Effect of Genotype-by-Environment Interaction on Oil and Oleic Fatty Acid Contents of Cultivated Peanuts
1
Department of Crop Science and Technology, College of Agriculture, South China Agricultural University, Guangzhou 510642, China
2
Department of Crop, Soil, and Environmental Sciences, Auburn University, Auburn, AL 36849, USA
3
USDA-ARS Plant Genetic Resources Conservation, Griffin, GA 30223, USA
4
USDA-ARS National Peanut Research Laboratory, Dawson, GA 39842, USA
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Horticulturae 2023, 9(12), 1272; https://doi.org/10.3390/horticulturae9121272
Submission received: 5 October 2023
/
Revised: 22 November 2023
/
Accepted: 23 November 2023
/
Published: 27 November 2023
(This article belongs to the Section Genetics, Genomics, Breeding, and Biotechnology (G2B2))
Abstract
:Twenty-seven genotypes of varieties and advanced breeding lines were grown in two locations in three years with three replications to estimate the effects of the genotype-by-environment interaction (G × E) on the oil and oleic fatty acid contents of cultivated peanuts. Oil and oleic fatty acid contents were quantified using NMR and GC, respectively. The tested lines were genotyped with functional SNP markers from the FAD2A and FAD2B genes using real-time PCR and classified into four genotypes. The results indicated that Alabama was the environment that better discriminated the test genotypes during the year 2012. Eight promising selected genotypes #12, #15, ARSOKR, Brantley, GaHO, M04-149, M04-48, and SunO97R showed wide adaptation and high-oleic acids of 83.02%, 81.32%, 82.03%, 81.15%, 79.21%, 80.94%, 82.46%, and 82.18%, respectively. The Additive Main Effects and Multiplicative Interaction (AMMI) model that combines the conventional analyses of variance for additive main effects with the principal component analysis (PCA) for the non-additive residuals was applied to estimate the additive effects from FAD2A and FAD2B genes and the G × E interaction. The results indicated significant G × E interactions for oleic fatty acid contents. No correlation between oil content and FAD2A and FAD2B genes was found. The FAD2B gene had a larger additive effect than the FAD2A gene. The results from this study may be useful not only for peanut breeders, but also for food processors and product consumers to select suitable cultivars.
1. Introduction
Peanut (Arachis hypogaea L.), an autogamous allotetraploid legume (2n = 4x = 40), is an important oilseed crop providing rich nutrients all over the world [1]. It has A and B subgenomes that came from their diploid ancestors Arachis duranensis (A-genome) and Arachis ipaensis (B-genome), respectively. Its seeds generally contain 50% oil, 25% protein, and some useful phytochemicals such as flavonoids, resveratrol, folic acids, and tocopherols [2,3,4]. The peanut oil quality was mainly affected by its fatty acid composition. Peanut oil has eight major fatty acids including oleic acid (C18:1), linoleic acid (C18:2), palmitic acid (C16:0), stearic acid (C18:0), arachidic acid (C20:0), gadoleic acid (C20:1), behenic acid (C22:0), and lignoceric acid (C24:0), among which the amounts of only three fatty acids (C18:1, C18:2, C16:0) exceed 50% [5,6]. These three fatty acids constitute about 90% of peanut oil fat, while only C18:1 and C18:2 account for about 80% of peanut fatty acids. High O/L ratio (ratio of oleic and linoleic acid) is an important parameter for desired oil quality. Peanut with a high oleic acid content can offer a longer shelf life, flavor, and several health benefits such as a lowered risk of heart diseases and slowed atherosclerosis and tumorigenesis development [7,8].
Most of studies were conducted on breed peanut varieties with a high oleic acid content [9,10,11]. For example, one high oleate cultivar, SunOleic 95R, includes 49% oil composed of 80.6% oleic acid and only 2.8% linoleic acid [11]. In peanut, genes ahFAD2A and ahFAD2B, encoding enzyme fatty acid desaturase (FAD2), controls the quantity of oleic acid. The function of this enzyme is to convert oleic acid to linoleic acid in the fatty acid synthesis pathway, while the high-oleic mutants will appear if this enzyme is inactivated [12,13]. In 1987, Norden et al. first identified high-oleic mutants F435-2-1 and F435-2-2, and the content of oleic acid was up to 80%, while the linoleic acid level was only 2% [14]. The mutant ahFAD2A gene, with a G448A substitution, was located on the peanut A genome, and the ahFAD2B gene, with an insertion of one base pair in 442, was located on the peanut B genome [15,16,17]. Subsequently, F435 has been used to develop a series of breeding lines with a high-oleic-acid characteristic [11]. Other high-oleate mutants were produced through mutagenesis [18,19]. In addition, new mutation sites were also identified in ahFAD2B with a substitution of C301G from Arachis hypogaea [17] and in FAD2H with a substitution of C37T from Arachis veigae [20].
Oil and oleic acid contents in peanut seeds are complex quantitative traits that are controlled by more than one gene and are affected by the environment. Genotype, environment, and G × E interaction can affect the performance of the varieties. The environment allows the varieties to exhibit their best advantage. G × E analysis is the final step for the breeders to select the promising breeding materials [21]. Among the statistical techniques used to analyze and interpret G × E data, the additive main effect and multiplicative interaction (AMMI) and genotype-by-environment interaction (GGE) are two most popular analysis methods [22].
Until now, no research on the genotype-by-environment interaction on oil and oleic acid contents in cultivated peanut has been conducted. And before this study, we did not know which gene had a higher additive effect on oleic acid, considering the two genes ahFAD2A and ahFAD2B. The objectives of this research were to (1) determine the variability in oil and oleic acid among germplasm accessions; (2) determine the effects of the genotype x environment interaction on the oil and oleic acid contents; and (3) determine the additive effects of genes ahFAD2A and ahFAD2B.
2. Materials and Methods
2.1. Germplasm Selection and Fresh Seed Collection
A total of 27 accessions (#10, #12, #14, #15, #16, #16-1, ARSOKR, AT3085RO, Brantley, Exp27-1516, F435, F435HO, F435N, FR458, Fla-07, Florunner, Ga02C, GaGreen, GaHO, M04-0149, M04-048, M04-088, NC-7, Olin, SunOleic93R, SunOleic97R, WT4-121; Table 1), representing a wide range of oleic acid content variabilities (from low to high oleic acid content) were selected from the USDA cultivated peanut germplasm collection. In order to determine the effects of genotype (G, i.e., germplasm accessions), environment (E, i.e., locations and years), and G × E interaction, 240 seeds for each accession were requested from the USDA-ARS, Plant Genetic Resources Conservation Unit (PGRCU) in Griffin, GA. A total of 80 seeds of each accession were cultivated for three years at two locations (2010, Georgia; 2011, Alabama; 2012 Alabama). Twenty seeds were planted in two 10-feet-long rows for each replicate. Each accession had three replicates for each location. The planting and harvesting dates were determined according to the local varieties. After harvesting, the pods were dried and shelled by hand. Freshly harvested seeds (around 10% water content) from the fields were collected and used for chemical nutritional quality analysis.
2.2. Genotyping FAD2A and FAD2B through Real-Time PCR
Genotyping was carried out using functional SNP markers from the FAD2A and FAD2B genes through real-time PCR according to the published method. Peanut seed slices (75–150 mg) were put into a 2 mL microcentrifuge tube, along with 600 μL of P1 buffer from the Omega-BioTek kit (Doraville, GA, USA) and two 3 mm tungsten carbide beads (Qiagen, Valencia, CA, USA) for the purpose of extracting DNA. After that, the tissue was ground up for three minutes at 30 Hz using a Retsch Mixer Mill 301 (Leeds, UK). Using a DyNA Quant 200 fluorometer from Hoefer Pharmacia Biotech (San Francisco, CA, USA), extracts were measured. To assess the amount and caliber of each extraction, all samples were also placed onto a 1% agarose gel using a Low DNA Mass Ladder from Invitrogen (Carlsbad, CA, USA). The samples were then diluted to 10 ng/μL in order to perform real-time PCR. 1× TaqMan Genotyping Master Mix (Applied Biosystems), 0.16 μM forward primer, 0.16 μM reverse primer, 0.4 μM VIC probe, 0.3 μM 6FAM probe, and 0.4 ng/μL of DNA made up the 25 μL total volume of the PCR reaction. AmpliTaq Gold polymerase and ROX, a passive internal reference to account for signal variance between wells, are included in the TaqMan Genotyping Master Mix. Applied Biosystems’ ABI StepOne real-time PCR equipment was used to conduct each PCR experiment. The cycling conditions were as follows: one cycle of 60 °C for 30 s, one cycle of 95 °C for 10 min, 50 cycles of 95 °C for 15 s and 62 °C for 1 min, and one final cycle of 60 °C for 30 s.
2.3. Oil and Fatty Acid Composition Measurement
Five medium-sized and healthy, naturally dried peanut seeds were crushed into fine powder in a small plastic bag with a hammer. Approximately 200 mg crushed seed powder was transferred into a small column and then pressed into a small pellet. Oil was quantified through nuclear magnetic resonance (NMR) analysis on a minispec seed analyzer (Bruker Optics Inc., Houston, TX, USA) according to the published report [23]. Fatty acid methyl esters (FAMEs) were prepared from seeds through alkaline transmethylation, and fatty acid composition was determined using an Agilent 7890A gas chromatography (GC) equipped with a flame ionization detector (FID) and an autosampler. Sample preparation, GC operation, and data collection followed the standard methods routinely used by Wang’s lab [23]. Genotyping was carried out using functional SNP markers from the FAD2A and FAD2B genes through real-time PCR.
2.4. Statistical Analysis
A mixed-model analysis of variance (ANOVA) was performed using the GLM procedure of SAS (SAS, 2008, Online Doc® 9.2., SAS Institute Inc., Cary, NC, USA) and means were separated using Duncan’s multiple test procedure. Excel (2016) was used to perform variance analysis of variable components among 27 tested varieties. R4.0.3 [24] was used for AMMI and GGE analysis. Narrow-sense heritability (h2) estimate for FAD2A gene can be estimated as h2a = σ2a/[(σ2e/yab) + (σ2yab/yb) + (σ2ya/b) + (σ2ab/y) + σ2a], where σ2a is variance for gene FAD2A, σ2e is error variance, σ2yab is variance for FAD2A × FAD2B × year, σ2ya is variance for FAD2A × year, σ2ab is variance for FAD2A × FAD2B, y is number of years, and a is degrees of freedom. Narrow-sense heritability (h2) estimate for FAD2B gene can be estimated as h2b = σ2b/[(σ2e/yab) + (σ2yab/yb) + (σ2yb/b) + (σ2ab/y) + σ2b], where σ2b is variance for gene FAD2B, σ2e is error variance, σ2yab is variance for FAD2A × FAD2B × year, σ2yb is variance for FAD2B × year, σ2ab is variance for FAD2A × FAD2B, y is number of years, and b is degrees of freedom. Two-factor analysis of variance for estimates of two-gene model genetic effects was suggested by Cockerham [25] (Table 2).
3. Results
3.1. Analysis of Variance and Variability
A significant variability in oil and fatty acid composition among the 27 tested varieties was detected (Table 3, Dataset S1). Among all of these traits, the variance of C18:1 (162.64) and C18:2 (116.65) was larger than those of the other traits. The maximum (Max) of C18:1 was 83.02%, while the minimum (Min) of C18:1 was only 42.95%. The distribution of fatty acid composition among different genotypes is shown in Figure 1. Similarly, the Max and Min of C18:2 were 35.01% and 1.59%, respectively. C18:2 had the highest difference (22.02-fold difference) between the Max and Min. C20:0 and C18:0 also had large differences (3.03-fold and 2.46-fold) between the Max (2.18% and 4.60%) and Min (0.72% and 1.87%) than the other traits. According to the variance and standard deviation (SD), several of these traits such as C18:1 and C18:2 could be potentially improved through peanut breeding.
3.2. Variability in Traits among Accessions, across Locations and Years
The results of statistical analysis of the variable components and their interactions are listed in Table 4. There were significant differences in the variability among the tested varieties (G) for all of the 10 traits. Year also had significant effects on all of the nine fatty acid contents, but not for oil content. No significant differences between replicates for all investigated traits were detected. According to the F values determined through comparisons among the three individual components (year, replicate, genotype), genotype had a significant effect on all of the traits except for C26:0 and C24:0. Year had a larger effect on C26:0 (F value = 102.25 and 96.11, respectively) and C24:0 (F value = 149.87 and 81.69, respectively) than genotype. Among the two-factor interactions (Y × R, Y × G, and R × G), Y × R interaction effects on the variability in C26:0 and C20:0 were significant. Y × G effects and R × G effects on the variability in six traits were significant, but they were not significant for the variability for four other traits (oil content, C16:0, C18:2, C18:1). Interestingly, the three-factor interaction (Y × R × G) effect on the variability was similar to Y × G and R × G effects, which had significant effects on the variability in six traits, but not for four other traits (oil content, C16:0, C18:2, C18:1).
3.3. Phenotypic Correlations among Traits
The results of variability in 10 investigated traits among the 27 tested varieties are listed in Table 5. Genotype had significant effects on all of the traits. Table 6 shows Duncan test results on the C18:1 content of four genotypes of FAD genes. There were significant effects of both genes FAD2A and FAD2B on the C18:1 content. The genotype aabb had the highest mean oleic fatty acid contents (80.7%) among the four genotypes (AABB, AAbb, aaBB, aabb). Compared to genotype aaBB, genotype AAbb had a larger effect on the mean oleic fatty acid content. Figure 2 indicates that the fatty acid composition changed with different genotypes of FAD2 alleles. The correlations among the investigated traits are listed in Table 7. The oil content was significantly correlated with C18:0 (r = −0.017) and C24:0 (r = 0.198). The C16:0 content was significantly correlated with all of the tested fatty acid compositions; however, it was negatively correlated with C18:1 (r = −0.969), C20:1 (r = −0.753), C24:0 (r = −0.415), and C26:0 (r = −0.555). The C18:1 content was negatively correlated with C16:0 (r = −0.969), C18:0 (r = −0.408), C18:2 (r = −0.996), C20:0 (r = −0.446), and C22:0 (r = −0.461) and positively correlated with C20:1 (r = 0.737), C24:0 (r = 0.371), and C26:0 (r = 0.511).
3.4. Additive Effect and Narrow-Sense Heritability of Gene FAD2
The additive effects and the G × E interaction involving two genes (FAD2A and FAD2B) and three environments (2010, 2011, and 2012) for 27 varieties were calculated (Table 8). The results indicated that the FAD2B gene had a larger additive effect (63%) than the FAD2A gene (28%). The narrow-sense heritability (h2) estimate for the FAD2A gene was 0.95 (h2a = σ2a/[(σ2e/yab) + (σ2yab/yb) + (σ2ya/b) + (σ2ab/y) + σ2a]; h2a = 0.95) and the narrow-sense heritability (h2) estimate for the FAD2B gene was 0.97 (h2b = σ2b/[(σ2e/yab) + (σ2yab/yb) + (σ2yb/b) + (σ2ab/y) + σ2b]; h2b = 0.97). The additive effect of gene FAD2B was larger than that of gene FAD2A, which means that peanut breeders can improve high-oleic varieties by editing gene FAD2B. No correlation between oil content and FAD2A and FAD2B genes was found.
3.5. AMMI1 Biplot Display
The AMMI analysis was utilized to quantify the impact of the environment on the genotypes. Biplot analysis is a valuable tool for interpreting AMMI models. Two types of AMMI biplots were generated: the AMMI1 biplot, which displayed the interaction between the genotype mean and environments; and the AMMI2 biplot (GGE biplot), which showed scores for IPCA1 and IPCA2 [26]. From Figure 3, it is evident that Env1 had the greatest main effects and was favorable for the performance of most genotypes. Conversely, Env3 exhibited lower main-effect values, indicating little interaction with genotypes. Env2 had a positive PC score with a high mean value. Genotypes #12, M04-48, and SunO97R were identified as well adapted to both Env2 and Env3, suggesting these two environments as suitable for these three genotypes. Genotypes F435, Florunner, Exp27-1516, Ga02C, ARSOKR, SunO97R, #12, M04-48, and GaHO displayed PC1 scores close to zero, while other genotypes demonstrated a below-average oleic acid content with negative PC scores or an above-average oleic acid content with positive PC scores. Genotypes #16-1 and F435 had a lower C18:1 content, whereas genotypes #12, M04-48, and SunO97R had a higher C18:1 content. Moreover, Env1 had a large negative PC1 score, which positively interacted with genotypes with negative PC1 scores like Olin, and negatively interacted with genotypes with positive PC1 scores. Finally, the AMMI1 biplot statistical model was employed to identify G × E interactions in peanut. Genotypes #10, #12, ARSOKR, AT3085RO, Brantley, Ga02C, GaHO, M04-48, and SunO97R were suitable for planting in Env2 and Env3, while genotypes #15, #16, F435HO, Fla-07, FR458, M04-149, M04-88, SunO93R, and WT4-121 were considered favorable environments for Env1.
3.6. AMMI2 Biplot Display
To assess the environment cluster differentiation, genotype-specific adaptation, and G × E interaction, a biplot illustrating the performance of 27 genotypes in three environments was generated (Figure 4). In Figure 4, the environments were categorized into three sections. Among them, Env3 exhibited short spokes and demonstrated weak interactive forces, whereas Env1 and Env2 displayed long spokes, indicating their discriminatory nature. In the AMMI 2 biplot, the genotype GaGreen was more responsive since it was more distant from the origin and suitable for Env2 and Env3. With the exception of #10, #14, #16-1, AT3085RO, F435N, GaGreen, and Olin, most genotypes were located near the origin, implying their lower sensitivity to environmental interactive forces. Overall, based on the findings from AMMI1 and AMMI2, genotypes #12, #15, ARSOKR, Brantley, GaHO, M04-149, M04-48, and SunO97R were identified as the best performers in terms of high oleic contents, making them favorable for Env3.
4. Discussion
A correlation between oleic acid content and FAD2A and FAD2B genes has clearly been demonstrated by many researches; however, the relative contribution of FAD2 genes (FAD2A and FAD2B) to oleic acid quality traits in peanut is still unknown [27,28,29,30]. The mutant allele FAD2A is widely available in the U.S. peanut germplasm collection but the mutant allele FAD2B is only present in the selected genotypes such as SunOleic 95R and SunOleic 97R [31,32]. There are no studies on estimating the contribution of these two mutant alleles to oleic acid and we do not know which of the mutant alleles could produce more oleic acid [32]. Researchers have just examined how the interactions of additive alleles determine the content of oleic acid; however, each allele can have an additive effect as well. The additive effects of two alleles were calculated for improving the further understanding of the genetic control for oleic acid synthesis in peanut.
The AMMI analysis is a most popular method to study G×E interactions and it was used to quantify the effect of G, E, and G × E interactions on drought-related traits in peanut [33]. In addition, the effectiveness of the AMMI procedure has been clearly demonstrated in other crops, such as wheat [34], soybean [35], maize [36], pear millet [37], and field pea [38]. However, it still has its own limits; for example, it does not provide a measure for quantitative stability [39]. In this study, 27 peanut genotypes were evaluated in a three-year (2010–2012) field experiment, which was conducted at two locations. The combination of the AMMI model and biplot made it possible to describe the genotype-by-environment interactions effect more accurately.
Compared to regular peanuts, the high-oleic peanuts are more naturally resistant to oxidation because it is higher in monounsaturated fats. The peanut single kernel oleic acid distributions were influenced by seed size, seed maturity, growing environment, and season flower termination [40]. The main chemical technique is gas chromatography (GC), which calls for 100 extractions or injections of a standard sample and an experienced operator. Although more affordable and quicker than the GC technique, refractive index approaches still need a significant amount of time to complete [41]. However, they exhibit a good correlation with the principal GC method. Numerous breeding efforts are using near-infrared (NIR) techniques to measure this fatty acid chemistry. It is also critical to understand how costly and time-consuming it is to measure this chemistry. Therefore, it is better to select varieties by genotype with higher additive effects and it is easy to obtain the genetic gain. For companies, quality is important, so they need to consider where to grow these high-oleic peanuts. The results from this study could help breeders and companies to obtain peanuts with stable high oleic contents.
5. Conclusions
In the present study, high-oleic and stable genotypes, such as genotypes #12, #15, ARSOKR, Brantley, GaHO, M04-149, M04-48, and SunO97R, could be used as new potential genetic resources for improving the peanut varieties with contents of oleic. In addition, the results also indicated that the FAD2B gene had a larger additive effect than the FAD2A gene, which provides important values for breeding high-oleic peanut varieties by editing gene FAD2B.
Supplementary Materials
The following are available online at https://www.mdpi.com/article/10.3390/horticulturae9121272/s1, Dataset S1: The phenotype and genotype data of varieties used in this study.
Author Contributions
H.Z., Y.Y., and C.C. designed the study and performed the data analysis; M.W. and P.D. contributed to sample preparation and chemical analysis; H.Z., Y.Y., and C.C. wrote the draft manuscript. All authors contributed to the writing of the manuscript. All authors have read and agreed to the published version of the manuscript.
Funding
This project was supported by “Double first-class discipline promotion project” of South China Agricultural University (grant number 2021B10564001), the Guangzhou science and technology planning project (grant number 2023A04J1449) and The Peanut Foundation (grant number 04-833-17).
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
The data presented in this study are available in the supplementary material.
Acknowledgments
We are indebted to Sam Hilton, Brian Gamble, Larry Wells, and Jason Burkett for their devoted assistance with the management of field experimental plots at the Wiregrass Research and Extension Center and the E.V. Smith Research Center. Thanks also to Joseph Powell, and other personnel from the USDA-ARS National Peanut Research Lab at Dawson, GA, for assistance with the experimental aspects associated with this research.
Conflicts of Interest
Authors declare that there are no conflict of interest.
References
- Wang, M.L.; Chen, C.Y.; Tonnis, B.; Barkley, N.A.; Pinnow, D.L.; Pittman, R.N.; Davis, J.; Holbrook, C.C.; Stalker, H.T.; Pederson, G.A. Oil, Fatty Acid, Flavonoid, and Resveratrol Content Variability and FAD2A Functional SNP Genotypes in the U.S. Peanut Mini-Core Collection. J. Agric. Food Chem. 2013, 61, 2875–2882. [Google Scholar] [CrossRef] [PubMed]
- Shin, E.-C.; Huang, Y.-Z.; Pegg, R.B.; Phillips, R.D.; Eitenmiller, R.R. Commercial Runner Peanut Cultivars in the United States: Tocopherol Composition. J. Agric. Food Chem. 2009, 57, 10289–10295. [Google Scholar] [CrossRef] [PubMed]
- Dean, L.L.; Hendrix, K.W.; Holbrook, C.C.; Sanders, T.H. Content of Some Nutrients in the Core of the Core of the Peanut Germplasm Collection. Peanut Sci. 2009, 36, 104–120. [Google Scholar] [CrossRef]
- Wang, M.L.; Gillaspie, A.G.; Morris, J.B.; Pittman, R.N.; Davis, J.; Pederson, G.A. Flavonoid Content in Different Legume Germplasm Seeds Quantified by HPLC. Plant Genet. Resour. 2008, 6, 62–69. [Google Scholar] [CrossRef]
- Grosso, N.R.; Guzman, C.A. Chemical Composition of Aboriginal Peanut (Arachis hypogaea L.) Seeds from Peru. J. Agric. Food Chem. 1995, 43, 102–105. [Google Scholar] [CrossRef]
- Jadhav, M.P.; Patil, M.D.; Hampannavar, M.; Venkatesh; Dattatreya, P.; Shirasawa, K.; Pasupuleti, J.; Pandey, M.K.; Varshney, R.K.; Bhat, R.S. Enhancing Oleic Acid Content in Two Commercially Released Peanut Varieties through Marker-Assisted Backcross Breeding. Crop Sci. 2021, 61, 2435–2443. [Google Scholar] [CrossRef]
- Bera, S.K.; Kamdar, J.H.; Kasundra, S.V.; Dash, P.; Maurya, A.K.; Jasani, M.D.; Chandrashekar, A.B.; Manivannan, N.; Vasanthi, R.P.; Dobariya, K.L.; et al. Improving Oil Quality by Altering Levels of Fatty Acids through Marker-Assisted Selection of Ahfad2 Alleles in Peanut (Arachis hypogaea L.). Euphytica 2018, 214, 162. [Google Scholar] [CrossRef]
- O’Byrne, D.J.; Knauft, D.A.; Shireman, R.B. Low Fat-Monounsaturated Rich Diets Containing High-Oleic Peanuts Improve Serum Lipoprotein Profiles. Lipids 1997, 32, 687–695. [Google Scholar] [CrossRef]
- Enhancing Oleic Acid Content in Two Commercially Released Peanut Varieties through Marker-Assisted Backcross Breeding—Jadhav—2021—Crop Science—Wiley Online Library. Available online: https://acsess.onlinelibrary.wiley.com/doi/abs/10.1002/csc2.20512 (accessed on 24 September 2021).
- Kamdar, J.H.; Jasani, M.D.; Georrge, J.J. Development of Peanut Breeding Lines with High Oleic Acid Content; Social Science Research Network: Rochester, NY, USA, 2020. [Google Scholar]
- Gorbet, D.W.; Knauft, D.A. Registration of ‘SunOleic 97R’ Peanut. Crop Sci. 2000, 40, 1190. [Google Scholar] [CrossRef]
- Moore, K.M.; Knauft, D.A. The Inheritance of High Oleic Acid in Peanut. J. Hered. 1989, 80, 252–253. [Google Scholar] [CrossRef]
- Identification of Potential QTLs and Genes Associated with Seed Composition Traits in Peanut (Arachis hypogaea L.) Using GWAS and RNA-Seq Analysis—ScienceDirect. Available online: https://www.sciencedirect.com/science/article/abs/pii/S0378111920308842 (accessed on 24 September 2021).
- Norden, A.J.; Gorbet, D.W.; Knauft, D.A.; Young, C.T. Variability in Oil Quality Among Peanut Genotypes in the Florida Breeding Program1. Peanut Sci. 1987, 14, 7–11. [Google Scholar] [CrossRef]
- Comparison of the Δ12 Fatty Acid Desaturase Gene between High-Oleic and Normal-Oleic Peanut Genotypes—ScienceDirect. Available online: https://www.sciencedirect.com/science/article/abs/pii/S1673852708600909 (accessed on 24 September 2021).
- López, Y.; Nadaf, H.L.; Smith, O.D.; Connell, J.P.; Reddy, A.S.; Fritz, A.K. Isolation and Characterization of the Δ12-Fatty Acid Desaturase in Peanut (Arachis hypogaea L.) and Search for Polymorphisms for the High Oleate Trait in Spanish Market-Type Lines. Theor. Appl. Genet. 2000, 101, 1131–1138. [Google Scholar] [CrossRef]
- Wang, M.L.; Tonnis, B.; An, Y.-Q.C.; Pinnow, D.; Tishchenko, V.; Pederson, G.A. Newly Identified Natural High-Oleate Mutant from Arachis hypogaea L. Subsp. Hypogaea. Mol. Breed. 2015, 35, 186. [Google Scholar] [CrossRef]
- Wang, C.T.; Tang, Y.Y.; Wang, X.Z.; Zhang, S.W.; Li, G.J.; Zhang, J.C.; Yu, S.L. Sodium Azide Mutagenesis Resulted in a Peanut Plant with Elevated Oleate Content. Electron. J. Biotechnol. 2011, 14, 10. [Google Scholar] [CrossRef]
- High-Oleate Peanut Mutants Result from a MITE Insertion into the FAD2 Gene|SpringerLink. Available online: https://link.springer.com/article/10.1007%2Fs00122-004-1590-3 (accessed on 24 September 2021).
- Tonnis, B.; Wang, M.L.; Tallury, S.; Tishchenko, V.; Stalker, H.T. Identification of a Mutant from Arachis Veigae with Enhanced Seed Oleic and Very Long-Chain Fatty Acid Content. Appl. Biol. Chem. 2019, 62, 9. [Google Scholar] [CrossRef]
- Horn, L.; Shimelis, H.; Sarsu, F.; Mwadzingeni, L.; Laing, M.D. Genotype-by-Environment Interaction for Grain Yield among Novel Cowpea (Vigna unguiculata L.) Selections Derived by Gamma Irradiation. Crop J. 2018, 6, 306–313. [Google Scholar] [CrossRef]
- Yan, W.; Kang, M.S.; Ma, B.; Woods, S.; Cornelius, P.L. GGE Biplot vs. AMMI Analysis of Genotype-by-Environment Data. Crop Sci. 2007, 47, 643–653. [Google Scholar] [CrossRef]
- Wang, M.L.; Chen, C.Y.; Tonnis, B.; Pinnow, D.; Davis, J.; An, Y.-Q.C.; Dang, P. Changes of Seed Weight, Fatty Acid Composition, and Oil and Protein Contents from Different Peanut FAD2 Genotypes at Different Seed Developmental and Maturation Stages. J. Agric. Food Chem. 2018, 66, 3658–3665. [Google Scholar] [CrossRef]
- Bunn, A.; Korpela, M. An Introduction to dplR. Ind. Commer. Train. 2008, 10, 11–18. [Google Scholar]
- Cockerham, C.C. Estimation of Genetic Variance. In Statistical Genetics and Plant Breeding; Hansan, W.D., Robinson, H.F., Eds.; National Academy of Science-National Research Council: Washington, DC, USA, 1963; Volume 53. [Google Scholar]
- Akter, A.; Hasan, M.J.; Kulsum, M.U.; Rahman, M.H.; Paul, A.K.; Lipi, L.F.; Akter, S. Genotype × Environment Interaction and Yield Stability Analysis in Hybrid Rice (Oryza sativa L.) By AMMI Biplot. Bangladesh Rice J. 2015, 19, 83–90. [Google Scholar] [CrossRef]
- Wang, M.L.; Chen, C.Y.; Davis, J.; Guo, B.; Stalker, H.T.; Pittman, R.N. Assessment of Oil Content and Fatty Acid Composition Variability in Different Peanut Subspecies and Botanical Varieties. Plant Genet. Resour. 2010, 8, 71–73. [Google Scholar] [CrossRef]
- Wilson, J.N.; Baring, M.R.; Burow, M.D.; Rooney, W.L.; Simpson, C.E. Diallel Anaysis of Oil Production Components in Peanut (Arachis hypogaea L.). Int. J. Agron. 2013, 2013, 975701. [Google Scholar] [CrossRef]
- Barkley, N.A.; Isleib, T.G.; Wang, M.L.; Pittman, R.N. Genotypic Effect of ahFAD2 on Fatty Acid Profiles in Six Segregating Peanut (Arachis hypogaea L) Populations. BMC Genet. 2013, 14, 62. [Google Scholar] [CrossRef] [PubMed]
- Wang, M.L.; Barkley, N.A.; Chen, Z.; Pittman, R.N. FAD2 Gene Mutations Significantly Alter Fatty Acid Profiles in Cultivated Peanuts (Arachis hypogaea). Biochem. Genet. 2011, 49, 748–759. [Google Scholar] [CrossRef]
- Chu, Y.; Holbrook, C.C.; Ozias-Akins, P. Two Alleles of ahFAD2B Control the High Oleic Acid Trait in Cultivated Peanut. Crop Sci. 2009, 49, 2029–2036. [Google Scholar] [CrossRef]
- Pandey, M.K.; Wang, M.L.; Qiao, L.; Feng, S.; Khera, P.; Wang, H.; Tonnis, B.; Barkley, N.A.; Wang, J.; Holbrook, C.C.; et al. Identification of QTLs Associated with Oil Content and Mapping FAD2 Genes and Their Relative Contribution to Oil Quality in Peanut (Arachis hypogaea L.). BMC Genet. 2014, 15, 133. [Google Scholar] [CrossRef]
- Chen, C.Y.; Nuti, R.C.; Rowland, D.L.; Faircloth, W.H.; Lamb, M.C.; Harvey, E. Heritability and Genetic Relationships for Drought-Related Traits in Peanut. Crop Sci. 2013, 53, 1392–1402. [Google Scholar] [CrossRef]
- Tarakanovas, P.; Ruzgas, V. Additive Main Effect and Multiplicative Interaction Analysis of Grain Yield of Wheat Varieties in Lithuania. Agron. Res. 2006, 4, 91–98. [Google Scholar]
- Zobel, R.W.; Wright, M.J.; Gauch, H.G., Jr. Statistical Analysis of a Yield Trial. Agron. J. 1988, 80, 388–393. [Google Scholar] [CrossRef]
- Crossa, J.; Gauch, H.G., Jr.; Zobel, R.W. Additive Main Effects and Multiplicative Interaction Analysis of Two International Maize Cultivar Trials. Crop Sci. 1990, 30, 493–500. [Google Scholar] [CrossRef]
- Shinde, G.C.; Bhingarde, M.T.; Khairnar, M.N.; Mehetre, S.S. AMMI Analysis for Stability of Grain Yield of Pearl Millet (Pennisetum typhoides L.) Hybrids. Indian J. Genet. Plant Breed. 2002, 62, 215–217. [Google Scholar]
- Taye, G.; Getachew, T.; Bejiga, G. AMMI Adjustment for Yield Estimate and Classification of Genotypes and Environments in Field Pea (Pisum sativum L.). J. Genet. Breed. 2000, 54, 183–191. [Google Scholar]
- Abate, M. Genotype by Environment Interaction and Yield Stability Analysis of Open Pollinated Maize Varieties Using AMMI Model in Afar Regional State, Ethiopia. JPBCS 2020, 12, 8–15. [Google Scholar] [CrossRef]
- Davis, J.P.; Leek, J.M.; Sweigart, D.S.; Dang, P.; Butts, C.L.; Sorensen, R.B.; Chen, C.Y.; Lamb, M.C. Measurements of Oleic Acid among Individual Kernels Harvested from Test Plots of Purified Runner and Spanish High Oleic Seed. Peanut Sci. 2017, 44, 134–142. [Google Scholar] [CrossRef]
- Davis, J.P.; Sweigart, D.S.; Price, K.M.; Dean, L.L.; Sanders, T.H. Refractive Index and Density Measurements of Peanut Oil for Determining Oleic and Linoleic Acid Contents. J. Am. Oil Chem. Soc. 2012, 90, 199–206. [Google Scholar] [CrossRef]
Figure 1.
The distribution of fatty acid composition among different genotypes. The unit of the Y-axis is “%”.
Figure 1.
The distribution of fatty acid composition among different genotypes. The unit of the Y-axis is “%”.
Figure 2.
The changes in fatty acid composition with different genotypes of FAD2 alleles. The unit of the Y-axis is “%”.
Figure 2.
The changes in fatty acid composition with different genotypes of FAD2 alleles. The unit of the Y-axis is “%”.
Figure 3.
AMMI1 biplot for oleic acid of 27 genotypes tested in three environments. The three environments are Env1, GA in 2010; Env2, AL in 2011; and Env3, AL in 2012. A total of 27 genotypes are represented as #10, #12, #14, #15, #16, #16-1, ARSOKR, AT3085RO, Brantley, Exp27-1516, F435, F435HO, F435N, FR458, Fla-07, Florunner, Ga02C, GaGreen, GaHO, M04-0149, M04-048, M04-088, NC-7, Olin, SunOleic93R, SunOleic97R, WT4-121 in order. PC, principal component.
Figure 3.
AMMI1 biplot for oleic acid of 27 genotypes tested in three environments. The three environments are Env1, GA in 2010; Env2, AL in 2011; and Env3, AL in 2012. A total of 27 genotypes are represented as #10, #12, #14, #15, #16, #16-1, ARSOKR, AT3085RO, Brantley, Exp27-1516, F435, F435HO, F435N, FR458, Fla-07, Florunner, Ga02C, GaGreen, GaHO, M04-0149, M04-048, M04-088, NC-7, Olin, SunOleic93R, SunOleic97R, WT4-121 in order. PC, principal component.
Figure 4.
AMMI2 GGE biplot representing a vector view of the three environments used to test the mean oleic acid of 27 peanut genotypes. The three environments are Env1, GA in 2010; Env2, AL in 2011; and Env3, AL in 2012. A total of 27 genotypes are represented as #10, #12, #14, #15, #16, #16-1, ARSOKR, AT3085RO, Brantley, Exp27-1516, F435, F435HO, F435N, FR458, Fla-07, Florunner, Ga02C, GaGreen, GaHO, M04-0149, M04-048, M04-088, NC-7, Olin, SunOleic93R, SunOleic97R, WT4-121 in order. PC, principal component.
Figure 4.
AMMI2 GGE biplot representing a vector view of the three environments used to test the mean oleic acid of 27 peanut genotypes. The three environments are Env1, GA in 2010; Env2, AL in 2011; and Env3, AL in 2012. A total of 27 genotypes are represented as #10, #12, #14, #15, #16, #16-1, ARSOKR, AT3085RO, Brantley, Exp27-1516, F435, F435HO, F435N, FR458, Fla-07, Florunner, Ga02C, GaGreen, GaHO, M04-0149, M04-048, M04-088, NC-7, Olin, SunOleic93R, SunOleic97R, WT4-121 in order. PC, principal component.
Table 1.
The genotypes of 27 accessions used in this study.
| Variety | FAD2A | FAD2B | Genotype |
|---|---|---|---|
| #10 | aa | bb | aabb |
| #12 | aa | bb | aabb |
| #14 | aa | bb | aabb |
| #15 | aa | bb | aabb |
| #16 | aa | bb | aabb |
| #16-1 | AA | BB | AABB |
| ARSOKR | aa | bb | aabb |
| AT3085RO | aa | bb | aabb |
| Brantley | aa | bb | aabb |
| Exp27-1516 | aa | BB | aaBB |
| F435 | AA | BB | AABB |
| F435HO | aa | bb | aabb |
| F435N | aa | BB | aaBB |
| Fla-07 | aa | bb | aabb |
| Florunner | aa | BB | aaBB |
| FR458 | aa | bb | aabb |
| Ga02C | aa | bb | aabb |
| GaGreen | aa | BB | aaBB |
| GaHO | aa | bb | aabb |
| M04-149 | aa | bb | aabb |
| M04-48 | aa | bb | aabb |
| M04-88 | aa | bb | aabb |
| NC-7 | aa | BB | aaBB |
| Olin | aa | bb | aabb |
| SunO93R | aa | bb | aabb |
| SunO97R | aa | bb | aabb |
| WT4-121 | aa | bb | aabb |
The letters mean different genotypes.
Table 2.
Two-factor analysis of variance for estimates of two-gene model genetic effects suggested by Cockerham (1963).
Table 2.
Two-factor analysis of variance for estimates of two-gene model genetic effects suggested by Cockerham (1963).
| Source | Degrees of Freedom | Expected Mean Squares |
|---|---|---|
| Year (Y) | (Y − 1) = 2 | |
| Rep (year) | (R − 1) × Y = 6 | |
| Fad2A (A) | (A − 1) = 1 | σ2e + 3σ2yab + 6σ2ya+ 9σ2ab + 18σ2a |
| Fad2B (B) | (B − 1) = 1 | σ2e + 3σ2yab+ 6σ2yb+ 9σ2ab + 18σ2b |
| A × B | (A − 1)(B − 1) = 1 | σ2e + 3σ2yab + 9σ2ab |
| Y × A | (Y − 1)(A − 1) = 2 | σ2e + 3σ2yab + 6σ2ya |
| Y × B | (Y − 1)(B − 1) = 2 | σ2e + 3σ2yab + 6σ2yb |
| Y × A × B | (Y − 1)(A − 1)(B − 1) = 2 | σ2e + 3σ2yab |
| Error | Total − Model = 271 | σ2e |
Table 3.
Variability in oil percentage on dry weight and fatty acid composition on oil content among selected peanut accessions.
Table 3.
Variability in oil percentage on dry weight and fatty acid composition on oil content among selected peanut accessions.
| Trait | Mean | Variance | Minimum | Maximum | Max/Mini (fold) | SD |
|---|---|---|---|---|---|---|
| Oil | 52.32 (%) | 1.89 | 49.43 | 56.36 | 1.14 | 1.38 |
| C16:0 | 7.00 (%) | 3.28 | 5.44 | 11.75 | 2.16 | 1.81 |
| C18:0 | 2.58 (%) | 0.70 | 1.87 | 4.60 | 2.46 | 0.84 |
| C18:1 | 73.82 (%) | 162.64 | 42.95 | 83.02 | 1.93 | 12.75 |
| C18:2 | 8.94 (%) | 116.65 | 1.59 | 35.01 | 22.02 | 10.80 |
| C20:0 | 1.35 (%) | 0.09 | 0.72 | 2.18 | 3.03 | 0.30 |
| C20:1 | 1.65 (%) | 0.18 | 1.07 | 1.98 | 1.85 | 0.43 |
| C22:0 | 2.72 (%) | 0.21 | 2.04 | 3.62 | 1.77 | 0.46 |
| C24:0 | 1.65 (%) | 0.06 | 1.22 | 1.99 | 1.63 | 0.24 |
| C26:0 | 0.30 (%) | 0.01 | 0.19 | 0.40 | 2.11 | 0.08 |
SD, standard deviation.
Table 4.
Statistical analysis of variable components among 27 tested varieties.
| Source | DF | F Value | Pr > F | DF | F Value | Pr > F |
|---|---|---|---|---|---|---|
| C16:0 | Oil | |||||
| year (y) | 2 | 12.453 | <0.0001 | 1 | 0.243 | 0.6303 |
| rep (r) | 2 | 0.619 | 0.5411 | 2 | 0.838 | 0.4545 |
| PI (P) | 26 | 125.822 | 0.0000 | 25 | 2.900 | 0.0244 |
| y ×r | 4 | 0.513 | 0.7264 | 2 | 1.997 | 0.1752 |
| y × P | 47 | 1.052 | 0.4148 | 22 | 0.889 | 0.6097 |
| r × P | 51 | 1.472 | 0.0612 | 46 | 0.386 | 0.9912 |
| y × r × P | 77 | 1.275 | 0.1437 | 40 | 0.421 | 0.9820 |
| C26:0 | C24:0 | |||||
| year (y) | 2 | 102.246 | <0.0001 | 2 | 149.870 | <0.0001 |
| rep (r) | 2 | 0.032 | 0.9686 | 2 | 0.394 | 0.6755 |
| PI (P) | 26 | 96.107 | <0.0001 | 26 | 81.691 | <0.0001 |
| y × r | 4 | 3.868 | 0.0064 | 4 | 1.202 | 0.3167 |
| y × P | 47 | 5.295 | <0.0001 | 47 | 8.569 | <0.0001 |
| r × P | 51 | 1.791 | 0.0100 | 51 | 4.560 | <0.0001 |
| y × r × P | 77 | 2.652 | <0.0001 | 77 | 3.463 | <0.0001 |
| C22:0 | C20:1 | |||||
| year (y) | 2 | 18.254 | <0.0001 | 2 | 31.870 | <0.0001 |
| rep (r) | 2 | 1.224 | 0.2997 | 2 | 0.684 | 0.5074 |
| PI (P) | 26 | 79.716 | <0.0001 | 26 | 164.626 | <0.0001 |
| y × r | 4 | 2.147 | 0.0828 | 4 | 3.138 | 0.0190 |
| y × P | 47 | 7.380 | <0.0001 | 47 | 5.432 | <0.0001 |
| r × P | 51 | 5.798 | <0.0001 | 51 | 3.282 | <0.0001 |
| y × r × P | 77 | 2.882 | <0.0001 | 77 | 3.930 | <0.0001 |
| C20:0 | C18:2 | |||||
| year (y) | 2 | 15.546 | <0.0001 | 2 | 8.528 | 0.0004 |
| rep (r) | 2 | 0.771 | 0.4659 | 2 | 0.747 | 0.4773 |
| PI(P) | 26 | 100.533 | <0.0001 | 26 | 116.125 | <0.0001 |
| y × r | 4 | 1.347 | 0.2602 | 4 | 0.663 | 0.6199 |
| y × P | 47 | 3.003 | <0.0001 | 47 | 0.794 | 0.8025 |
| r × P | 51 | 2.432 | 0.0002 | 51 | 0.811 | 0.7863 |
| y × r × P | 77 | 1.976 | 0.0015 | 77 | 0.848 | 0.7657 |
| C18:1 | C18:0 | |||||
| year (y) | 2 | 10.794 | 0.0001 | 2 | 39.398 | <0.0001 |
| rep (r) | 2 | 0.706 | 0.4969 | 2 | 1.082 | 0.3440 |
| PI (P) | 26 | 113.710 | <0.0001 | 26 | 64.923 | <0.0001 |
| y × r | 4 | 0.581 | 0.6775 | 4 | 1.501 | 0.2099 |
| y × P | 47 | 0.815 | 0.7732 | 47 | 1.572 | 0.0382 |
| r × P | 51 | 0.831 | 0.7588 | 51 | 2.575 | 0.0001 |
| y × r × P | 77 | 0.806 | 0.8280 | 77 | 1.519 | 0.0337 |
Table 5.
Comparison of ten investigated traits among 27 tested varieties: oil content is expressed as % of dry weight, and fatty acids as % of oil content.
Table 5.
Comparison of ten investigated traits among 27 tested varieties: oil content is expressed as % of dry weight, and fatty acids as % of oil content.
| Variety | Oil (%) | C16:0 (%) | C18:0 (%) | C18:1 (%) | C18:2 (%) | C20:1 (%) | C20:0 (%) | C22:0 (%) | C24:0 (%) | C26:0 (%) |
|---|---|---|---|---|---|---|---|---|---|---|
| #10 | 52.05 bcd | 6.05 ghi | 2.1 fg | 81.73 ab | 3.17 g | 1.76 fg | 1.1 ij | 2.08 m | 1.65 d–f | 0.37 abc |
| #12 | 52.26 a–d | 5.92 ghijk | 2.02 fg | 83.02 a | 2.13 g | 1.79 efg | 1.07 j | 2.04 m | 1.63 d–f | 0.38 ab |
| #14 | 52.88 ab | 6.18 gh | 2.3 efg | 79.6 ab | 4.78 g | 1.74 g | 1.16 f–j | 2.19 lm | 1.68 c–f | 0.39 ab |
| #15 | 52.81 ab | 6.26 g | 2.35 ef | 81.32 ab | 2.36 g | 1.79 efg | 1.27 ef | 2.66 f–j | 1.71 cde | 0.28 gh |
| #16 | 52.67 ab | 6.33 g | 2.53 de | 78.92 b | 4.54 g | 1.75 fg | 1.31 de | 2.62 g–k | 1.71 cde | 0.31 fgh |
| #16-1 | 50.26 de | 11.4 a | 3.84 b | 42.95 h | 35.01 a | 0.72 l | 1.79 b | 2.83 e–h | 1.29 ij | 0.19 k |
| ARSOKR | 52.62 abc | 5.93 ghijk | 2.25 efg | 82.03 ab | 2.26 g | 1.93 cdef | 1.18 efghij | 2.35 kl | 1.74 cde | 0.33 d–f |
| AT3085RO | 49.43 e | 6.02 ghij | 2.35 ef | 80.35 ab | 3.64 g | 1.8 d–g | 1.24 efgh | 2.69 e–j | 1.65 d–f | 0.27 hi |
| Brantley | 51.17 cd | 5.45 jk | 4.6 a | 81.15 ab | 1.59 g | 1.21 ij | 1.98 a | 2.62 g–k | 1.22 j | 0.19 k |
| Exp27-1516 | 53.36 ab | 9.55 c | 2.77 d | 54.29 f | 26.3 d | 1.13 ij | 1.4 cd | 2.9 def | 1.44 gh | 0.22 jk |
| F435 | 11.75 a | 3.48 bc | 43.6 h | 33.32 ab | 0.91 k | 1.69 b | 3.62 a | 1.43 ghi | 0.2 jk | |
| F435HO | 53.76 a | 7.05 f | 2.56 de | 79.48 ab | 2.4 g | 1.75 fg | 1.46 c | 3.36 bc | 1.73 cde | 0.24 ij |
| F435N | 56.36 a | 9.5 c | 3.19 c | 57.79 e | 21.29 e | 1.2 ij | 1.71 b | 3.54 ab | 1.55 fg | 0.23 jk |
| Fla-07 | 52.48 a–d | 6.33 g | 2.29 efg | 80.37 ab | 3.08 g | 1.86 c–g | 1.26 efg | 2.84 e–h | 1.71 cde | 0.27 hi |
| Florunner | 53.04 ab | 10.32 b | 2.29 efg | 50.15 g | 30.08 c | 1.23 i | 1.28 ef | 2.75 e–j | 1.62 ef | 0.28 h |
| FR458 | 52.45 a–d | 6.06 ghi | 1.91 g | 80.36 ab | 3.98 g | 1.95 cde | 1.11 h–j | 2.55 h–k | 1.75 cde | 0.35 b–e |
| Ga02C | 52.62 abc | 6.03 ghi | 2.28 efg | 80.13 ab | 2.96 g | 2.13 ab | 1.25 efg | 2.87 efg | 1.99 a | 0.38 ab |
| GaGreen | 53.15 ab | 10.08 b | 2.09 fg | 48.51 g | 31.38 bc | 1.46 h | 1.22 efghi | 3.13 cd | 1.84 bc | 0.3 fgh |
| GaHO | 52.29 a–d | 6 ghijk | 3.17 c | 79.21 ab | 2.86 g | 1.74 g | 1.7 b | 3.45 ab | 1.65 d–f | 0.22 jk |
| M04-149 | 51.87 bcd | 5.9 ghijk | 1.91 g | 80.94 ab | 3.55 g | 1.99 bcd | 1.11 h–j | 2.48 jk | 1.77 b–e | 0.37 a–d |
| M04-48 | 51.61 bcd | 5.62 ijk | 1.97 fg | 82.46 ab | 2.15 g | 1.97 bcde | 1.14 g–j | 2.54 ijk | 1.78 b–e | 0.37 a–d |
| M04-88 | 52.86 ab | 6.15 gh | 1.87 g | 80.8 ab | 3.34 g | 2.04 abc | 1.09 j | 2.58 h–k | 1.8 bcd | 0.33 c–f |
| NC-7 | 52.04 bcd | 8.32 d | 3.78 b | 61.03 d | 19.77 e | 1.03 jk | 1.79 b | 2.8 e–i | 1.29 hij | 0.19 k |
| Olin | 51.11 d | 7.63 e | 3.63 b | 71.97 c | 9.27 f | 1.2 ij | 1.69 b | 2.96 de | 1.43 ghi | 0.22 jk |
| SunO93R | 53.75 a | 5.56 ijk | 2.08 fg | 81.54 ab | 2.91 g | 1.96 bcde | 1.18 efghij | 2.58 h–k | 1.82 bc | 0.38 ab |
| SunO97R | 52.62 abc | 5.44 k | 1.89 g | 82.18 ab | 2.31 g | 2.18 a | 1.1 ij | 2.59 g–k | 1.91 ab | 0.4 a |
| WT4-121 | 52.66 ab | 6.01 ghijk | 1.91 g | 81.41 ab | 3.16 g | 1.93 cdef | 1.11 ij | 2.47 jk | 1.69 c–f | 0.32 efg |
Note: F435, missing oil data. Means with different letters within the same row are significantly different.
Table 6.
Duncan test on oleic fatty acid contents of 4 genotypes of Fad genes.
| Fad2a | Fad2b | Mean | Std Dev | Significant |
|---|---|---|---|---|
| AA | BB | 43.3 | 2.64 | A |
| AA | bb | 60.8 | 3.42 | C |
| aa | BB | 55.7 | 6.57 | B |
| aa | bb | 80.7 | 3.12 | D |
Table 7.
Pearson correlation coefficients and probability values for seed oil content and fatty acid composition among 27 tested varieties.
Table 7.
Pearson correlation coefficients and probability values for seed oil content and fatty acid composition among 27 tested varieties.
| Oil | C16:0 | C18:0 | C18:1 | C18:2 | C20:0 | C20:1 | C22:0 | C24:0 | C26:0 | |
|---|---|---|---|---|---|---|---|---|---|---|
| Oil | 1 | 0.133 | −0.170 * | −0.131 | 0.135 | −0.149 | 0.092 | 0.104 | 0.198 * | 0.083 |
| C16:0 | 1 | 0.366 ** | −0.969 ** | 0.962 ** | 0.396 ** | −0.753 ** | 0.423 ** | −0.415 ** | −0.555 ** | |
| C18:0 | 1 | −0.408 ** | 0.350 ** | 0.954 ** | −0.737 ** | 0.371 ** | −0.688 ** | −0.721 ** | ||
| C18:1 | 1 | −0.996 ** | −0.446 ** | 0.737 ** | −0.461 ** | 0.371 ** | 0.511 ** | |||
| C18:2 | 1 | 0.382 ** | −0.722 ** | 0.395 ** | −0.365 ** | −0.472 ** | ||||
| C20:0 | 1 | −0.713 ** | 0.560 ** | −0.582 ** | −0.734 ** | |||||
| C20:1 | 1 | −0.203 ** | 0.841 ** | 0.783 ** | ||||||
| C22:0 | 1 | 0.101 | −0.411 ** | |||||||
| C24:0 | 1 | 0.788 ** | ||||||||
| C26:0 | 1 |
Note: *, significance with p ≤ 0.05; **, significance with p ≤ 0.01.
Table 8.
Additive effects and G × E interaction involving 2 genes (FAD2A and FAD2B) and 3 environments (2010, 2011, and 2012) for 27 varieties.
Table 8.
Additive effects and G × E interaction involving 2 genes (FAD2A and FAD2B) and 3 environments (2010, 2011, and 2012) for 27 varieties.
| Source | Mean Square | F Value | Pr > F | σ2 | Additive Effect and Interaction |
|---|---|---|---|---|---|
| Year (Y) | 31.98 | 2.18 | 0.1153 | ||
| Rep (year) | 5.2 | 0.36 | 0.9057 | ||
| FAD2A (A) | 2495.2 | 169.87 | <0.0001 | 130.67 | 0.28 |
| FAD2B (B) | 5468.9 | 372.32 | <0.0001 | 292.42 | 0.63 |
| A×B | 167.4 | 11.40 | 0.0008 | 15.88 | 0.03 |
| Y×A | 0.26 | 0.02 | 0.8936 | ||
| Y×B | 62.5 | 4.25 | 0.0152 | 6.33 | 0.01 |
| Y×A×B | 24.5 | 1.67 | 0.1973 | 3.27 | 0.01 |
| Error | 14.68 | 14.68 | 0.03 |
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. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
MDPI and ACS Style
Zhang, H.; Yu, Y.; Wang, M.; Dang, P.; Chen, C. Effect of Genotype-by-Environment Interaction on Oil and Oleic Fatty Acid Contents of Cultivated Peanuts. Horticulturae 2023, 9, 1272. https://doi.org/10.3390/horticulturae9121272
AMA Style
Zhang H, Yu Y, Wang M, Dang P, Chen C. Effect of Genotype-by-Environment Interaction on Oil and Oleic Fatty Acid Contents of Cultivated Peanuts. Horticulturae. 2023; 9(12):1272. https://doi.org/10.3390/horticulturae9121272
Chicago/Turabian StyleZhang, Hui, Yan Yu, Mingli Wang, Phat Dang, and Charles Chen. 2023. "Effect of Genotype-by-Environment Interaction on Oil and Oleic Fatty Acid Contents of Cultivated Peanuts" Horticulturae 9, no. 12: 1272. https://doi.org/10.3390/horticulturae9121272
Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.
