vitamin E composition contents in soybean seed in multiple locations. Oil Crop Sci. 2014, 36, 450–454. [Google Scholar]
  63. Li, H.; Xu, H.; Li, J.; Zhu, Q.; Chi, M.; Wang, J. Analysis of gene effect on chlorophyll content in maize. Crops 2019, 5, 46–51. [Google Scholar]
  64. Lin, T.; Wang, J.; Wang, L.; Chen, X.; Hou, X.; Li, Y. Major gene plus polygene inheritance of vitamin C content in non-heading Chinese cabbage. Acta Agron. Sin. 2014, 40, 1733–1739. [Google Scholar] [CrossRef]
  65. Wang, X.; Chen, L.; Hu, S.; Fu, L.; Xie, L.; Quan, H. Genetic analysis of vitamin C in pepper fruit. Chin. Agric. Sci. Bull. 2017, 33, 49–53. [Google Scholar]
  66. Song, Z.; Miao, H.; Zhang, S.; Wang, Y.; Zhang, S.; Gu, X. Genetic analysis and QTL mapping of fruit peduncle length in cucumber (Cucumis sativus L.). PLoS ONE 2016, 11, e0167845. [Google Scholar] [CrossRef] [PubMed]
  67. Xu, W.; Si, L.; Min, Y.; Gao, P.; Meng, Q.; Li, K. Genetic analysis of vitamin C content of the south China type cucumber. Acta Agric. Boreali Sin. 2012, 27, 102–106. [Google Scholar]
Cimb 46 00568 g001
Figure 1. Comparison of Vc content among the parents (P1 and P2) and F1 generation of crosses A and B. Different letters above the bars indicate significant differences (p < 0.05) as determined by a one-way ANOVA test. FW, fresh weight.
Figure 1. Comparison of Vc content among the parents (P1 and P2) and F1 generation of crosses A and B. Different letters above the bars indicate significant differences (p < 0.05) as determined by a one-way ANOVA test. FW, fresh weight.
Cimb 46 00568 g001
Cimb 46 00568 g002
Figure 2. Frequency distributions of style Vc content in four generations of two crosses. (a) Cross A (8S079 × 8S084); (b) cross B (8S243 × 8S007). FW, fresh weight.
Figure 2. Frequency distributions of style Vc content in four generations of two crosses. (a) Cross A (8S079 × 8S084); (b) cross B (8S243 × 8S007). FW, fresh weight.
Cimb 46 00568 g002
Table 1. Statistical analysis of Vc content in six generations of crosses A and B.
Table 1. Statistical analysis of Vc content in six generations of crosses A and B.
CrossGenerationNo. of Plants Minimum
(mg 100 g−1)		Maximum
(mg 100 g−1)		Mean
(mg 100 g−1)		SDVarianceCV
(%)
Cross A
8S079 × 8S084		P150125.02 160.02 141.24 a 8.27 68.37 5.85
P25062.76 90.27 78.58 f 7.26 52.64 9.23
F14696.64 135.68 112.22 c10.22 104.54 9.11
F221757.70 163.86 101.00 d20.27 410.95 20.07
BC1P112878.25 181.67 125.52 b23.17 536.94 18.46
BC1P212355.30 118.36 87.40 e 14.76 217.78 16.89
Cross B
8S243 × 8S007		P150122.79 154.89 139.34 a9.15 83.73 6.57
P25062.75 89.02 74.30 f7.30 53.24 9.82
F14899.74 129.13 112.94 c8.26 68.20 7.31
F224547.23 174.65 94.37 d23.64 558.84 25.05
BC1P111985.32 170.50 122.81 b19.77 390.70 16.09
BC1P211154.54 123.66 87.81 e16.96 287.71 19.32
Note: values marked with different letters indicate statistically significant differences (p < 0.05).
Table 2. The skewness and kurtosis values of Vc content in six generations of crosses A and B.
Table 2. The skewness and kurtosis values of Vc content in six generations of crosses A and B.
CrossGenerationP1F1F1F2BC1P1BC1P2
Cross ASkewness0.21−0.180.250.760.360.08
Kurtosis−0.57−0.94−0.960.55−0.60−0.69
Cross BSkewness0.090.350.120.950.500.18
Kurtosis−1.16−0.54−1.081.35−0.29−0.59
Table 3. Estimation of the maximum likelihood values and AIC values of 24 different genetic models for crosses A and B.
Table 3. Estimation of the maximum likelihood values and AIC values of 24 different genetic models for crosses A and B.
ModelMaximum LikelihoodAIC Value
Cross ACross BCross ACross B
1MG-AD−2587.82−2668.375183.645344.75
1MG-A−2602.40−2671.645210.795349.28
1MG-EAD−2735.58−2795.435477.155596.86
1MG-NCD−2673.71−2749.705353.425505.40
2MG-ADI−2574.38−2624.145168.765268.28
2MG-AD−2579.41−2642.255170.815296.49
2MG-A−2639.73−2664.715287.455337.41
2MG-EA−2600.38−2665.745206.755337.48
2MG-CD−2711.23−2768.115430.465544.22
2MG-EAD−2711.23−2768.115428.465542.22
PG-ADI−2569.15−2637.905158.295295.79
PG-AD−2596.03−2681.625206.065377.24
MX1-AD-ADI−2555.84−2627.905135.685279.79
MX1-AD-AD−2562.51−2657.105143.035332.20
MX1-A-AD−2572.86−2649.335161.725314.66
MX1-EAD-AD−2595.26−2677.935206.525371.87
MX1-NCD-AD−2573.26−2657.185162.515330.35
MX2-ADI-ADI−2546.93−2603.425129.855242.84
MX2-ADI-AD−2548.58−2608.825127.175247.63
MX2-AD-AD−2561.92−2636.235145.855294.46
MX2-A-AD−2554.48−2609.535126.955237.06
MX2-EA-AD−2572.90−2635.765161.805287.52
MX2-CD-AD−2618.23−2689.425254.465396.84
MX2-EAD-AD−2595.26−2677.935206.515371.86
Note: MG: major gene model; MX: mixed major gene and polygene model; PG: polygene model; A: additive effect; D: dominance effect; I: interaction (epistasis); N: negative; E: equal; CD: complete dominance; NCD: negatively complete dominance; EA: equally additive; EAD: equally additive dominance.
Table 4. Suitability test of candidate genetic models for Vc content.
Table 4. Suitability test of candidate genetic models for Vc content.
CrossModelGenerationU12U22U32nW2Dn
Cross AMX2-A-ADP10.10 (0.75)0.20 (0.66)0.28 (0.60)0.10 (0.62)0.10 (0.60)
F10.01 (0.91)0.13 (0.72)1.00 (0.32)0.11 (0.57)0.11 (0.54)
P20.00 (0.96)0.04 (0.85)0.89 (0.34)0.08 (0.71)0.10 (0.64)
BC1P10.26 (0.61)0.06 (0.81)1.00 (0.32)0.14 (0.41)0.08 (0.33)
BC1P20.34 (0.56)0.45 (0.50)0.19 (0.67)0.06 (0.79)0.06 (0.77)
F20.24 (0.63)0.37 (0.54)0.30 (0.58)0.08 (0.71)0.61 (0.61)
MX2-AD-ADIP10.15 (0.70)0.05 (0.83)0.37 (0.54)0.11 (0.54)0.12 (0.42)
F10.40 (0.53)0.12 (0.73)1.18 (0.28)0.15 (0.40)0.15 (0.25)
P20.08 (0.78)0.00 (0.97)0.88 (0.35)0.08 (0.68)0.10 (0.64)
BC1P10.40 (0.84)0.01 (0.93)0.16 (0.69)0.03 (0.98)0.05 (0.92)
BC1P20.24 (0.62)0.28 (0.60)0.04 (0.84)0.05 (0.86)0.05 (0.91)
F20.02 (0.88)0.01 (0.92)0.00 (0.85)0.03 (0.98)0.04 (0.92)
Cross BMX2-A-ADP10.27 (0.60)0.60 (0.44)1.17 (0.28)0.12 (0.49)0.10 (0.66)
F10.13 (0.71)0.37 (0.54)1.05 (0.31)0.09 (0.65)0.11 (0.55)
P20.27 (0.60)0.19 (0.66)0.07 (0.79)0.07 (0.73)0.10 (0.63)
BC1P10.03 (0.86)0.16 (0.69)0.89 (0.34)0.08 (0.68)0.06 (0.85)
BC1P21.39 (0.24)1.27 (0.26)0.00 (0.96)0.15 (0.38)0.07 (0.63)
F20.45 (0.50)0.66 (0.42)0.43 (0.51)0.10 (0.58)0.55 (0.55)
MX2-ADI-ADIP10.01 (0.93)0.04 (0.85)1.23 (0.27)0.11 (0.57)0.11 (0.48)
F10.01 (0.94)0.03 (0.85)1.07 (0.30)0.08 (0.74)0.10 (0.65)
P20.06 (0.81)0.03 (0.86)0.05 (0.83)0.05 (0.88)0.09 (0.80)
BC1P10.15 (0.70)0.04 (0.84)0.46 (0.50)0.05 (0.85)0.05 (0.94)
BC1P20.00 (0.97)0.00 (0.99)0.04 (0.84)0.02 (1.00)0.04 (1.00)
F20.04 (0.85)0.09 (0.77)0.18 (0.67)0.06 (0.82)0.05 (0.63)
Note: U12, U22, U32: statistics of the Uniformity test, with the number in parenthesis is a theoretical probability value; nW2: statistic of Smirnov test; Dn: statistic of Kolmogorov test. The value in parentheses represents the probability for U12, U22, and U32 and significance levels for nW2 and Dn.
Table 5. Estimates of the first-order genetic parameters of Vc content.
Table 5. Estimates of the first-order genetic parameters of Vc content.
First-Order Genetic ParameterEstimate
Cross ACross B
m107.11103.92
da22.4330.72
db−6.34−9.61
[d]19.6515.95
[h]−0.523.20
[h]/[d]−0.030.20
Note: m: mean of graduation; da: additive effect of the first major gene; db: additive effect of the second major gene; [d]: additive effect of polygene; [h]: dominant effect of polygene; [h]/[d]: dominance degree of the polygenes; ‘−’ indicates a negative effect.
Table 6. Estimates of the second-order genetic parameters of crosses A and B.
Table 6. Estimates of the second-order genetic parameters of crosses A and B.
Second-Order Genetic ParameterEstimate
Cross ACross B
BC1P1BC1P2F2BC1P1BC1P2F2
σ2p536.94217.78410.95390.70281.71558.84
σ2e71.9371.9371.9367.0767.0767.07
σ2mg225.76119.60339.02289.72147.33491.77
σ2pg239.2526.250.0033.9173.310.00
h2mg (%)42.0554.9282.5074.1551.2188.00
h2pg (%)44.5612.050.008.6825.480.00
h2mg + pg (%)86.6066.9782.5082.8376.6988.00
1 − h2mg + pg (%)13.4033.0317.5017.1723.3112.00
Note: σ2p: phenotypic variance; σ2e: environmental variance; σ2mg: major gene variance; σ2pg: polygenic variance; h2mg: major gene heritability; h2pg: polygene heritability.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Wang, C.; Wang, T.; Wang, X.; Wang, H.; Dun, X. Genetic Analysis of Vitamin C Content in Rapeseed Seedlings by the Major Gene Plus Polygene Mixed Effect Model. Curr. Issues Mol. Biol. 2024, 46, 9565-9575. https://doi.org/10.3390/cimb46090568

AMA Style

Wang C, Wang T, Wang X, Wang H, Dun X. Genetic Analysis of Vitamin C Content in Rapeseed Seedlings by the Major Gene Plus Polygene Mixed Effect Model. Current Issues in Molecular Biology. 2024; 46(9):9565-9575. https://doi.org/10.3390/cimb46090568

Chicago/Turabian Style

Wang, Chao, Tao Wang, Xinfa Wang, Hanzhong Wang, and Xiaoling Dun. 2024. "Genetic Analysis of Vitamin C Content in Rapeseed Seedlings by the Major Gene Plus Polygene Mixed Effect Model" Current Issues in Molecular Biology 46, no. 9: 9565-9575. https://doi.org/10.3390/cimb46090568

APA Style

Wang, C., Wang, T., Wang, X., Wang, H., & Dun, X. (2024). Genetic Analysis of Vitamin C Content in Rapeseed Seedlings by the Major Gene Plus Polygene Mixed Effect Model. Current Issues in Molecular Biology, 46(9), 9565-9575. https://doi.org/10.3390/cimb46090568

Article Metrics

Back to TopTop