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Article

Genotypic Variation in Agronomic Traits and Molecular Markers among Chinese Luobuma (Apocynum spp.) Germplasm Accessions

by
Yufeng Zhao
1,
Runqiu Feng
1,
Tao Li
1,
Jahufer Mohamed Zain Zulfiqhar
2,
Li Wang
3 and
Jiyu Zhang
1,*
1
State Key Laboratory of Grassland Agro-Ecosystems, Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730020, China
2
AgResearch, Grasslands Research Centre, Private Bag 11008, Palmerston North 4442, New Zealand
3
Altay GAUBAU Tea Co., Ltd., Altay 836509, China
*
Author to whom correspondence should be addressed.
Agriculture 2024, 14(3), 332; https://doi.org/10.3390/agriculture14030332
Submission received: 11 January 2024 / Revised: 22 January 2024 / Accepted: 5 February 2024 / Published: 20 February 2024
(This article belongs to the Section Genotype Evaluation and Breeding)
Browse Figures
Versions Notes

Abstract

:
Apocynum spp., known as Chinese Luobuma species, are perennial herbaceous plants that not only have good ecological characteristics, such as drought resistance, salt resistance, freezing resistance, high-temperature resistance and wind sand resistance, but also have good medicinal and textile value. However, studies on the genetic variation in Chinese Luobuma are rare. In this study, the genotypic variation in the agronomic traits and molecular markers among eight germplasm accessions (referred to as genotypes) of Apocynum spp. was investigated. The accessions were evaluated at two locations in China, Altay and Yuzhong, during a three-year period. Analysis of the variance in yield-related traits revealed significant genotypic variation (p < 0.05) among the eight genotypes at the early flowering and full flowering stages. There were also significant (p < 0.05) genotype × year and genotype × location × year interactions for all the traits except leaf dry weight. In comparison to those evaluated at Yuzhong, the plant height, number of branches, leaf dry weight and stem dry weight at the early flowering stage were greater in Altay, with averages of 991.0 mm, 5.52, 26.41 g and 25.35 g, respectively. There were significant (p < 0.05) differences among genotypes in terms of the quality traits measured at the early and full flowering stages. The crude protein and crude fat content for each genotype at different locations at the early flowering stage in different years ranged from 8.64 to 10.07%. The average flavone (FLA) content was 2.31 mg/100 g. Principal component analysis (PCA) revealed that the G1 genotype in Altay had a higher neutral detergent fiber content and leaf dry weight, and the G2 genotype had a larger stem thickness, branch number and stem-to-leaf ratio. Five DNA sequences, ITS, matK, psbA-trnH, rbcL and trnL-F, were selected for analysis of the molecular variance in Chinese Luobuma. Analyses of molecular variance (AMOVA) based on the nuclear DNA sequences and chloroplast DNA sequences showed that most of the variation occurred within species. Our study indicated the significant genetic variation in Chinese Luobuma for future cultivar domestication. Genotypes with high leaf dry weights and many branches are beneficial for tea production, while tall plants with long internode lengths are valuable for the production of hemp.

1. Introduction

Chinese Luobuma is the general term for Apocynum spp. Apocynum spp. have good textile and medicinal value [1,2] and are helpful for the treatment of liver yang dizziness, palpitations, insomnia, hypertension and neurasthenia [3,4]. In recent years, due to the deterioration of the ecological environment and excessive and aggressive mining driven by economic interests, the number of wild Apocynum spp. has decreased sharply, and these plants are nearly endangered [5]. In recent years, the development of related products has received a lot of attention. The leaves of Apocynum spp. can be used to produce tea [6] due to their medical effects, such as sleep aid and lowering blood pressure [7,8]. The stems of Apocynum spp. can produce hemp, which is mostly blended with other fibers [9]. In addition to Apocynum spp. fibers having excellent characteristics, such as moisture absorption, breathability, antistatic properties and comfort, they also reduce the frequency of far-infrared radiation [10,11]. The flowers of Apocynum spp. can also be used to make essential oils and produce cosmetics [12]. Studies have shown that the essential oil components in Apocynum spp. are mainly alcohols and esters and have inhibitory effects on E. coli and Penicillium [12]. Unfortunately, the genetic potential of these valuable plants has been underutilized due to the lack of new productive varieties with resistance to leaf rust [13,14]. The success of plant breeding depends on the extent of genetic variation in order to improve their traits. The key to detecting the genetic variation among plants, for example, in their agronomic traits, is to distinguish the genetic effects from the non-genetic effects that together make up the observed phenotype [15,16,17].
Agronomic traits are the result of the combined action of genes and the environment [18,19]. The phenotypic variation in agronomic traits among plants is affected not only by their growth environment but also by their individual genetics [20,21]. In Apocynum spp. breeding programs, increasing the biomass yield is the principal goal. A higher biomass yield improves the economic viability and sustainability of Apocynum spp. production [22,23]. In Apocynum spp., biomass was shown to be correlated with several morphological traits, such as plant height, stem diameter and branch number.
The development of DNA barcoding technology has brought new research directions to molecular biology, species classification and identification, etc. Because DNA line codes use unique DNA sequences, a large number of samples can be quickly identified using DNA barcoding through the construction of DNA libraries. At the same time, it is also a kind of molecular marker technology, which has been widely used in many research fields [24,25,26,27]. Due to their synchronous evolution, ITS sequences are found in many species, and there is relatively less intraspecific variation than interspecific variation [28,29]. Although the rbcL + matK composite sequence recommended by the International Plant Barcoding Working Group has a success rate of 86.3% in the identification of vascular plant taxa, there are still some taxa with a low success rate. Combined with previous studies, we believe that plant DNA barcodes should be studied in the form of multi-gene composite barcodes for specific taxa [30]. The trnL-F sequence has the advantages of less selection pressure and a faster evolutionary rate and is often used for phylogenetic analysis of intergeneric and subgenus taxa [31,32].
In this study, eight germplasm accessions of Apocynum spp. were evaluated in terms of their agronomic traits associated with yield and quality from 2017 to 2019. The objective of this study was to evaluate these accessions under field conditions to morphologically characterize them and estimate the genotypic variation among these accessions for agronomic traits. This study was conducted to assess the potential of using these eight accessions to develop base populations for future breeding programs.

2. Materials and Methods

2.1. Plant Materials and Field Trials

Trial location 1 was in Altay City in the Xinjiang Uygur Autonomous Region (E 85°31′36″–91°04′23″, N 45°00′00″–49°10′45″), and the altitude was 548 m. Based on 50 years of data recorded at the Altay weather station, the highest annual temperature is 37.6 °C, the lowest temperature is −43.5 °C, the average annual temperature is 4.5 °C, the average annual precipitation is 131–223 mm, the annual evaporation is 1367–2066 mm and the frost-free period is 123–152 days.
Trial location 2 was in Yuzhong County of Gansu Province (E 103°49′15″–104°34′40″, N 35°34′20″–36°26′30″), and the altitude was 1900 m. The annual average temperature is 6.6 °C, the annual extreme maximum temperature is 35.8 °C, the annual minimum temperature is −27.2 °C and the frost-free period lasts 100–140 days. The annual precipitation is 300–400 mm, and the evaporation is 1343.1 mm.
The eight accessions of Apocynum spp. are presented in Table 1 and Figure 1. A randomized complete block experimental design with 4 replicates was used at each location. Each replicate included more than 100 plants. In each 10 m × 30 m experimental plot, the plant spacing was 1 m and the row spacing was 3 m and we selected 20–50 plants from each replicate.
We collected 8 phenotype seeds, which were incubated and germinated in an incubator at 25 °C. After 15 days of germination, 10 individual plants were selected for each material, and their young tissues were collected and stored at −80 °C for DNA extraction. To ensure the quality of the samples, the A260/A280 value and concentration of the extracted genomic DNA were determined using a NanoDrop ND-1000 (NanoDrop, Wilmington, DE, USA).

2.2. Measurements

The agronomic traits were measured during the early flowering and full flowering growth periods during the years 2017, 2018 and 2019. The yield-related morphological traits were measured in 10 random individual plants sampled from each replicate. The traits measured were: plant height (PH/mm), stem diameter (SD/mm), number of branches (BN), internode length (IL/mm), leaf dry weight (LDW/g), stem dry weight (SDW/g) and the stem-to-leaf ratio (SLR). The leaves and stems (including inflorescences and leaf sheaths) were weighed separately to determine the stem-to-leaf ratio [33].
Quality traits: The leaves of five individual samples were mixed, crushed in a pulverizer and screened through a 1 mm sieve for further measurement; neutral detergent fiber (NDF), acid detergent fiber (ADF), crude fiber (CF), ether extract (EE), crude protein (CP) and ash were also collected. The crude fat was measured using an ANKOM XT15i automatic fat analyzer (ANKOM Technology Corporation, Beijing, China). Neutral detergent fiber, acid detergent fiber and crude fiber were measured using filter bag technology and using an ANKOM A200i semiautomatic fiber analyzer (ANKOM Technology Corporation, China). The flavone (FLA) (mg/100 g) was extracted using high-performance liquid chromatography and using an Agilent XDB C18 column in a methanol–water (65:35) mobile phase.

2.3. ANOVA

All the data collected at the Altay and Yuzhong locations were analyzed within the different growth periods for each year of the 2017, 2018 and 2019 trials. Analysis was conducted at two levels: (i) within individual locations and (ii) across years and locations.
The genotypic variation among the eight germplasm accessions for the 7 yield-related traits, 6 quality traits and flavone (FLA) was estimated by applying linear mixed-model analysis using the residual maximum likelihood (REML) [34,35,36] procedure in DelteGen 3.1 [37,38,39].
The mixed linear model:
Yijkl = M + gi + lj + (gl)ij + yk + (gy)ik + (gly)ijk + rjkl + εijkl
where Yijkl is the value of an attribute measured from accession i in replicate l in location j in year k and i = 1..., ng, j = 1..., nl, k = 1..., n and m is the mean value; gi is the random genotypic effect of accession i, N(0, σ2g); lj is the fixed effect of location j; yk is the fixed effect of year k; rjkl is the random effect of replicate l within location j, in year k, N(0, σ2b); (gl)ij is the effect of the interaction between accession i and location j, N(0, σ2gl); (gy)ik is the effect of the interaction between accession i and year k, N(0, σ2gy); (gly)ijk is the effect of the interaction between accession i, location j and year k, N(0, σ2gly) and εijkp is the residual effect for accession i in replicate l in location j and year k, N(0, σ2Ɛ).

2.4. Pattern Analysis

Pattern analysis, a combination of cluster analysis and principal component analysis, was conducted to provide a multi-trait graphical summary of the performance of the eight germplasm accessions. This analysis was based on the accession-by-trait BLUP matrix constructed using the individual trait outputs generated from the REML analysis across years. Only traits with significant (p < 0.05) genotypic variation among the eight accessions were included in the analysis, which was conducted using the DeltaGen 2.0 software.

2.5. Phenotypic Correlation

The phenotypic correlation coefficients among the eight accessions for the traits measured were estimated using the multivariate analysis option in DelteGen.

2.6. Genetic Variation in Molecular Markers

ITS, psbA-trnH, matK, trnL-F and rbcL were selected for PCR amplification. The sequences of primers used are listed in Table S7. The PCR mixture included 12.5 µL of 2× Master Mix, 2.5 µL of the upstream and downstream primers, 2.5 µL of 50 ng/µL template DNA and 5 µL of dd H2O. The PCR procedure was as follows: pre-denaturation at 95 °C for 3 min; 35 cycles of denaturation at 94 °C for 30 s; annealing at 55 °C for 40 s; extension at 72 °C for 50 s; extension at 72 °C for 7 min and preservation at 4 °C. The PCR amplification products were detected using 1% agarose gel electrophoresis, and products of the appropriate fragment sizes and meeting the sequencing standards were subsequently sent to Sangong Bioengineering (Shanghai, China) Co., Ltd., for Sanger sequencing. The Chromas software (version 2.22) and the Sequencher software (version 4.8) were used for sequence correction and manual calibration of the DNA sequencing results. The MEGA 7.0 software was used for sequence alignment, and neighbor-joining (NJ) was used to construct a phylogenetic tree for the clustering. AMOVA was performed using Arlequin version 3.5 [40].

3. Results

3.1. Genotypic Variance Components of Yield-Related Traits

The analysis of variance indicated significant (p < 0.05) genotypic variance in the yield-related traits at the early flowering stages across years 2018 and 2019 and across locations in Altay and Yuzhong (Table 2). The accession-by-year interaction effects were significant (p < 0.05) for the traits PH, SD, LDW and SDW. There were also significant (p < 0.05) accession-by-year-by-location interactions for all the traits except for the stem dry weight and leaf dry weight. There was no significant difference (p > 0.05) in the annual genotypic variation. The mean plant height across years ranged from 66.41 cm to 77.98 cm, and the mean stem diameter and internode length were 0.43 cm and 3.73 cm, respectively. The leaf dry weight ranged from 19.15 g to 21.77 g.
Analysis of the data collected from Altay showed significant (p < 0.05) genotypic variance among the eight accessions and an interaction between the accession and year for yield-related traits at the early flowering stage from 2017 to 2019 (Table S1). There was significant (p < 0.05) genotypic variance among the accessions for the yield-related traits and the accession-by-year interactions in Yuzhong at the early flowering stage from 2017 to 2019 (Table S2).
The analysis of variance indicated significant (p < 0.05) genotypic variance among the eight accessions for the yield-related traits at the full flowering stages from 2017 to 2018 in Altay, except for the trait of IL (Table 3). There were also significant (p < 0.05) genotype-by-year interactions, except for with SLR. However, there was no significant (p > 0.05) variation among years, except for with SD. The mean plant height among the accessions in different years ranged from 54.34 cm to 82.77 cm. The mean stem diameter and internode length were 0.39 cm and 4.03 cm, respectively, and the leaf dry weight ranged from 10.19 g to 16.45 g.

3.2. Genotypic Variance Components for Nutritional Quality Traits

The analysis of variance indicated significant (p < 0.05) genotypic variation in the nutritional quality traits among the eight accessions at the early flowering stage of 2018 across the two locations, Altay and Yuzhong (Table 4). These trait means and ranges indicated wide phenotypic variation in the nutritional quality traits and FLA content in the Apocynum spp. The genotypic variances estimated among the eight accessions for all the different traits measured were significant (p < 0.05) (Table 4). There were no significant (p > 0.05) differences among locations and no genotype-by-location interaction variance, except for in CP and FLA. The crude protein content in different years ranged from 12.18% to 16.77%, the average crude fiber and ash contents were 22.16% and 11.28%, respectively, and the average FLA content was 2.12 mg/100 g.
In Altay, there was significant (p < 0.05) genotypic variance among the eight accessions in the nutritional quality traits during the early flowering stages, from 2017 to 2018, except for the traits CP and NDF (Table S3). There were no significant (p > 0.05) differences among years or in the genotype-by-year interaction variance, except for that of CP. Compared to those in the full flowering stage, the early flowering stage presented higher levels of NDF, ADF and FLA (33.97%, 79.09% and 2.64 mg/100 g, respectively). There was significant (p < 0.05) genotypic variance in the nutritional quality traits among the eight accessions at the full flowering stage during 2017 and 2018 (Table S4). There were no significant (p > 0.05) differences among the years or among the genotype-by-year interactions, except for in the CP and FLA. In Yuzhong, there was significant (p < 0.05) genotypic variance among the eight accessions for the nutritional quality traits during the early flowering stage in 2018, except for in the CP, NDF and CF (Table S5).

3.3. Pattern Analysis

The biplot generated from the PCA based on the yield and quality traits measured in Altay during the full flowering stages in 2017 and 2018 indicated that the eight germplasm accessions were clustered into three groups (Figure 2). In group 2, accessions G2, G3, G4 and G6 had a greater average stem diameter, stem-to-leaf ratio, number of branches and amount of crude protein. Accession G1 had high neutral detergent fiber.
According to the biplot generated from the PCA using the yield and quality traits measured in Altay during the early flowering stages in 2017 and 2018 (Figure S2), the first principal component explained 34.8% of the total trait variation. The above-average plant height, branch number, internode length and leaf dry weight were shown for accessions G3, G4 and G5 in group 4. Accession G1 had a high crude fiber content and stem diameter. Accession G7 had a high stem-to-leaf ratio. A biplot (Figure 3) generated from the PCA of the yield and quality traits measured in Yuzhong during the early flowering stages in 2018 and 2019 indicated six accession groups. Accessions G2, G3 and G4 had above-average stem diameters and acid detergent fiber contents. Accession G1 had high amounts of neutral washing fibers and crude fat and a high internode length, crude fiber content and branch number.

3.4. Phenotypic Correlation

The phenotypic correlation coefficients among the eight accessions for the traits measured during the early flowering stage in Altay are presented in Table 5. The correlation coefficients ranged from strongly to weakly positive or negative pairwise associations between the 12 traits. Of special interest are the phenotypic correlations between the FLA and the other traits. The correlation coefficients between the FLA and PH and between the FLA and IL were −0.86 and −0.71, respectively, indicating a strong negative phenotypic correlation (p < 0.05).
The phenotypic correlation coefficients among the accessions for the different traits measured during the early flowering stage in Yuzhong are presented in Table S6. These coefficients ranged from strongly to weakly positive or negative pairwise associations between the 14 traits. The correlation coefficients between LDW and SDW and between LDW and BN were 0.93 and 0.63, respectively, indicating significant positive phenotypic correlations (Table S6).

3.5. Genotypic Variance Components of Molecular Markers

The PCR amplification efficiency and sequencing success rate are important indices for evaluating molecular markers. The analysis of each DNA sequence showed that the percentage range of GC content was 34.78–61.05%. The GC content was highest in the ITS sequence and lowest in the matK sequence. The number of variation sites ranged from 5 to 37, among which the psbA-trnH sequence had the most variation sites (Table S8).
The analyses of molecular variance (AMOVAs) for Chinese Luobuma based on the ITS sequences revealed that most of the variation occurred within groups. For the combined sequences of matK+psbA-trnH+trnL-F+rbcL, most of the variation also occurred within groups (Table 6). Cluster analysis of eight genotypes based on five sequences was performed using neighbor-joining, and a dendrogram was inferred (Figure 4). The sequence comparison of ITS, matK, psbA-trnH, rbcL and trnL-F in eight genotypes and the gel diagram showed that the sequence differences of five molecular markers could be clearly displayed (Figures S3 and S4). ITS, matK, rbcL and trnL-F can be used to divide all accessions into two major clusters and distinguish G1 from the other genotypes. The composite sequence matK+psbA-trnH+trnL-F+rbcL can also be used to divide all the genotypes into two clusters. However, we found that G1 and G3 were clustered together (Figure S5). The groups were generated according to species type; most Apocynum spp. accessions were grouped together, as was G8 (Figure 4). psbA-trnH could not distinguish the eight genotypes because the sequence was short and the similarity was high. We found that not all single-molecule marker techniques were able to distinguish the eight genotypes at the species level.

4. Discussion

A. venetum has a good ecological restoration ability and good feeding value. Moreover, these plants are taller and contain more branches, which can aid in wind prevention and sand fixation. This approach is helpful for ecological restoration [41]. There is good evidence that plant breeding has successfully improved populations whenever there is genetic variation within germplasm pools, and selection has been focused on the right traits being measured in the appropriate environments [42]. Previous studies have reported the results of germplasm evaluation in terms of pest and disease resistance in Apocynum spp. [43,44] and have economic value for agriculture, medicine and industry [25]. However, very few varieties are registered in China. The development of new varieties of Apocynum spp. has become a priority in China to increase the utilization of these valuable species [25].
Information on the phenotypic and genotypic diversity in germplasms in terms of the agronomic traits associated with breeding objectives enhances the development of appropriate breeding methods. Estimates of the genotypic and genetic variation in agronomic traits have been reported for the Gossypium barbadense [25], Glycine max [45], Melilotus officinalis [46] and Oryza sativa subspecies [47] and many other plant species [48,49,50].
In our study, analysis of the agronomic traits showed that Apocynum spp. are tall plants with a high number of branches and high leaf yield. These characteristics make these species useful for ecological restoration [51]. The stems of Apocynum spp. can produce hemp, and the internode length is a direct indicator of the length and toughness of the hemp plants. Therefore, the internode length measured in our study can provide a basis for selecting and breeding specific varieties of industry hemp [52,53]. Apocynum spp. show a good forage palatability with a high leaf dry weight and low crude fiber and crude ash contents [54,55]. Tea prepared from Apocynum spp. leaves has gained popularity as a nutritional supplement beverage for anti-aging purposes [56]. The flavonoid content in Luobuma tea is an important indicator of the tea quality [57], as flavonoids can scavenge free radicals [58,59]. Therefore, to select high-quality Apocynum spp. plants, we measured the flavonoid content. We found that the flavonoid content was 2.12 mg/100 g during the early flowering stage. Moreover, the flavonoid content during the full flowering stage was greater than that during the early flowering stage in the same year and at the same location.
The presence of genotype × environment interactions complicates the selection of material for broad adaptation due to variable relative performances across environments [55]. Quantifying the magnitude and understanding the causes of genotype × environment interactions can be helpful when planning breeding strategies [60,61]. Studies have reported that a range of traits in white clover, especially yield-related traits, are sensitive to genotype × environment interactions [62,63,64]. In our study, most of the traits exhibited significant differences under interactions between genotype and environment, indicating the importance of multisite evaluation. In this study, the yield and quality traits of Apocynum spp. were evaluated in Altay and Yuzhong, China. The results showed that there were significant genotypic differences in the yield traits among the genotypes and among the genotype × year and genotype × year × location interactions (p < 0.05). There were significant differences in the quality traits among the genotypes (p < 0.05).
The application of pattern analysis in this study provided a graphical summary of the yield and quality traits of the Apocynum spp. evaluated in different years and locations. These results will aid in the identification of genotypes with trait combinations beneficial for developing varieties for tea and hemp production. The principal component analysis (PCA) revealed that G1 had a greater leaf dry weight, branch number and plant height, while G2 had a greater stem diameter, internode length and plant height. In a similar study, Luo et al. (2018) [22] used pattern analysis to examine the associations among key agronomic traits of Melilotus albus and identified material for breeding new varieties with a high yield and a low coumarin content. Correlation analysis revealed that the crude protein content was positively associated with leaf dry weight, while the flavone content was negatively correlated with the plant height and internode length. These relationships between traits provide a basis for the future selection of new Apocynum spp. varieties (lines) with favorable yield and quality traits. From the different genotypes of Apocynum spp., we screened out the genotypes conducive to tea production, with a large leaf dry weight and more branches, and the genotypes conducive to hemp production, with tall plants, long internodes and large stems, which can a provide theoretical basis for breeding new varieties (lines) of Apocynum spp. with good agronomy and quality traits and suitable for domestic popularization.
The genetic background of Apocynum spp. is complex, and there are many genotypes affected by habitat changes. Apocynum spp. varieties are cultivated through genetic selection in different environments. In this study, cluster analysis of the eight genotypes of Apocynum spp. based on different barcodes found that there was a large genetic difference between G4 and the other genotypes, suggesting that there might be gene exchange between G4 and other genotypes, or it might be a heterozygote with multiple parental sources. Molecular marker technology is based on the nucleotide sequence variation in genetic material between individuals [23,65]. It is often used to detect differences between organisms. Compared with morphological, biochemical and cytological markers, molecular markers have many advantages [66]. For example, most molecular markers are codominant, and it is very convenient to select recessive traits [67,68]. The success rate of individual DNA barcodes for plant identification varies, especially in hybrids or varieties with a gene penetration phenomenon [30]. Scholars have commonly chosen DNA barcodes from nucleotide gene sources and chloroplast sources for plant identification and found that the DNA barcodes from the nuclear gene source had higher species-specific differences [69]. Of course, our results are similar to those of previous studies. On the other hand, DNA barcodes evolve at different rates from chloroplast-derived DNA barcodes, and the identification success rate is also different [23]. This study also confirmed that the chloroplast-derived DNA barcodes in different genotypes of Apocynum spp. had fewer loci of variation (except psbA-trnH, the barcode with the most loci of variation), and the genetic distance of each barcode was less different among different genotypes.
The success rate of individual DNA barcodes for plant identification varies, especially in hybrids or varieties with gene penetration phenomena [30]. Therefore, some scholars have proposed using DNA barcode sequence combination to solve the problem. The arpF-atpH+psbK-psbL+trnH-psbA combination barcode was used to identify Orchidaceae plants, and the identification success rate was 98.8% [23]. In this study, phylogenetic tree analysis of different barcodes showed that some individual barcodes were less able to distinguish different genotypes of Apocynum spp., and the eight genotypes could not be distinguished at the genetic level using different combinations of barcodes. Genomes are rich in variation, and the number of molecular markers is almost unlimited []. In our study, the analyses of molecular variance (AMOVAs) for Chinese Luobuma based on the nuclear and chloroplast sequences showed that most of the variation occurred within species. Using their agronomic traits and DNA barcoding technology, phenotypic morphological analysis and studies on the molecular genetic variation in different genotypes of Apocynum spp. were conducted, aiming to reveal the phenotypic differences in different genotypes of Apocynum spp. and provide a theoretical basis for the breeding of new varieties (lines) with a high yield and quality.

5. Conclusions

The significant genotype × year and genotype × year × location interactions estimated for the yield traits across the two locations, Yuzhong and Altay, indicate the importance of conducting multilocation trials to develop new broadly adapted varieties in China. The estimates of the genotypic variation indicated the potential genetic variation available in the key agronomic traits of Apocynum spp. At the Altay site, there were significant differences between the genotypes in the quality traits at the early flowering stage and the full flowering stage (p < 0.05). Principal component analysis found that the genotype G1 in Altay has a higher neutral detergent fiber content and leaf dry weight, and the genotype G2 has a larger stem thickness, branch number and stem-to-leaf ratio.
The barcodes matK, rbcL and trnL-F could divide all genotypes into two groups, which can distinguish Apocynum spp. and G8. ITS, matK, rbcL and trnL-F can be used to divide all the genotypes into two major clusters. The analyses of molecular variance (AMOVAs) for Chinese Luobuma based on five sequences showed that most of the genetic variation occurred within species. For the matK, psbA-trnH, trnL-F and rbcL sequences, most of the variation presented among the genotypes.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agriculture14030332/s1, Figure S1: Distribution map of sampling locations. Figure S2: Principal component analysis of agronomic traits in different genotypes of Apocynum spp. and Poacynum spp. during early flowering stage from 2017-2019 at Altay. Figure S3: gel pictures of five molecular markers for 8 genotypes. Figure S4: Comparison of sequences for eight genotypes ITS, matK, psbA-trnH, rbcL, trnL-F. Figure S5: Neighbor-joining (NJ) tree for different phenotypes using matK + psbA-trnH + rbcL + trnL markers. Table S1: Trait mean, range, least significant differences (LSD0.05), genotypic (σ2g), year (σ2y), accession-by-year interaction (σ2gy), and experimental error (σ2Ɛ) variance components, and associated standard errors (±SE), estimated from 8 accessions, for yield related traits at the early flowering stage from 2017 to 2019 at Altay. Table S2: Trait average, range, least significant differences (LSD0.05), genotypic (σ2g), year (σ2y), genotype × year interaction (σ2gy), and experimental error (σ2Ɛ) variance components, and associated standard errors (±SE), estimated from 8 genotypes, evaluated yield related traits at early flowering stage from 2018 to 2019 at Yuzhong. Table S3: Nutritional quality average, range, least signifcant differences (LSD0.05), genotypic (σ2g), year (σ2y) and genotype with year (σ2gy), and experimental error (σ2Ɛ) variance components, and associated standard errors (±SE), estimated from 8 genotypes, evaluated in early flowering stage at Altay. Table S4: Nutritional quality average, range, least significant differences (LSD0.05), genotypic (σ2g), year (σ2y) and genotype with year (σ2gy), and experimental error (σ2Ɛ) variance components, and associated standard errors (±SE), estimated from 8 genotypes, evaluated in full flowering stage at Altay. Table S5: Nutritional quality average, range, least significant differences (LSD0.05), genotypic (σ2g), and experimental error (σ2Ɛ) variance components, and associated standard errors (±SE), estimated from 8 genotypes, evaluated in early flowering stage at Yuzhong. Table S6: Correlation analysis of characters of Apocynum and Poacynum of different genotypes during early flowering stage from 2017 to 2018 at Yuzhong. Table S7: Primer information of different gene segments. Table S8: Sequences information of five sequences.

Author Contributions

Y.Z. and J.Z. conceived and designed the study. Y.Z., R.F. and T.L. performed the experiments. L.W. provided the experimental plots and places. Y.Z. and J.M.Z.Z. wrote the paper. Y.Z., J.Z. and J.M.Z.Z. reviewed and edited the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

Study on molecular basis of quality character formation of Melilotus albus and creation of high quality new materials (lzujbky-2022-ey17); Germplasm innovation and breed selection of important native grasses and herbage in Gansu province (23ZDKA013); the Major Science and Technology Special Project of Xinjiang Uygur Autonomous Region (2016A03006).

Institutional Review Board Statement

Not applicable.

Data Availability Statement

No new data were created or analyzed in this study. Data sharing is not applicable to this article.

Acknowledgments

The authors appreciate their laboratory classmates for their help in measuring all the traits.

Conflicts of Interest

Author Jahufer Mohamed Zain Zulfiqhar was employed by the AgResearch, and Author Li Wang was employed by the ltay GAUBAU Tea Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

References

  1. Kobayashi, M.; Saitoh, H.; Seo, S.; Butterweck, V.; Nishibe, S. Apocynum venetum extract does not induce CYP3A and P-glycoprotein in rats. Biol. Pharm. Bull. 2004, 27, 1649–1652. [Google Scholar] [CrossRef]
  2. Wang, L.L.; Han, G.T.; Zhang, Y.M. Comparative study of composition, structure and properties of Apocynum venetum fibers under different pretreatments. Carbohydr. Polym. 2007, 69, 391–397. [Google Scholar] [CrossRef]
  3. Guo, H.; Kuang, Z.P.; Zhang, J.; Zhao, X.; Pu, P.; Yan, J.F. The preventive effect of Apocynum venetum polyphenols on D-galactose-inducedoxidative stress in mice. Exp. Ther. Med. 2020, 19, 113–119. [Google Scholar]
  4. Li, C.; Huang, J.B.; Tan, F.; Zhou, X.R.; Mu, J.F.; Zhao, X. In vitro analysis of antioxidant, anticancer, and bioactive components of Apocynum venetum tea extracts. J. Food Qual. 2019, 2019, 346–359. [Google Scholar] [CrossRef]
  5. Geng, Y.W. Study on Genetic Diversity and Quality Evaluation of Wild Apocynum venetum Population. Master’s Thesis, Inner Mongolia Agricutural University, Huhhot, China, 2021. [Google Scholar]
  6. Zhou, J.; Sun, J.B.; Xu, X.Y.; Cheng, Z.H.; Zeng, P.; Wang, F.Q.; Zhang, Q. Application of mixed cloud point extraction for the analysis of six flavonoids in Apocynum venetum leaf samples by high performance liquid chromatography. J. Pharm. Biomed. Anal. 2015, 107, 273–279. [Google Scholar] [CrossRef] [PubMed]
  7. Xie, W.Y.; Chen, C.; Jiang, Z.H.; Wang, J.; Melzig, M.F.; Zhang, X.Y. Apocynum venetum attenuates acetaminophen-induced liver injury in Mice. Am. J. Chin. Med. 2015, 43, 457–476. [Google Scholar] [CrossRef] [PubMed]
  8. Zhang, Y.; Ma, X.Y.; Zhang, T.; Qin, M.; Sun, B.; Li, Q.; Hu, D.W.; Ren, L.Q. Protective effects of Apocynum venetum against pirarubicin-induced cardiotoxicity. Am. J. Chin. Med. 2019, 47, 1075–1097. [Google Scholar] [CrossRef]
  9. Li, M.H.; Han, G.T.; Chen, H.; Yu, J.Y.; Zhang, Y.M. Chemical compounds and antimicrobial activity of volatile oils from bast and fibers of Apocynum venetum. Fibers Polym. 2012, 13, 322–328. [Google Scholar] [CrossRef]
  10. Halim, F.M.; Lv, Z.Y.; Chen, Y.D.; Ma, M.B.; Liu, H.F.; Zhou, W.L. Fidelity of new chemical degumming method for obtaining superior properties of Bast fiber from Apocynum venetum. Text. Res. J. 2019, 31, 112–120. [Google Scholar] [CrossRef]
  11. Gong, J.X.; Zhang, Q.Y.; Lou, J.F.; Zhang, T.; Li, Z.; Li, Q.J.; Li, H.Q.; Zhang, J.F. Investigation of the degradation of bio-recalcitrance in Apocynum venetum fiber biodegumming. J. Nat. Fibers 2019, 16, 1–12. [Google Scholar] [CrossRef]
  12. Wang, L.L.; Zhang, X.F.; Niu, Y.Y.; Ahmed, A.F.; Wang, J.M.; Kang, W.Y. Anticoagulant activity of two novel polysaccharides from flowers of Apocynum venetum L. Int. J. Biol. Macromol. 2019, 124, 1230–1237. [Google Scholar] [CrossRef] [PubMed]
  13. Gao, P.; Duan, T.Y.; Christensen, M.J.; Nan, Z.B.; Liu, Q.T.; Meng, F.J.; Huang, J.F. The influence of irrigation frequency on the occurrence of rust disease (Melampsora apocyni) and determination of the optimum irrigation regime in organic Apocynum venetum production. Agric. Water Manag. 2018, 205, 81–89. [Google Scholar] [CrossRef]
  14. Gao, P.; Nan, Z.B.; Christensen, M.J.; Barbetti, M.J.; Duan, T.Y.; Liu, Q.T.; Meng, F.J.; Huang, J.F. Factors Influencing Rust (Melampsora apocyni) Intensity on Cultivated and Wild Apocynum venetum in Altay Prefecture, China. Phytopathology 2019, 109, 593–606. [Google Scholar] [CrossRef] [PubMed]
  15. Viana, P.; Riaz, S.; Walker, M.A. Genetic dissection of agronomic traits within a segregating population of breeding table grapes. Genet. Mol. Res. GMR 2013, 12, 951–964. [Google Scholar] [CrossRef] [PubMed]
  16. Abebe, M.; Melaku, G.; Sherry, T.; Adegoke, A.; Bunmi, B. Carotenoid accumulation and agronomic performance of maize hybrids involving parental combinations from different marker-based groups. Food Chem. 2013, 148, 131–137. [Google Scholar]
  17. Kouamé, C.N.; Quesenberry, K.H. Cluster analysis of a world collection of red clover germplasm. Genet. Resour. Crop Evol. 1993, 40, 112–118. [Google Scholar] [CrossRef]
  18. Ramadan, M.; Gamal, S.M.A.; Selim, F.A. Mechanical properties, radiation mitigation and fire resistance of OPC-recycled glass powder composites containing nanoparticles. Constr. Build. Mater. 2020, 251, 12–21. [Google Scholar] [CrossRef]
  19. Ponnilavan, V.; Alam, M.M.; Ezhilan, M.; Pandian, K.; Kannan, S. Structural, mechanical, morphological and optical imaging characteristics of Yb3+ substituted zirconia toughened alumina. Mater. Today Commun. 2020, 24, 78–85. [Google Scholar]
  20. Liu, L.Z.; Zhu, B.; Si, J.P.; Zhang, X.L.; Gao, T.T.; Zhu, Y.Q. Studies on agronomic traits of seedlings from different F1 generations of Dendrobium officinale. China J. Chin. Mater. Medica 2013, 38, 498–503. [Google Scholar]
  21. Zhang, J.Y.; Di, H.Y.; Luo, K.; Jahufer, M.Z.Z.; Wu, F.; Duan, Z.; Stewart, A.; Yan, Z.Z.; Wang, Y.R. Coumarin content, morphological variation, and molecular phylogenetics of Melilotus. Molecules 2018, 23, 810. [Google Scholar] [CrossRef]
  22. Luo, K.; Jahufer, M.Z.Z.; Zhao, H.; Zhang, R.; Wu, F.; Yan, Z.Z.; Zhang, J.Y.; Wang, Y.R. Genetic improvement of key agronomic traits in Melilotus albus. Crop Sci. 2018, 58, 285–294. [Google Scholar] [CrossRef]
  23. Xie, W.Y.; Zhang, X.Y.; Wang, T.; Hu, J.J. Botany, traditional uses, phytochemistry and pharmacology of Apocynum venetum L.: A review. J. Ethnopharmacol. 2012, 141, 1–8. [Google Scholar] [CrossRef] [PubMed]
  24. Luo, K.; Jahufer, M.Z.Z.; Wu, F.; Di, H.Y.; Zhang, Y.F.; Meng, X.C.; Zhang, J.Y.; Wang, Y.R. Genotypic variation in a breeding population of yellow sweet clover (Melilotus officinalis). Front. Plant Sci. 2016, 7, 234–241. [Google Scholar] [CrossRef]
  25. Liu, J.; Gao, L.M.; Li, D.Z. Integrating a comprehensive DNA barcode reference library with a global map of yews (Taxus L.) for forensic identification. Mol. Ecol. Resour. 2018, 18, 1115–1131. [Google Scholar] [CrossRef]
  26. Wu, F.; Ma, J.X.; Meng, Y.Q.; Pascal Muvunyi, B.; Luo, K.; Zhang, J. Potential DNA barcodes for Melilotus species based on five single loci and their combinations. PLoS ONE 2017, 12, 69–82. [Google Scholar] [CrossRef] [PubMed]
  27. Zhou, H.; Ma, S.J.; Song, J.Y.; Lin, Y.L.; Wu, Z.J.; Han, Z.Z.; Yao, H. QR code labeling system for Xueteng-related herbs based on DNA barcode. Chin. Herb. Med. 2019, 11, 52–59. [Google Scholar] [CrossRef]
  28. Solano, J.; Anabalón, L.; Encina, F.; Esse, C.; Penneckamp, D. Hybrid identification in Nothofagus subgenus using high resolution melt with ITS and trnL approach. PeerJ 2019, 7, 67–79. [Google Scholar] [CrossRef]
  29. Guo, X.; Wang, Z.; Cai, D.; Song, L.; Bai, J. The chloroplast genome sequence and phylogenetic analysis of Apocynum venetum L. PLoS ONE 2022, 17, 0261710. [Google Scholar] [CrossRef]
  30. Tanaka, S.; Ito, M. DNA barcoding for identification of agarwood source species using trnL-trnF and matK DNA sequences. J. Nat. Med. 2019, 19, 42–50. [Google Scholar] [CrossRef]
  31. Wang, Y.S.; Feng, D.L.; Xue, H.; Nie, C.; Li, E.; Wu, X. Universal DNA primers for amplification of complete mitochondrial protein-coding genes and ribosomal RNA genes from Crocodilia. Conserv. Genet. Resour. 2013, 5, 873–877. [Google Scholar] [CrossRef]
  32. Zheng, C.; Fan, J.; Caraballo-Ortiz, M.A.; Liu, Y.; Liu, T.; Fu, G.; Su, X. The complete chloroplast genome and phylogenetic relationship of Apocynum pictum (Apocynaceae), a Central Asian shrub and second-class national protected species of western China. Gene 2022, 830, 146517. [Google Scholar] [CrossRef]
  33. GB/T 23387-2009; Evaluation of Forage Nutritional Quality-Grading Index (GI) Method. Chinese National Standard: Beijing, China, 2009. (In Chinese)
  34. Harville, D.H. Maximum likelihood approaches to variance component estimation and related problems. J. Am. Stat. Assoc. 1977, 72, 320–340. [Google Scholar] [CrossRef]
  35. Patterson, H.D.; Thompson, R. Recovery of inter-block information when block sizes are unequal. Biometrika 1971, 58, 445–554. [Google Scholar] [CrossRef]
  36. Patterson, H.D.; Thompson, R. Maximum likelihood estimation of components of variance. In Proceedings of the 8th International. Biometrical Conference, Constanta, Romania, 25–30 August 1977; pp. 197–207. [Google Scholar]
  37. Jahufer, M.Z.Z.; Luo, D.W. DeltaGen: A comprehensive decision support tool for plant breeders. Crop Sci. 2018, 58, 1251–1257. [Google Scholar] [CrossRef]
  38. Jahufer, M.Z.Z.; Cooper, M.; Bray, R.A.; Ayres, J.F. Evaluation of white clover (Trifolium repens L.) populations for summer moisture stress adaptation in Australia. Aust. J. Agric. Res. 1999, 50, 561–574. [Google Scholar] [CrossRef]
  39. Jahufer, M.Z.Z.; Luo, D.W. Pattern analysis; New pattern analysis study findings have been reported by researchers at AgResearch (DeltaGen: A comprehensive decision support tool for plant breeders). J. Eng. 2018, 67, 87–93. [Google Scholar]
  40. Excoffier, L.; Lischer, H.E.L. Arlequin suite ver 3.5: A new series of programs to perform population genetics analyses under Linux and Windows. Mol. Ecol. Res. 2010, 10, 564–567. [Google Scholar] [CrossRef]
  41. Asiya, M.; Sun, T.; Selike, D.; Cui, H.X. Resources and development and Utilization Prospect of Daye white hemp in Xinjiang. J. China Agric. Resour. Reg. Plan. 2003, 24, 27–30. [Google Scholar]
  42. Cooper, M.; Messina, C.D.; Podlich, D.; Totir, L.R.; Baumgar-ten, A.; Hausmann, N.J. Predicting the future of plant breeding: Complementing empirical evaluation with genetic prediction. Crop Past. Sci. 2014, 65, 311–336. [Google Scholar] [CrossRef]
  43. Zhang, X.W.; Cao, Y.; Zhang, W. Adenine·cytosine substitutions are an alternative pathway of compensatory mutation in angiosperm ITS2. RNA 2020, 26, 123–128. [Google Scholar] [CrossRef]
  44. Zhang, Y.X.; Li, G.Q.; Zhang, Q.; Zhang, H.; Zhu, L.; Wan, H.X. The preliminary research on photosynthetic characteristics of Apocynum venetum under different shading. Northwest Bot. 2007, 27, 2555–2558. [Google Scholar]
  45. Ma, Q.; Su, J.J.; Ning, X.Z.; Li, J.L.; Liu, P.; Chen, H.; Lin, H.; Deng, F.J. Genetic diversity analysis on phenotypic traits of sea island cotton (G. barbadense) germplasm resources in Xinjiang. Xinjiang Agric. Sci. 2016, 53, 197–206. [Google Scholar]
  46. Pu, Y.Y.; Gong, Y.C.; Li, N.N.; Liu, Y.; Wang, Q.L.; Song, D.T.; Yan, T.J.; Ding, H.F. The progress in genetic diversity of the soybean germplasm in China. Soy Sci. 2018, 37, 315–321. [Google Scholar]
  47. Yang, J.; Yu, H.Y.; Li, X.J.; Dong, J.G. Genetic diversity and population structure of Commelina communis in China based on simple sequence repeat markers. J. Integr. Agric. 2018, 17, 2292–2301. [Google Scholar] [CrossRef]
  48. Sun, L.C. Genetic Diversity Analysis of Early Japonica Rice Germplasm in Ningxia and Northeast Reigon of China; Ningxia University: Yinchuan, China, 2016. [Google Scholar]
  49. Shi, Y.C.; Tang, J.M.; Chai, S.F.; Zou, R.; Chen, Z.Y.; Wei, X. Genetic diversity and relationship of endangered plant Heteroplexis microcephala assessed with ISSR polymorphisms. Guangxi Plants 2017, 37, 9–14. [Google Scholar]
  50. Yu, B.; Liu, J.; Li, M.H.; Duan, H.M.; Wang, P.F.; Yuan, J.L.; Ye, X.M. Evaluation on the color characters of tuber and flour of introduced potato germplasm resources. J. Gansu Agric. Univ. 2019, 54, 37–47. [Google Scholar]
  51. Zhang, S.J.; Li, X.H.; Liu, C.J.; Tang, L.Y.; Zhang, X.Y.; Zhang, J.Y.; Wang, H.T. Analysis and evaluation on nutritive quality indicators of different varieties of radish. Orthern Hortic. 2018, 11, 8–14. [Google Scholar] [CrossRef]
  52. Sancin, P. The phenolic compounds of underground parts of Apocynum venetum. Planta Medica 1971, 20, 123–127. [Google Scholar] [CrossRef]
  53. Li, M.H.; Han, G.T.; Yu, J.Y. Microstructure and mechanical properties of Apocynum venetum fibers extracted by alkali-assisted ultrasound with different frequencies. Fibers Polym. 2010, 11, 112–119. [Google Scholar] [CrossRef]
  54. Song, C.H.; Zhang, C.Q.; Li, G.L.; Zhang, X.L.; Chen, G.; You, J.M. Highly selective and sensitive determination of free and total amino acids in Apocynum venetum L. (Luobuma tea) by a developed HPLC–FLD method coupled with pre-column fluorescent labelling. Int. J. Food Sci. Nutr. 2012, 63, 78–86. [Google Scholar] [CrossRef]
  55. Lei, J.Q. Quantitative determination of quercetin in the leaves of Luo-Bu-Ma (Apocynum venetum). Zhong Yao Tong Bao 1982, 7, 145–152. [Google Scholar]
  56. Wei, Z.; Zheng, D.; Chang, X.J.; Zhang, C.H.; Rong, G.H.; Gao, X.D.; Zeng, Z.; Wang, C.P.; Chen, Y.; Rong, Y.H.; et al. Protective effect of the total flavonoids from Apocynum venetum L. on carbon tetrachloride-induced hepatotoxicity in vitro and in vivo. J. Physiol. Biochem. 2018, 74, 301–312. [Google Scholar]
  57. Grundmann, O.; Mnakajima, J.; Seo, S.J.; Butterweck, V. Anti-anxiety effects of Apocynum venetum L. in the elevated plus maze test. J. Ethnopharmacol. 2006, 110, 406–411. [Google Scholar] [CrossRef]
  58. Tan, Z.J.; Yi, Y.J.; Wang, H.Y.; Zhou, W.L.; Wang, C.Y. Extraction, preconcentration and isolation of flavonoids from Apocynum venetum L. leaves using lonic liquid-based ultrasonic-assisted extraction coupled with an aqueous biphasic system. Molecules 2016, 21, 262. [Google Scholar] [CrossRef] [PubMed]
  59. Cao, Y.H.; Zhang, X.; Fang, Y.Z.; Ye, J.N. Determination of active ingredients of Apocynum venetum by capillary electrophoresis with electrochemical detection. Mikrochim. Acta 2001, 137, 57–62. [Google Scholar] [CrossRef]
  60. Xu, Y.C.; Wang, C.; Liu, H.S.; Zhu, G.L.; Fu, P.; Wang, L.P.; Zhu, W.M. Meroterpenoids and isocoumarinoids from a myrothecium fungus associated with Apocynum venetum. Mar. Drugs 2018, 16, 363. [Google Scholar] [CrossRef] [PubMed]
  61. Hale, R.W.; Coey, W.E. Genotype-environment interactions in a herd of bacon pigs. J. Agric. Sci. 1963, 61, 81–87. [Google Scholar] [CrossRef]
  62. Herrera-Ojeda, J.B.; Parra-Bracamonte, G.M.; Lopez-Villalobos, N.; Martínez-González, J.C.; Magaña-Monforte, J.G.; Morris, S.T.; López-Bustamante, L.A. Genetic variances and covariances of live weight traits in Charolais cattle by multi-trait analysis. J. Appl. Genet. 2019, 60, 385–391. [Google Scholar] [CrossRef] [PubMed]
  63. Wade, L.J.; Mclaren, C.G.; Quintana, L.; Harnpichitvitaya, D.; Rajatasereekul, S.; Sarawgia, K.; Kumar, A.; Ahmed, H.U.; Singha, K.; Rodriguez, R.; et al. Genotype by environment interactions across diverse rainfed lowland rice environments. Field Crops Res. 1999, 64, 35–50. [Google Scholar] [CrossRef]
  64. Susan, D. The Response to differing selection on plant physiological traits: Evidence for Local Adaptation. Evolution 1996, 50, 103–110. [Google Scholar]
  65. Williams, T.A.; Abberton, M.T.; Rhodes, I. Performance of white clover varieties combined in blends and alone when grown with perennial ryegrass under sheep and cattle grazing. Grass Forage Sci. 2003, 58, 90–93. [Google Scholar] [CrossRef]
  66. Kim, H.M.; Oh, S.H.; Bhandari, G.S.; Kim, C.S.; Park, C.W. DNA barcoding of Orchidaceae in Korea. Mol. Ecol. Resour. 2013, 8, 18–24. [Google Scholar] [CrossRef] [PubMed]
  67. Qiu, Y.X.; Huang, A.J.; Fu, C.X. Studies on genetic diversity in Changium smyrnioides Wolff (Umbelliferae). Acta Phytotaxon. Sin. 2000, 38, 111–120. [Google Scholar]
  68. Qu, M.; Tang, W.; Liu, Q.H.; Wang, D.; Ding, S. Genetic diversity within grouper species and a method for interspecific hybrid identification using DNA barcoding and RYR3 marker. Mol. Phylogenet. Evol. 2017, 121, 46–51. [Google Scholar] [CrossRef]
  69. Mo, Y.H.; Liang, J.; Xie, J.L.; Lin, L.; Li, C.N. A review of sugarcane genetics and breeding based on molecular markers. South China Agric. 2021, 15, 233–234. [Google Scholar]
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Figure 1. Morphological map of eight genotypes of Apocynum spp.
Figure 1. Morphological map of eight genotypes of Apocynum spp.
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Figure 2. Principal component analysis of agronomic traits of different genotypes of Apocynum spp. and Poacynum spp. during the full flowering stage from 2018 to 2019 in Altay. Different colors represent different groups. PH, plant height; SD, stem diameter; IL, internode length; BN, branch number; LDW, leaf dry weight; SDW, stem dry weight; SLR, stem−-to−leaf ratio; ns, not significant; CP, crude protein; NDF, neutral detergent fiber; ADF, acid detergent fiber; Different colored a represents genotypes with the same trend.
Figure 2. Principal component analysis of agronomic traits of different genotypes of Apocynum spp. and Poacynum spp. during the full flowering stage from 2018 to 2019 in Altay. Different colors represent different groups. PH, plant height; SD, stem diameter; IL, internode length; BN, branch number; LDW, leaf dry weight; SDW, stem dry weight; SLR, stem−-to−leaf ratio; ns, not significant; CP, crude protein; NDF, neutral detergent fiber; ADF, acid detergent fiber; Different colored a represents genotypes with the same trend.
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Figure 3. Principal component analysis of agronomic traits of different genotypes of Apocynum spp. and Poacynum spp. during the early flowering stage from 2018 to 2019 in Yuzhong. Different colors represent different groups. PH, plant height; SD, stem diameter; IL, internode length; BN, branch number; LDW, leaf dry weight; SDW, stem dry weight CP, crude protein; NDF, neutral detergent fiber; ADF, acid detergent fiber; EE, ether extract; CF, crude fiber; FLA, flavone. Different colored a represents genotypes with the same trend.
Figure 3. Principal component analysis of agronomic traits of different genotypes of Apocynum spp. and Poacynum spp. during the early flowering stage from 2018 to 2019 in Yuzhong. Different colors represent different groups. PH, plant height; SD, stem diameter; IL, internode length; BN, branch number; LDW, leaf dry weight; SDW, stem dry weight CP, crude protein; NDF, neutral detergent fiber; ADF, acid detergent fiber; EE, ether extract; CF, crude fiber; FLA, flavone. Different colored a represents genotypes with the same trend.
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Figure 4. Neighbor-joining (NJ) tree for different genotypes based on five DNA sequences, ITS, matK, psbA-trnH, rbcL and trnL-F.
Figure 4. Neighbor-joining (NJ) tree for different genotypes based on five DNA sequences, ITS, matK, psbA-trnH, rbcL and trnL-F.
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Table 1. List of evaluated germplasm accessions.
Table 1. List of evaluated germplasm accessions.
Accession NumberSpeciesPhenotype
G1A. venetumRed stems and little flowers
G2A. pictumRed stems and medium-sized flowers
G3Purple spotted medium-sized flowers
G4Thick leaves and medium-sized flowers
G5Slender leaves and medium-sized flowers
G6Green stems and medium-sized flowers
G7Green stems and medium-sized flowers
G8 Green stems and big flowers
Table 2. Mean, range, least significant difference (LSD0.05), genotypic effect (σ2g), year effect (σ2y), genotype-by-year interaction (σ2gy), genotype-by-location interaction (σ2gl), genotype-by-year-by-location interaction (σ2gly), experimental error (σ2Ɛ), variance components and associated standard errors (±SE), estimated from across-year and across-location analyses among the eight Chinese Luobuma germplasm accessions for yield-related traits measured during the early flowering stage in the years 2018 and 2019 in Altay and Yuzhong.
Table 2. Mean, range, least significant difference (LSD0.05), genotypic effect (σ2g), year effect (σ2y), genotype-by-year interaction (σ2gy), genotype-by-location interaction (σ2gl), genotype-by-year-by-location interaction (σ2gly), experimental error (σ2Ɛ), variance components and associated standard errors (±SE), estimated from across-year and across-location analyses among the eight Chinese Luobuma germplasm accessions for yield-related traits measured during the early flowering stage in the years 2018 and 2019 in Altay and Yuzhong.
PH (mm)SD (mm)IL (mm)BNLDW (g)SDW (g)SLR
Mean714.54.337.34.3920.1318.450.86
Range664.1–779.83.9–4.534.5–40.93.75–4.8819.15–21.7717.52–19.090.79–0.96
LSD0.0588.110.261.4111.0320.1213.770.86
σ2g841.23 ± 95.565.57 ± 0.361.87 ± 0.283.63 ± 0.6229.07 ± 14.480.07 ± 0.0010.04 ± 0.005
σ2ynsnsnsnsnsnsns
σ2gy29.05 ± 12.730.06 ± 0.03nsns17.21 ± 8.4921.97 ± 7.94ns
σ2glnsnsnsnsnsnsns
σ2gly46.02 ± 19.500.09 ± 0.040.22 ± 0.070.42 ± 0.20nsns0.0009 ± 0.0003
σ2Ɛ591.57 ± 33.992.92 ± 0.178.09 ± 0.459.78 ± 0.5479.28 ± 5.3177.21 ± 6.220.02 ± 0.002
PH, plant height; SD, stem diameter; IL, internode length; BN, branch number; LDW, leaf dry weight; SDW, stem dry weight; SLR, stem-to-leaf ratio; ns, not significant.
Table 3. The trait mean, range, least significant difference (LSD0.05), genotypic effect (σ2g), year effect (σ2y), genotype-by-year interaction (σ2gy) and experimental error (σ2Ɛ) variance components and associated standard errors (±SE) were estimated for eight Chinese Luobuma accessions for yield-related traits measured at the full flowering stage from 2018 to 2019 at the Altay location.
Table 3. The trait mean, range, least significant difference (LSD0.05), genotypic effect (σ2g), year effect (σ2y), genotype-by-year interaction (σ2gy) and experimental error (σ2Ɛ) variance components and associated standard errors (±SE) were estimated for eight Chinese Luobuma accessions for yield-related traits measured at the full flowering stage from 2018 to 2019 at the Altay location.
PH (mm)SD (mm)IL (mm)BNLDW (g)SDW (g)SLR
Mean685.63.940.35.0814.9015.370.90
Range543.4–827.73.3–4.833.4–43.43.98–5.9910.19–16.4510.00–17.630.74–1.06
LSD0.0588.950.211.0810.3226.8370.810.66
σ2g890.40 ± 77.450.003 ± 0.001ns11.71 ± 1.4480.25 ± 8.17574.91 ± 27.310.04 ± 0.008
σ2yns0.04 ± 0.02nsnsnsnsns
σ2gy44.70 ± 13.860.003 ± 0.00080.23 ± 0.070.92 ± 0.385.17 ± 1.738.97 ± 2.79ns
σ2Ɛ121.03 ± 7.180.01 ± 0.00060.75 ± 0.0410.60 ± 0.6212.68 ± 0.8816.58 ± 1.150.16 ± 0.01
PH, plant height; SD, stem diameter; IL, internode length; BN, branch number; LDW, leaf dry weight; SDW, stem dry weight; SLR, stem-to-leaf ratio; ns, not significant.
Table 4. The nutritional quality trait mean, range, least significant difference (LSD0.05), genotypic effect (σ2g), location effect (σ2l), genotype-by-location interaction (σ2gl), and experimental error (σ2Ɛ) variance components and associated standard errors (±SE) were estimated among eight Chinese Luobuma accessions evaluated at the early flowering stage from 2018 to 2019 in Altay and Yuzhong.
Table 4. The nutritional quality trait mean, range, least significant difference (LSD0.05), genotypic effect (σ2g), location effect (σ2l), genotype-by-location interaction (σ2gl), and experimental error (σ2Ɛ) variance components and associated standard errors (±SE) were estimated among eight Chinese Luobuma accessions evaluated at the early flowering stage from 2018 to 2019 in Altay and Yuzhong.
CP (%)NDF (%)ADF (%)EE (%)CF (%)Ash (%)FLA (mg/100 g)
Mean15.3626.8369.147.4822.1611.282.12
Range12.18–16.7722.88–30.3966.05–73.814.88–13.5418.68–24.739.73–12.091.94–2.36
LSD0.0518.329.0921.2140.087.7116.180.73
σ2g37.79 ± 3.268.05 ± 2.4644.19 ± 11.32174.07 ± 30.560.02 ± 0.0229.57 ± 2.450.03 ± 0.01
σ2lnsnsnsnsnsnsns
σ2gl1.48 ± 0.69nsnsnsnsns0.05 ± 0.02
σ2Ɛ2.01 ± 0.4212.07 ± 2.1343.62 ± 7.5887.69 ± 15.0345.29 ± 8.161.71 ± 0.370.05 ± 0.01
CP, crude protein; NDF, neutral detergent fiber; ADF, acid detergent fiber; EE, ether extract; CF, crude fiber; FLA, flavone; ns, not significant.
Table 5. Phenotypic correlation coefficients among the eight germplasm accessions of Chinese Luobuma based on trait means across the early and full flowering stages and across different years in Altay.
Table 5. Phenotypic correlation coefficients among the eight germplasm accessions of Chinese Luobuma based on trait means across the early and full flowering stages and across different years in Altay.
TraitSDILBNLDWCPNDFADFEECFFLAAsh
PH0.230.560.66 *0.40−0.500.03−0.140.39−0.51−0.86 **0.39
SD 0.38−0.410.63−0.20−0.100.08−0.170.13−0.470.18
IL 0.260.39−0.51−0.64−0.130.31−0.72 *−0.71 *0.53
BN 0.08−0.48−0.09−0.440.03−0.38−0.530.27
LDW 0.68 *0.180.33−0.17−0.23−0.390.63
CP 0.19−0.150.200.310.47−0.93 **
NDF −0.530.180.160.37−0.18
ADF 0.38−0.220.460.41
EE −0.72−0.080.00
CF 0.33−0.42
FLA −0.31
*, ** indicate significance at the 0.05 and 0.01 probability levels, respectively. PH, plant height; SD, stem diameter; IL, internode length; BN, branch number; LDW, leaf dry weight; ns, not significant; CP, crude protein; NDF, neutral detergent fiber; ADF, acid detergent fiber; EE, ether extract; CF, crude fiber; FLA, flavone.
Table 6. Analyses of molecular variance (AMOVAs) for Chinese Luobuma based on five DNA sequences.
Table 6. Analyses of molecular variance (AMOVAs) for Chinese Luobuma based on five DNA sequences.
SequenceSource ofd.f.Sum of SquaresVariance ComponentsPercentage of VariationFst
Variation
Nuclear sequence ITSAmong groups10.450.1233.330.33
Within groups61.430.2466.67
Chloroplast DNA sequences
(matK+psbA-trnH+rbcL+trnlL)		Among groups17.131.2426.570.27
Within groups620.53.4273.43
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Zhao, Y.; Feng, R.; Li, T.; Zulfiqhar, J.M.Z.; Wang, L.; Zhang, J. Genotypic Variation in Agronomic Traits and Molecular Markers among Chinese Luobuma (Apocynum spp.) Germplasm Accessions. Agriculture 2024, 14, 332. https://doi.org/10.3390/agriculture14030332

AMA Style

Zhao Y, Feng R, Li T, Zulfiqhar JMZ, Wang L, Zhang J. Genotypic Variation in Agronomic Traits and Molecular Markers among Chinese Luobuma (Apocynum spp.) Germplasm Accessions. Agriculture. 2024; 14(3):332. https://doi.org/10.3390/agriculture14030332

Chicago/Turabian Style

Zhao, Yufeng, Runqiu Feng, Tao Li, Jahufer Mohamed Zain Zulfiqhar, Li Wang, and Jiyu Zhang. 2024. "Genotypic Variation in Agronomic Traits and Molecular Markers among Chinese Luobuma (Apocynum spp.) Germplasm Accessions" Agriculture 14, no. 3: 332. https://doi.org/10.3390/agriculture14030332

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