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Article

Genetic Diversity Analysis of Monogerm Cytoplasmic Male Sterile and Maintainer Lines of Sugar Beet

by
Pian Chen
1,
Shuyuan Chen
1,
Zhi Pi
2,
Shengnan Li
2 and
Zedong Wu
1,*
1
Academy of Modern Agriculture and Ecological Environment, Heilongjiang University, Harbin 150080, China
2
Key Laboratory of Sugar Beet Genetic Breeding, Heilongjiang University, Harbin 150080, China
*
Author to whom correspondence should be addressed.
Agronomy 2024, 14(10), 2217; https://doi.org/10.3390/agronomy14102217
Submission received: 18 August 2024 / Revised: 23 September 2024 / Accepted: 25 September 2024 / Published: 26 September 2024
(This article belongs to the Section Crop Breeding and Genetics)
Browse Figures
Versions Notes

Abstract

:
Sugar beet is an economically significant crop, and the homozygosity of paired monogerm cytoplasmic male sterile (CMS) and maintainer lines directly influences the number of hybrid combinations that can be created. This study aimed to evaluate the genetic variation within monogerm sugar beet germplasm resources to establish a foundation for advancements in sugar beet breeding and the development of hybrid female parent lines. This study analyzed the genetic diversity of 86 distinct monogerm germplasm resources, including 38 paired monogerm CMS and maintainer lines, 5 individual maintainer lines, and 5 externally introduced sterile lines. The analysis employed 26 pairs of SSR primers and 35 pairs of InDel primers across nine sugar beet chromosomes. Several genetic parameters, and analyses such as structural analysis, genetic diversity analysis, and principal coordinate analysis, were used to evaluate the samples. The results indicated that these strains could be classified into two groups: Group I and Group II. Group I was further divided into three subgroups. Further, 18 pairs of original CMS and maintainer lines were successfully clustered, confirming that their nuclei had achieved homozygosity, making them suitable for use in the development of binary sterile lines. However, 20 other pairs still require further backcrossing to achieve homozygosity. The analysis of molecular variance (AMOVA) revealed that most of the genetic variation occurred within individuals, with relatively low genetic differentiation between groups. Significant genetic differentiation was observed between Subgroups 2 and 3, and between Subgroups 1 and 3. The results suggest that additional monogerm sterile and maintainer lines from these subgroups should be selected to configure binary sterile lines. This study offers a theoretical foundation for developing new sugar beet germplasm resources and cultivating hybrid mother plants.

1. Introduction

Sugar beet (Beta vulgaris L.) is a biennial herbaceous plant from the Amaranthaceae family and the Beta genus [1]. It is the second-largest source of sugar globally after sugarcane [2]. With an annual global planting area of 7.8–8.5 million hectares since the 20th century, sugar beet is an economically important crop. In China, sugar beet accounts for about 13% of the total sugar production, whereas, globally, it accounts for about 35% [3]. Outside of sugar production, sugar beet has research value due to its by-products, such as pectin extracted from beet pulp [4,5], which is used in food stabilization through emulsification. Additionally, sugar beet pectin has applications as a functional food and animal feed [6].
Sugar beet is a biennial, cross-pollinated crop that predominantly exhibits self-incompatibility. The varieties commonly used in production are typically developed from monogerm binary sterile lines as maternal parents [7,8]. China has fewer hybrid combinations of sugar beet compared with foreign countries mainly because of the limited number of monogerm maintainer and sterile lines available for configuring these hybrids [9]. Half of the increase in sugar beet root yield and sugar content has been attributed largely to advances in breeding [10]. To maximize the benefits of hybrid breeding, selecting hybrid mother plants through targeted breeding programs is crucial [11]. Investigating the genetic diversity of sugar beet germplasm resources is crucial for understanding the genetic characteristics and relationships among different lines. This enables a more accurate selection of parent plants, reduces randomness in hybrid configurations, and improves the efficiency of hybrid breeding [12,13,14,15].
Traditionally, maternal offspring were selected based on field traits, with traditional breeding methods used to transfer alleles between plants, a process that typically required 5–10 generations of backcrossing [16]. However, molecular marker technology offers a faster, simpler, environmentally friendly, and more reliable method for detecting genetic diversity [17] and has been widely applied in population genetics research [18]. SSR markers are commonly used in crop genetic diversity studies because of their multiallelic nature, high polymorphism, strong stability, and reproducibility [19,20,21,22]. InDel markers exhibit co-dominance, and their polymorphism often affects gene function. Diversity has been identified in crops such as durian [23] and bitter gourd [24]. In a study on Japanese sugar beet, Taguchi et al. used 33 CAPS and 38 SSR markers to analyze the genetic diversity of an excellent Japanese sugar beet inbred line consisting of 63 lines [25]. Nam Ngoc Nguyen et al. also established a fingerprint map of 53 Eastern melon varieties using 43 SSR core markers [26]. These molecular marker technologies have proven successful in analyzing plant genetic diversity, population structure, and assisted breeding [27].
Genetic diversity plays a crucial role in breeding, gene function research, and the development of new varieties with excellent traits [28]. The maternal parent of monogerm sugar beet varieties is a monogerm binary sterile line. More homozygous monogerm sterile and maintainer lines with different genetic foundations are needed to obtain more monogerm binary sterile lines. Many of the monogerm cytoplasmic male sterile (CMS) and maintainer lines used in this study were improved through multi-embryo maintainer lines. This study used SSR and InDel primers across nine sugar beet chromosomes to identify 38 pairs of sugar beet monogerm maintainer and sterile lines, 5 monogerm maintainer lines, and 5 externally introduced sterile lines. The objective of this study was twofold: first, to assess the nuclear homozygosity of paired sterile and maintainer lines to determine whether further backcrossing is necessary. Second, the genetic diversity of these monogerm sterile and maintainer lines was analyzed to identify those with larger genetic distances, laying the foundation for the future configuration of binary sterile lines.

2. Materials and Methods

2.1. Test Materials

2.1.1. Sugar Beet Materials

The study materials comprised 81 CMS and maintainer lines provided by the Core Laboratory of Beet Genetics and Breeding at Heilongjiang University, 3 sterile lines from the Jilin Academy of Agricultural Sciences, and 2 sterile lines from Xinjiang, totaling 86 sugar beet germplasms. The experimental materials were planted at the Sugar Beet Research Institute’s experimental base located in Hulan District, Harbin City, Heilongjiang Province. Detailed information on the test materials is presented in Table 1. In the sugar beet monogerm resources, C represented the sterile line and O represented the maintainer line. For example, WC1 and WO1 were paired sterile and maintainer lines, respectively.

2.1.2. Primers

The SSR and InDel primers used in this study were designed by the Sugar Beet Molecular Genetics Laboratory of Heilongjiang University based on the sugar beet whole-genome sequence. A total of 26 SSR primer pairs and 35 InDel primer pairs with significant polymorphism and stable amplification across nine sugar beet chromosomes were selected. These primers were synthesized by Shanghai Shenggong Bioengineering Co., Ltd. (Shanghai, China). The primer sequences and their chromosomal positions are listed in Table 2 and Table 3, respectively.

2.2. Test Method

2.2.1. Extraction of Sugar Beet Genomic DNA

Two to three fresh, tender sugar beet leaves (weighing 20–30 mg) were collected, and genomic DNA was extracted using the CTAB method [29]. A NanoDrop 2000/2000c ultramicro ultraviolet-visible spectrophotometer was used to measure DNA purity and concentration (Thermo Fisher, Waltham, MA, USA). For polymerase chain reaction (PCR) amplification, 10 μL of each DNA sample was diluted with ddH2O to a concentration of 10 ng/μL and stored at 4 °C for experimental use. The remaining DNA stock solution was stored in a freezer at −20 °C.

2.2.2. Polymerase Chain Reaction Amplification Program and System

Primer length and concentration influence the annealing temperature, and amplified bands are produced only at the optimal annealing temperature [30]. Distinct PCR protocols were employed for each primer to enhance PCR specificity and minimize nonspecific binding.
Standard PCR amplification was performed as follows: pre-denaturation at 94 °C for 3 min, denaturation at 95 °C for 15 s, annealing for 15 s, and extension at 72 °C for 15 s, for a total of 35 cycles, followed by a final extension at 72 °C for 5 min.
The touchdown program involved pre-denaturation at 94 °C for 3 min, denaturation at 94 °C for 15 s, annealing at 65 °C for 15 s (followed by two cycles per degree decrease from 65 °C to 56 °C), and extension at 72 °C for 30 s; denaturation at 94 °C for 15 s, annealing at 55 °C for 15 s, and extension at 72 °C for 30 s, for a total of 20 cycles, concluding with a final extension at 72 °C for 5 min.
The PCR amplification system used 5 μL of the total reaction volume, including 1 μL of the DNA template, 0.2 μL of upstream and downstream primers, 2.5 μL of supermix, and 1.1 μL of ddH2O.

2.2.3. PCR Product Detection and Data Analysis

After completing PCR amplification, 1.5 μL of the amplified product was analyzed using electrophoresis on an 8% non-denaturing polyacrylamide gel at 180 V for 1.5 h. GelRed nucleic acid dye was used for efficient and sensitive detection of DNA through gel staining, followed by observation, imaging, and gel analysis.
A 50 bp DNA marker was used for comparison, and bands were scored manually. Visible bands were marked as “1”, absent bands as “0”, and strains with no visible bands as “9”. A binary data table was created using the binary system. The genetic diversity indicators were calculated using Popgene 1.32 software [31], including the number of alleles (NAs) [32], the effective number of alleles (NEs) [32], observed heterozygosity (Ho) [33], expected heterozygosity (He) [33], and Shannon’s information index (I) [34]. PowerMarker 3.25 [35] was used to calculate the polymorphism information content (PIC) [36] and gene diversity among different primers. The population structure of the 86 sugar beet germplasms was analyzed using Structure 2.3.4 software [37]. The analysis was performed with a burn-in period of 10,000 and an MCMC value of 100,000, with K set from 1 to 10. GenAIEx 6.5 software [38] was used for variance and principal coordinate analysis (PCoA) of the germplasms. A cluster tree diagram of the 86 sugar beet germplasms was generated using Nei’s genetic distance [39] and the arithmetic mean (UPGMA) unweighted algorithm implemented using MEGA 7 software [40].

3. Results

3.1. Genetic Diversity Analysis of SSR and InDel Primers

A total of 26 pairs of SSR primers were used to evaluate the polymorphism of 86 sugar beet CMS and maintainer lines. A total of 84 alleles were identified across the primer sets. The NAs per primer pair ranged from 2 to 6, with an average of 2.923 alleles per primer (Table 4). The NEs varied from 1.047 to 5.342, with an average of 2.090. Shannon’s information index (I) ranged from 0.123 to 1.728, with an average value of 0.765. The observed heterozygosity (Ho) varied from 0.045 to 0.817, with an average of 0.465. The expected heterozygosity (He) ranged from 0.045 to 0.812, with an average of 0.455. The gene diversity index ranged from 0.046 to 0.824, with an average of 0.493. PIC ranged from 0.045 to 0.801, with an average of 0.437.
A total of 35 pairs of InDel primers were used to evaluate the polymorphism of 86 sugar beet CMS and maintainer lines, identifying 89 alleles in total. The NAs per primer pair ranged from 2 to 4, with an average of 2.542 alleles (Table 5). The NEs ranged from 1.230 to 3.621, with an average of 1.878. Shannon’s information index (I) ranged from 0.404 to 1.333, with an average of 0.689. The observed heterozygosity (Ho) ranged from 0.324 to 0.811, with an average of 0.565. The expected heterozygosity (He) ranged from 0.188 to 0.728, with an average of 0.434. The gene diversity index ranged from 0.111 to 0.728, with an average of 0.473. The PIC ranged from 0.108 to 0.688, with an average of 0.409.

3.2. Group Structure Analysis

The genetic structure of the 86 sugar beet CMS and maintainer lines was analyzed using 26 SSR primers and 35 InDel primers with Structure 3.2.4 software, revealing a significant ΔK peak at K = 2 (Figure 1). This divided the 86 sugar beet CMS and maintainer lines into two groups: Group I and Group II. Group I consisted of 33 germplasms, including 32 monogerm sterile and maintainer lines from the Core Laboratory of Beet Genetics and Breeding at Heilongjiang University and 1 sugar beet embryo line from Xinjiang. Group II comprised 44 germplasms, including 40 monogerm sterile and maintainer lines from the Core Laboratory of Beet Genetics and Breeding at Heilongjiang University, 3 monogerm sterile lines from the Jilin Academy of Agricultural Sciences, and 1 monogerm sterile line from Xinjiang (Figure 2).

3.3. Cluster Analysis of Sugar Beet CMS and Maintainer Lines

The cluster analysis using the UPGMA method (Figure 3 and Figure 4) showed that the 86 sugar beet lines could be grouped into two main groups using SSR and InDel markers. The majority of germplasms, except for germplasms 25 (WC17) and 69 (T14), were categorized into two main groups: Group I and Group II. Group I was further divided into three subgroups: Subgroup 1, Subgroup 2, and Subgroup 3.
The genetic distance (Supplementary Materials) between the 86 lines, calculated using MEGA 7 software, ranged from 0.134 to 0.847. Germplasms 78 and 79 (Dy5 CMS and Dy5 O) exhibited the lowest genetic distance of 0.134, whereas germplasms 42 and 70 (WO35 and T15) had the highest genetic distance of 0.847. Further, 18 pairs of original sugar beet CMS and maintainer lines were clustered, with 4 pairs in Subgroup 1, 1 pair in Subgroup 2, and 9 pairs in Subgroup 3. Group II contained four additional pairs of original sugar beet CMS and maintainer lines, Dy5 CMS and Dy5 O, WC19 and WO19, WC37 and WO37, and WC39 and WO39, with genetic distances of 0.134, 0.192, 0.221, and 0.281, respectively.
Based on Nei’s genetic distance, the genetic distance between strains 30 (WO20) and 7 (WC8) and 71 (T3) was 0.833 and 0.841, respectively. The genetic distance between strain 42 (WO35) and strains 55 (2B9) and 1 (WC1) was 0.838 and 0.813, respectively. The genetic distance between strains 22 (WO15) and 55 (2B9) was 0.815. The genetic distance between strains 29 (WC20) and 86 (S2) was 0.816. The genetic distance between strains 9 (WC9) and 56 (2B10) was 0.803. These sterile lines with large genetic distances and their heterozygous maintainer lines can be used to construct different binary sterile lines.

3.4. Principal Coordinate Analysis

The PCoA on sugar beet germplasms was conducted using the GeneAlex 6.5 software, based on Nei’s genetic distance. The first two principal coordinates accounted for 9.01% and 16.39% of the variance, respectively (Figure 5). The analysis showed a clear separation between Group I and Group II, with distinct subgroups visible within Group I. Subgroup 1 (gray) was the farthest from Subgroups 2 (yellow) and 3 (blue). Subgroup 2 was clearly separated from Subgroup 3. Subgroups 2 and 3 were closer to each other, exhibiting small genetic distances. The results of the PCoA were consistent with both the cluster and structure analyses.

3.5. AMOVA of 86 Sugar Beet Monogerm Sterile and Maintainer Lines

The genetic diversity of sugar beet monogerm germplasm resources was further evaluated using AMOVA. These results indicated that the genetic differentiation between these two groups was relatively small, with a high level of gene flow. This might be because these strains were all improved through multi-embryo sugar beet germplasms. Gene exchange between groups was relatively frequent, limiting their genetic differentiation. AMOVA revealed that 2% of genetic variation was between groups, whereas 98% was within individuals (Table 6). This suggests that the genetic variations among the 86 sugar beet germplasms mainly originated within individual plants. Most heterozygous plant species exhibited significant genetic variations within groups, possibly due to pollen transfer between cultivated germplasms.
The genetic differentiation among the three subgroups of Group I was analyzed using AMOVA. The results indicated that the inter-subgroup variation was 4%, the inter-individual genetic variation was 96%, the FST value was 0.052, and the gene flow was 4.567. The FST values between subgroups ranged from 0.063 to 0.185 (0.030 between Subgroups 1 and 2, 0.056 between Subgroups 1 and 3, and 0.057 between Subgroups 2 and 3). This indicated a high level of genetic differentiation between Subgroups 2 and 3, as well as between Subgroups 1 and 3, and a moderate level of genetic differentiation between Subgroups 1 and 2. Gene flow among the three subgroups ranged from 4.139 to 8.184 (8.184 between Subgroups 1 and 2, 4.178 between Subgroups 1 and 3, and 4.139 between Subgroups 2 and 3). These results indicated a high level of gene flow between these subgroups, which might be due to artificial breeding practices and potential connections among the parent strains (Table 7).

4. Discussion

A comprehensive evaluation of the genetic structure of sugar beet germplasm is essential for the effective use of plant genetic resources. Molecular marker analysis is considered the most reliable method for characterizing these genetic resources, as it allows for the differentiation of germplasms at the DNA level. This study selected 61 pairs of SSR and InDel primers from the entire sugar beet genome to act on nine sugar beet chromosomes. The NEs for the 26 pairs of SSR primers ranged from 2 to 6, with an average of 2.923 alleles. Similarly, the NEs for the 35 pairs of InDel primers ranged from 2 to 4, with an average of 2.542 alleles. These findings were consistent with the results of Fugate et al. [41] and Amangeldiyeva et al. [42], but lower than the results of Desplanque et al. [43] and Fei Peng et al. [30]. The higher NAs observed in these studies can be attributed to their examination of a wider range of wild sugar beet varieties. In addition, only 19.6% of the primers in the present study showed an expected heterozygosity (He) that was higher than the observed heterozygosity (Ho), indicating a high degree of heterozygosity in the cultivated sugar beet lines. This suggested that most of these lines still required further self-purification. The average PIC value was 0.437 and 0.409 for SSR and InDel markers, respectively, indicating a high degree of polymorphism and genetic diversity within the population. SSR primers exhibited significant amplification and higher polymorphism, providing good stability. However, InDel primers produced clearer and more accurate bands, reducing errors caused by specificity and complexity. Combining these two types of markers could improve the effectiveness of the study.
The population structure, phylogenetic, and principal component analyses indicated that, except for strains 25 and 69, the 86 germplasms could be classified into two major groups, Group I and Group II, with Group I further divided into three subgroups (Subgroups 1, 2, and 3). The results suggested that the sugar beet strains introduced from Xinjiang and Jilin were not significantly different from the existing germplasms. This could be due to the limited number of germplasms in the collection and the repeated use of parental lines with the same genetic background in breeding programs [44]. The AMOVA results showed minimal genetic differentiation between sugar beet groups (2%), with the main genetic variation occurring within individual plants (98%), which agreed with the findings of Abbasi et al. [45], Ksenija et al. [46], and De Riek et al. [47]. Over the years, breeding programs have selected parents with genetically distant backgrounds from different gene banks, leading to increased genetic variations within individual sugar beet plants [47]. However, the gene flow between Subgroups 1 and 3, and between Subgroups 2 and 3, was limited, restricting the choice of parents between these subgroups. The cluster analysis of sugar beet germplasms revealed two major groups (Figure 3), with 18 pairs of original sugar beet CMS and maintainer lines clustering together. This indicated that their nuclear genomes were almost homozygous after backcrossing, whereas the remaining 20 pairs exhibited larger genetic distances and required further backcrossing. Research has shown that most of the beet affinities are related to geographical location [48]. A sugar beet germplasm from another country needs to be introduced to cultivate new sugar beet strains. Further studies should focus on various traits related to sugar beet breeding, such as yield, disease resistance, and sugar content. These traits can be targeted in breeding programs to develop new varieties that are better adapted to changing environmental conditions and consumer demands.

5. Conclusions

This study comprehensively and effectively evaluated the genetic diversity of 86 sugar beet CMS and maintainer lines cultivated in China, using 61 pairs of SSR and InDel molecular markers. The genetic diversity analysis showed that these 86 sugar beet varieties could be classified into two major groups: Group I and Group II. Group I was further divided into three subgroups (Subgroups 1, 2, and 3). The genetic differentiation mainly occurred within individual plants, and strains between Subgroups 1 and 3, as well as between Subgroups 2 and 3, were more suitable as parental lines for future breeding programs. The cluster analysis showed that 18 pairs of original monogerm CMS and maintainer lines backcrossed to become homozygous. This study provided a theoretical basis for germplasm innovation. Selecting strains with distant genetic relationships for hybridization could improve breeding efficiency and lead to the development of new sugar beet varieties with desirable traits. Further introducing high-quality sugar beet germplasm from various countries will help broaden the genetic base of species, supporting the continued development and utilization of sugar beet resources globally. Overall, this study contributes valuable insights into the genetic diversity of monogerm CMS and maintainer lines, offering guidance for future breeding programs aimed at improving sugar beet varieties.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/agronomy14102217/s1, Table S1: Genetic distance of 86 beet germplasm.

Author Contributions

Conceptualization, Z.W. and P.C.; methodology, Z.W.; software, P.C.; validation, Z.W., Z.P. and S.L.; formal analysis, P.C. and S.C.; investigation, P.C. and S.C.; resources, Z.W.; data curation, P.C.; writing—original draft preparation, P.C.; writing—review and editing, Z.W., Z.P. and S.L.; visualization, P.C.; supervision, Z.W.; project administration, Z.W.; funding acquisition, Z.W. All authors have read and agreed to the published version of the manuscript.

Funding

Supported by the earmarked fund for CARS, 170111.

Data Availability Statement

All data generated or analyzed during this study are included in this published article and its Supplementary Information files.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Genetic structure of 86 sugar beet germplasms. The highest ΔK was obtained at K = 2, indicating that the population could be divided into two groups: Group I and Group II.
Figure 1. Genetic structure of 86 sugar beet germplasms. The highest ΔK was obtained at K = 2, indicating that the population could be divided into two groups: Group I and Group II.
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Figure 2. Population genetic structure of 86 sugar beet germplasms at K = 2, Red represents Group I and green represents Group II.
Figure 2. Population genetic structure of 86 sugar beet germplasms at K = 2, Red represents Group I and green represents Group II.
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Figure 3. Cluster analysis of 86 sugar beet monogerm sterile and maintainer lines. Red represents Group I, and black represents Group II.
Figure 3. Cluster analysis of 86 sugar beet monogerm sterile and maintainer lines. Red represents Group I, and black represents Group II.
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Figure 4. Diagram depicting Subgroup 1, Subgroup 2, and Subgroup 3. Blue represents Subgroup 1, gray represents Subgroup 2, and yellow represents Subgroup 3.
Figure 4. Diagram depicting Subgroup 1, Subgroup 2, and Subgroup 3. Blue represents Subgroup 1, gray represents Subgroup 2, and yellow represents Subgroup 3.
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Figure 5. Principal coordinate analysis of 86 sugar beet germplasms. Red represents Group II, Blue represents Subgroup 1, gray represents Subgroup 2, and yellow represents Subgroup 3.
Figure 5. Principal coordinate analysis of 86 sugar beet germplasms. Red represents Group II, Blue represents Subgroup 1, gray represents Subgroup 2, and yellow represents Subgroup 3.
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Table 1. Numbers and names of 86 single-embryo strains of sugar beet.
Table 1. Numbers and names of 86 single-embryo strains of sugar beet.
No.Resource NameNo.Resource NameNo.Resource Name
1WC130WO20592D5
2WO131WC21602D6
3WC332WO21612A1
4WO3332D7622A2
5WC7342D8632A3
6WO735WC25642A4
7WC836WO25652B13
8WO837WC31662B14
9WC938WO3167T20
10WO939WC3268T21
11WC1040WO3269T14
12WO1041WC3570T15
13WC1142WO3571T3
14WO1143WC3672T2
15WC1244WO3673WO5
16WO1245WC3774WO28
17WC1346WO3775WO34
18WO1347WC3976WO33
19WC1448WO3977WO38
20WO1449WC4078Dy5 CMS
21WC1550WO4079Dy5 O
22WO1551WC4180Dy20CMS
23WC1652WO4181Dy20 O
24WO16532B582JL1-CMS
25WC17542B683JL2-CMS
26WO17552B984JL3-CMS
27WC19562B1085S1
28WO19572B1586S2
29WC20582B16
Table 2. Twenty-six pairs of SSR primer sequences and annealing temperature.
Table 2. Twenty-six pairs of SSR primer sequences and annealing temperature.
No.LocusChromosomal PositionPositive SequenceBackward SequenceAnnealing Temperature
1L7Chr1TCCATTTCCAACAACAGCAACCAAAGCCAGGAAAGTTGAA57 °C
2A01Chr1TTCCATGGGAAGTGTCGCTGCAAGAGAATGAGAACGGATD
3A02Chr1CCTTCTTACATCTCCAACCCTTGGTGGTTGTTGTGGTGGTD
414118Chr2AAGTCTAACACCAGAATCCAGAAACCAGAGAGAATATGAGGATGTD
5B06Chr2TGGAGAAAATTGAGAGTGTGGTCCTTCATCGTCTTCCTTTCATD
611965Chr3TTGAGTATTTTCGTCGGCCATCTACATCAGTTTTCCCTTC57 °C
726391Chr3CAGAATACACTTGGTGAGATGATACTATGTTGTTGCTGCTGTG57 °C
8L16Chr3GTTGAATCAGGTAATGCGGGTTTCTCCCCGTGAAGATGAC57 °C
9A05Chr4AATGAGCTTGAGGCGTCGAAAGAAAGGGGAAAGGGGATD
10A06Chr4TTGTTGTTGGTGCAACGGACACCAAAATTGCGGGAATD
11A07Chr4GGGCCCTAACCCTAACCACAGGGGAAGCAAATTCCATD
12L37Chr5TCCATGAATTCTCCGACGAGGAGGAGAAATGGAGAAAAGG57 °C
132305Chr5TACTAAAACCCTACGAACTCCATACACCTGTGATTGTCAGAAGA57 °C
1457236Chr5TTGGAGAGAGAAAAGAGAGAAGATCCCTTGACAGTAGAACTCC57 °C
15W21Chr6GTGAGTATTCGGGAGATGGCGAAGCAAAAGCAATGGAAAATD
16TC94Chr6GAAGAAGCCGAGGAGAGAGACCCGTAAGAAGCGAACTCTG57 °C
17L48Chr6TGTTGCCTTGACTGTTGCTCGAGGGGAAGTGGGAAAGAAGTD
18A18Chr7AACCCTAACCCCACCACCTGGTTGGGGAAATGAACGTD
19A19Chr7ACTTTGCCTTTGCAATCCAGACGGCGGTAGGAGGAGTTD
20B34Chr7CAAAGCCACAACAAAAGTGACCAAACTTGCAATTGTGCTTTD
21TC46Chr8GATCCGAGGAAACAAGGGATGCCACGACCAAAATCTCAGTTD
22L70Chr8GCTGATGATCTTGTGGAGCATTGGTTTAGGCTGGAATTGGTD
2386067Chr8CTTTAGTGTAGCGTTAGAGCGTAACAGCAGGACTGGAGAAGTD
24A27Chr9TACGATGAGTGCCTGCGATTTTGGTGGGGAGGGGTD
25A28Chr9CGGTCAAGGAAGCTACGGCACGAACCATTTCCCCTGTD
26A29Chr9CGATGGGTAGGAGGAGGAAGGAAGAGGAAGAGGAAGAGGATD
TD: touchdown.
Table 3. Thirty-five pairs of InDel primer sequences and annealing temperature.
Table 3. Thirty-five pairs of InDel primer sequences and annealing temperature.
No.LocusChromosomal PositionPositive SequenceBackward SequenceAnnealing Temperature
1ND18Chr1TTGCCTTTGCATCTTCTTTTCGGGGCCTTTAATAACTTTTCCTD
2ND109Chr1AGCCTAGCAGGGATGGGTCCTTACCAAAAGGTTCTGCAATD
3ND113Chr1CTTCAGCTTGCAGTCACCAGTGGATGGTTCAGGGAGGATD
4ND19Chr1GCATTCGTCCAAGTAAAGGGTGGTGGTAGAGCCTTCAGGATD
5ND31Chr2CGAGGATCAAGATCCCACATTTTTGACGGGCTAGCTACTTTD
6ND29Chr2TTTTTCGTGCGATATGCCTGGATCACCCAAAATCAATAGTD
7ND33Chr2GCTGCAATAGGCGATTCGTGCAAGGTGACAACCACCATD
8ND34Chr2TGGTGGTTGTCACCTTGCGGAACAAGCTATTGGGAATTTTD
9ND47Chr3CGATTAGATTTCTCTGCTGGCGGGGCTTCAGCCAGAACTTD
10ND220Chr3CAGTCCAACAACACCACACCCGCATAAGAATCTGCTGGGTTD
11ND129Chr3AACCCGACTCTATCCAGCATCCCCACCTTCATACATGGTD
12ND52Chr4CCTTCATGAGTTCCGGCTCGCAACGTGCACTTACTTTCTD
13ND139Chr4TGGTCGACGATCAGGGATCAAACCTCTCCACCCACGTD
14ND141Chr4TCAATTCCAGCCTCACAAAAAATTCGTACTGGGGTTTTGAATD
15ND142Chr4TGCCAACTAAGTCCTTAGCCATGCACAGTTGCACACACGTD
16ND63Chr5CTTCAGCTTGCAGTCACCAGTGGATGGTTCAGGGAGGATD
17ND247Chr5TGGCTGCTAAAGGGATGGCACGGAAAAGATCTTGCACATD
18ND65Chr5GGCCCATGCTCATTGTCTTTGAAGAGGGTCTCTCACCTGTD
19ND66Chr5TTGTTGAGCTCACGATATGCGGGATGGTGGTTTTGTGGTD
20ND73Chr6CTTGCCAAACAACCCATCTTTTCTTGAATCCTAAACCCTCCTD
21ND75Chr6TTGTGCTCTCTGCATAACGAGTGTCAAGGTGAGGAAGAAGAATD
22ND249Chr6GAAAATTGCTGAAACTGCAACATTCCACCCAACCCCTTCTD
23ND251Chr6TCAAAATTGAAATTGTTGCCTCTAACCACCTTGCCAGCGTD
24ND173Chr7GGGTCTATCACATGATGCCAGGATAGCCCTTAAGCTTTTGATD
25ND262Chr7CTTCAGGTTTTGCTGTGCCTCCCATTACGCAAAAGTCGTD
26ND264Chr7GTCCCCCATGTGTTGCATAGCGGAAAAATTCGGACCTD
27ND267Chr7GCTTGCTCACGAGCTTCCACTGTTCGAATCCGACGCTD
28ND272Chr8CGCAATTTTTCAAACCCAACATTGAGCCGCAAGGACTTD
29ND274Chr8CTTGAGGTTGTGGGTTTGAATTGAGACAAATGGACTCCTCATD
30ND276Chr8CCCTCTTGGGTAGTTGAGCACCACTGAGATCAGCATCAACATD
31ND286Chr8ATCATGGAGGCTCACCCAGCTACCCTCGGATTGCATTD
32ND99Chr9AATGAACGGCTTTAGCACAAAACCTGGTTTCCGCTGTTGTD
33ND285Chr9GGTGGCTTCTTTGGCACAGCAATTTCGAGCAAAAATCCTTD
34ND283Chr9TGCCAAGAATGGTCGTCATCTGAGCTTAGGCTCCATCTTTD
35ND284Chr9GCAGCCAGCAGAAGGAGACCCATGTTCGGTGGTTGTTD
Table 4. Genetic diversity assessed using 26 pairs of SSR primers across the 86 sugar beet germplasms.
Table 4. Genetic diversity assessed using 26 pairs of SSR primers across the 86 sugar beet germplasms.
LocusNaNeIHoHeGenetic
Diversity		PIC
L72.0001.7320.6130.4250.4220.4490.367
A012.0001.8320.6460.4570.4540.4540.351
A023.0001.0470.1230.0450.0450.0460.045
141183.0002.6821.0400.6310.6270.6440.578
B063.0001.5680.5730.3640.3620.3860.318
119656.0005.3421.7280.8170.8120.8240.801
263912.0001.7300.6130.4240.4220.5350.469
L162.0001.4830.5070.3280.3260.4370.391
A052.0002.0000.6930.5070.5000.5470.488
A063.0001.8910.8270.4740.4710.4840.439
A073.0001.3310.4850.2500.2480.3150.297
L373.0002.4760.9790.6000.5960.6650.606
23052.0001.7510.6200.4310.4290.4420.354
572363.0002.0910.8020.5300.5220.5400.485
W214.0001.9720.8600.4960.4930.5160.454
TC942.0001.9980.6920.5020.4990.5530.452
L483.0001.4840.6170.3280.3260.3570.334
A185.0003.4381.3440.7130.7090.7160.665
A195.0002.7331.2860.6370.6340.6430.609
B342.0001.8440.6500.4600.4570.4580.353
TC462.0001.4870.5090.3300.3270.4720.425
L703.0002.7351.0540.6430.6390.6480.575
860674.0003.2331.2670.6940.6900.6910.637
A272.0002.0000.6930.6660.5000.0460.045
A282.0001.1280.2280.1140.1130.4240.365
A293.0001.2800.4470.2200.2180.5390.461
MEAN2.9232.0900.7650.4650.4550.4930.437
Na—number of alleles; Ne—effective number of alleles; I—Shannon’s information index; Ho—observed heterozygosity; He—expected heterozygosity.
Table 5. Genetic diversity evaluated using 35 pairs of InDel primers across the 86 sugar beet germplasms.
Table 5. Genetic diversity evaluated using 35 pairs of InDel primers across the 86 sugar beet germplasms.
LocusNaNeIHoHeGenetic
Diversity		PIC
ND182.0001.4090.4660.7050.2950.5230.441
ND1092.0001.9960.6920.4970.5020.6470.571
ND1132.0001.3280.4130.7510.2480.2970.269
ND192.0001.9950.6920.4980.5010.4980.374
ND314.0003.4941.3190.2790.7210.7280.688
ND293.0001.6960.7330.5860.4130.4850.449
ND333.0001.4050.5070.7070.2920.5300.446
ND344.0001.7310.7530.5740.4250.4950.449
ND472.0001.9860.6890.5000.4990.5190.406
ND2202.0001.4120.4670.7030.2960.4980.423
ND1292.0001.3180.4050.7560.2430.5530.471
ND524.0002.3880.9830.4150.5840.5900.511
ND1392.0001.8600.6550.5340.4650.4860.388
ND1413.0002.3560.9330.4200.5790.5750.485
ND1423.0002.1790.9230.4550.5440.5410.483
ND632.0001.9140.6700.5180.4810.6130.538
ND2472.0001.7150.6070.5800.4190.4170.330
ND652.0001.4340.4800.6950.3040.3020.257
ND663.0002.0130.8620.4930.5060.5030.446
ND733.0001.4050.5590.7090.2900.3040.286
ND752.0001.4240.4740.7000.3000.3140.271
ND2492.0001.3160.4040.7580.2410.2400.211
ND2513.0001.4320.5610.6960.3030.3170.291
ND1732.0001.7100.6050.5820.4170.4280.346
ND2622.0001.8680.6290.5590.4400.4500.359
ND2642.0001.3990.4600.7120.2870.3170.280
ND2672.0001.9960.6920.4950.5040.6520.578
ND2724.0003.6211.3330.2710.7280.7230.673
ND2742.0001.9960.6920.4970.5020.6020.518
ND2763.0002.0700.7820.4790.5200.5280.421
ND2862.0001.2300.3350.8110.1880.1870.169
ND992.0002.0000.6930.4440.5550.1110.108
ND2854.0003.0481.1850.3240.6750.6710.606
ND2832.0001.8840.6620.5270.4720.4810.376
ND2843.0001.7890.7840.5560.4430.4410.399
MEAN2.5421.8780.6890.5650.4340.4730.409
Table 6. AMOVA of 86 sugar beet monogerm sterile and maintainer lines.
Table 6. AMOVA of 86 sugar beet monogerm sterile and maintainer lines.
SchemedfSSMSEst. Var.%
Among Groups245.97022.9850.3812%
Among Individuals84954.19111.3590.0000%
Within Individual871653.00019.00019.00098%
Total1732653.161 19.381100%
df, degrees of freedom; SS, sum of squares; Est. Var., estimated variance.
Table 7. AMOVA among the three subgroups.
Table 7. AMOVA among the three subgroups.
SourcedfSSMSEst. Var.%
Among Subgroups288.82444.4120.8094%
Among Individuals65680.17610.4640.0000%
Within Individual681298.00019.08819.08896%
Total1352067.000 19.897100%
df, degrees of freedom; SS, sum of squares; Est. Var., estimated variance.
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Chen, P.; Chen, S.; Pi, Z.; Li, S.; Wu, Z. Genetic Diversity Analysis of Monogerm Cytoplasmic Male Sterile and Maintainer Lines of Sugar Beet. Agronomy 2024, 14, 2217. https://doi.org/10.3390/agronomy14102217

AMA Style

Chen P, Chen S, Pi Z, Li S, Wu Z. Genetic Diversity Analysis of Monogerm Cytoplasmic Male Sterile and Maintainer Lines of Sugar Beet. Agronomy. 2024; 14(10):2217. https://doi.org/10.3390/agronomy14102217

Chicago/Turabian Style

Chen, Pian, Shuyuan Chen, Zhi Pi, Shengnan Li, and Zedong Wu. 2024. "Genetic Diversity Analysis of Monogerm Cytoplasmic Male Sterile and Maintainer Lines of Sugar Beet" Agronomy 14, no. 10: 2217. https://doi.org/10.3390/agronomy14102217

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