Exploring Sampling Strategies and Genetic Diversity Analysis of Red Beet Germplasm Resources Using SSR Markers

Abstract

By using 14 SSR primer pairs, we here analyzed and compared the amplification results of 534 DNA samples from six red sugar beet germplasm resources under three treatments. These data were used to explore the sampling strategy for the aforementioned resources. Based on the sampling strategy results, 21 SSR primer pairs were used to evaluate the genetic diversity of 47 red sugar beet germplasm resources. The six population genetic parameters used for exploring the sampling strategy unveiled that individual plants within the population had a relatively large genetic distance. The genetic parameters Ne, I, and Nei’s of the randomly mixed sampling samples increased rapidly between 10 and 30 plants before decreasing. Therefore, when SSR technology was used to analyze the genetic diversity of the red sugar beet germplasm resources, the optimal sampling gradient for each population was the adoption of a random single-plant mixed sampling sample of no less than 10 plants and no more than 30 plants. The 21 SSR primer pairs were used to detect genetic diversity in 30 random mixed samples of 47 resources. The average polymorphic information content (PIC) was 0.5738, the average number of observed alleles (Na) was 4.1905, the average number of effective alleles (Ne) was 2.8962, the average Shannon’s information index (I) was 1.1299, the average expected heterozygosity (Nei’s) was 0.6127, and the average expected heterozygosity (He) was 0.6127. The genetic distance of the 47 germplasm resources ranged from 0.0225 to 0.551 (average: 0.316). According to the population structure analysis, the most suitable K value was six, which indicated the presence of six populations. Based on the clustering analysis results, the 47 germplasm resources were segregated into six groups, with obvious clustering and some germplasm resources noted for having groups with close genetic relationships. We here established a more accurate and scientific sampling strategy for analyzing the genetic diversity of red sugar beet germplasm resources by using SSR molecular markers. The findings provide a reference for collecting and preserving red sugar beet germplasms and protecting their genetic diversity.

1. Introduction

Red sugar beet (Beta vulgaris L. ssp. vulgaris var. conditiva Alef.) belongs to the order Caryophyllales, family Amaranthaceae, and genus Beta. It is a cultivated variety of sugar beet, often referred to as edible beets, flame vegetables, purple cabbage heads, and root beets. This plant typically has long, oval-shaped, purple-red- or red-colored leaves, long or spherical roots, and purple-red outer skin [1]. In addition to its use in food, red sugar beet offers nutritional, medicinal, breeding, and ornamental value [2]. It is a biennial, exhibiting self-incompatibility. Due to this feature, it is highly susceptible to various factors that can compromise its genetic integrity. Nearly all of China’s red sugar beet germplasm resources are imported, and significant genetic variation exists between different populations and within individuals of the same population. This has led to issues of extremely low purity among populations [3]. The lack of comprehensive germplasm resources in China, coupled with close genetic relationships between germplasms and narrow genetic foundations, has hindered advancements in red sugar beet breeding. Genetic diversity analysis provides insight into the genetic information of a species that constitutes biodiversity and serves as a crucial foundation for variety breeding [4]. Through this analysis, researchers can determine the genetic diversity levels within populations and the genetic differentiation between them [5]. When using molecular markers for studying the genetic diversity of populations with high heterogeneity, the choice of sampling strategy is critical. Improper sampling strategies significantly impact the accuracy of genetic diversity parameters and increase human and material costs [6]. The sampling strategy refers to the optimal sampling method that ensures representative samples capture as much genetic variation as possible. Key factors include the number of samples and the method of sampling, considering the population size within a geographical range and the number of individuals in each population [7]. Optimizing these strategies is essential to understanding genetic diversity and improving the preservation and breeding of red sugar beet. The goals and objectives of a research study are critical in determining the sampling strategy, especially when it comes to assessing population genetic diversity based on this strategy. In this context, the sample size is often based on genetic parameters that adequately capture the diversity within a population [8]. Jin Yan [9] found that in wild soybean populations, a random sampling size of 35–45 plants can effectively represent the population’s overall genetic characteristics. Similarly, Liu Wenxian [10] explored various sampling gradients in Huashan new wheatgrass, testing sample sizes for seven different individual plants, including sample sizes of 6, 10, 14, 18, 22, 26, and 30 plants. His research unveiled that a sample size of 18 plants could capture more than 95% of the population’s genetic variation. Zhang Qingying [11] conducted a genetic diversity analysis on randomly sampled wild cannabis populations by using inter simple sequence repeat (ISSR) molecular markers, concluding that a sample size of no less than 25 individuals was required to represent over 90% of the population’s genetic diversity. Although studies have focused on red sugar beet as the plant material, research concerning optimal sampling strategies for genetic diversity analysis in red sugar beet is limited.

Genetic diversity analysis of germplasm resources is vital for improving the genetic structure of crop cultivars [12]. Whether viewed through plant morphology or at a molecular level, such analysis offers a theoretical basis for crop breeding [13]. While morphological markers were once used for crop genetic diversity studies, individual trait differences are difficult to distinguish and are highly susceptible to environmental factors [14]. With the advancement of molecular biology, molecular markers have emerged ideal alternatives, surpassing morphological, cellular, and biochemical markers [15]. Using molecular markers, breeders can determine genetic relationships, distances, and differences between germplasm materials, making them a primary tool for crop improvement. Among various molecular marker techniques, polymorphic molecular markers are particularly valuable as they provide reliable analyses of germplasm resources [16], enabling more effective utilization of plant genetic resources and further division of hybrid vigor groups [17], thus improving the predictability of breeding and facilitating the identification of hybrid vigor groups. This, in turn, improves breeding predictability and provides reference points for hybrid vigor patterns [18]. Common molecular marker techniques include amplified fragment length polymorphism (AFLP) [19], sequence-related amplified polymorphism [20], random amplified polymorphic DNA [21], simple sequence repeats (SSRs) [22], ISSR [23], single nucleotide polymorphisms [24], and start codon targeted polymorphism (SCoT) [25]. Among these techniques, SSR markers stand out for their co-dominance, strong repeatability, high polymorphism, high resolution, and abundant presence in plant genomes [26]. Consequently, SSR markers have been widely used in the genetic diversity analysis of red sugar beet. In this study, SSR molecular markers, were combined with highly polymorphic universal SSR core primers screened by Li Qiaoqiao [27] to develop a sampling strategy for red sugar beet varieties (lines) using six red sugar beet germplasm resources. Additionally, a genetic diversity analysis was conducted on 47 collected red sugar beet germplasm resources, with a focus on the genetic relationships between populations. The goal of this research is to provide a scientific basis for selecting parents for red sugar beet germplasm and hybrid combinations, preserving valuable germplasm resources and fostering innovation in breeding materials. Using molecular marker techniques such as SSR, this study contributes to the efficient use of genetic diversity for future crop improvement and germplasm conservation efforts.

2. Materials and Methods

2.1. Material

2.1.1. Plant Materials

In total, 47 domestic and foreign red sugar beet germplasm resources collected in China were used as research materials (

Table 1. Names and codes of 47 tested red sugar beet germplasm resources.

Number	Name of Germplasm Resource	Number	Name of Germplasm Resource	Number	Name of Germplasm Resource
1	Yulu Red Beet	17	S214	33	GOLDEN
2	Tianyong Big Red Ball	18	HTC-95	34	CYCLHDAR
3	Red New three	19	JHTC 1601	35	ChioGGiA
4	Marunouchi mating	20	JHTC 1504	36	Geralt Yi
5	Hua Yu	21	JHTC 1602	37	Red sugar beet in the north
6	Jilin Qingfeng	22	HTC 2005-2	38	HTC 2403
7	96001-2/1-2	23	HTC 2006-1	39	HTC 2401
8	96001/2	24	BOLDOR-ORI:FR	40	HTC 2406
9	Red New two	25	WODAN-F1-ORI:AU	41	HTC 2402
10	Red stem sugar beet	26	SUBETO-F1-ORI:FR	42	HTC 2405
11	Shan Yan Hong Sweet Cabbage Head	27	ACTION-F1-ORI:FR	43	HTC 2404
12	Inner Mongolia Red	28	PABLO-F1-ORI:AU	44	Yien
13	357357	29	Red New one	45	Seed Agriculture
14	American Hybrid	30	Heida Red No.30	46	JinTai
15	rootstock	31	Heida Red No.5	47	Guilan
16	Striped Red Beet	32	Gongda Shitian No.1

). Six varieties (lines) of red sugar beet were randomly selected for studying the optimal sampling strategy. Approximately 100 seeds were sown per germplasm resource. Plants were grown until they reached the stage of having one pair of true leaves. Then, the top young leaves were cut to extract genomic DNA. This DNA was stored at −80 °C. The germplasm resources primarily came from Heilongjiang, Inner Mongolia, Xinjiang, and other places in China. Some germplasm resources also came from countries such as the United States, the Netherlands, and Japan. The germplasm types and sources are shown in Table S1.

The plant materials used for investigating the optimal sampling strategy for constructing the germplasm resources were from the varieties Marunouchi mating, Yu Lu Hong Sugar Beet, and ACTION F1 ORI:FR. The product lines included Inner Mongolia Red, Heida Red No.30, and CYCLHDAR. In total, 50 young leaves were randomly selected from 100 individual plants of each of the 6 selected red sugar beet varieties (lines). The 300 collected leaves were individually placed in 2-mL centrifuge tubes. After the leaves were brought back to the laboratory, they were frozen and stored in a −80 °C freezer for future use. Based on the constructed sampling strategy for red sugar beet, genomic DNA was extracted from the leaves of the 47 germplasm resources after mixing. The extracted DNA was used for the genetic diversity analysis of the resources.

2.1.2. SSR Primers

The primer sequences were obtained from the Key Laboratory of Beet Genetics and Breeding at Heilongjiang University and by referring to the related literature [28]. SSR primers were synthesized by Shanghai Shenggong Bioengineering Co., Ltd. (Shanghai, China) and purified using HAP. Table S2 presents the relevant information of the primers used.

2.1.3. Experimental Instruments and Reagents

The key test instruments included a concussion grinder (Retsch China Headquarters, Shanghai, China), metal bath (Chuangbo Biotechnology Co., Ltd., Shanghai, China), ultra-micro UV visible spectrophotometer (Thermo Fisher Scientific Technology Company, Waltham, America), electrophoresis tank (Liuyi Instrument Factory, Beijing, China), PCR instrument (EPPENDORF, Hamburg, Germany), high-speed centrifuge (Yingtai Instrument Co., Ltd., Changsha, China), UV gel imager (Bio-Gene Technology Ltd., Shanghai, China), electrophoresis instrument (Liuyi Instrument Factory, Beijing, China), etc.

Liquid nitrogen (Huiyuan Gas Co., Ltd., Harbin, China), CTAB buffer (Solarbio Biotechnology Co., Ltd., Beijing, China), isoamyl alcohol (Xinbote Chemical Co., Ltd., Tianjing, China), isopropanol (Tianli Chemical Reagent Co., Ltd., Tianjin, China), anhydrous ethanol (Tianli Chemical Reagent Co., Ltd., Tianjin, China), TE buffer (Solarbio Biotechnology Co., Ltd., Beijing, China), purified water (Wahaha Group Co., Ltd., Hangzhou, China), TBE buffer (Solarbio Biotechnology Co., Ltd., Beijing, China), ammonium persulfate (Fuchen Chemical Reagent Co., Ltd., Tianjin, China), N′,N2-methylenebisacrylamide (Yuanye Biotechnology Co., Ltd., Shanghai, China), 2× Rapid Tap Master Mix (Vazyme Biotechnology Co., Ltd., Nanjing, China), 50-bp marker (Tiangen Biochemical Technology Co., Ltd., Beijing, China), G-Red fluorescent nucleic acid dye (Baitaike Biotechnology Co., Ltd., Wuxi, China), etc. were the primary experimental reagents used.

2.2. Test Methods

2.2.1. Extraction of Sugar Beet Genomic DNA

Genomic DNA was extracted using the CTAB method [29]; the concentration and purity of the extracted DNA were measured using a spectrophotometer, and the extracted DNA was uniformly diluted to 10 ng/μL with TE buffer solution.

2.2.2. Sampling Gradient Division in Sampling Strategy Research

Fifty individual plant samples were extracted from the six red sugar beet germplasm resources, numbered from 1 to 50, and divided into three processing groups. In Group A, each individual sample was treated independently, resulting in 50 samples per variety (line). In Group B, DNA from individual plants was mixed in equal quantities in specific increments (1–5, 1–10, 1–20, 1–30, 1–40, and 1–50) and processed in combined samples, creating six new samples (51, 52, 53, 54, 55, and 56) for analysis. In Group C, samples were randomly mixed and divided into 33 gradients, producing 33 mixed samples per variety (line).

Table 2. Construction of sampling strategy for red beet variety (line) DNA processing method.

Processing Groups	Processing	Numbers	Sample Size
Individual plant (Group A)	Individual	1–50	50
Fixed number, single-plant mixed (Group B)	DNA mixture with numbers 1–5	51	1
	DNA mixture with numbers 1–10	52	1
	DNA mixture with numbers 1–20	53	1
	DNA mixture with numbers 1–30	54	1
	DNA mixture with numbers 1–40	55	1
	DNA mixture with numbers 1–50	56	1
Single plant, random sample mixture (Group C)	Randomly mix 5 individual plants	57–66	10
	Randomly mix 10 individual plants	67–76	10
	Randomly mix 20 individual plants	77–81	5
	Randomly mix 30 individual plants	82–86	5
	Randomly mix 40 individual plants	87–88	2
	Randomly mix 50 individual plants	89	1

provides the specific processing methods.

2.2.3. PCR Amplification and Electrophoresis Detection

The total volume of the PCR amplification system was 5 μL, including 1 μL template DNA, 2.5 μL of 2× Rapid Tap Master Mix, 0.4 μL of combined forward and reverse primers, and 1.1 μL purified water.

The fixed annealing temperature or touch down program was selected based on primer specifics. The fixed annealing temperature PCR program was run as follows: predenaturation for 3 min at 94 °C; denaturation at 95 °C for 15 s, annealing at 57 °C or 55 °C for 15 s, extension at 72 °C for 30 s, cycling 35 times; and extension at 72 °C for 5 min. The touch down PCR program was run as follows: predenaturation for 3 min at 94 °C; denaturation at 95 °C for 15 s, annealing at 65 °C for 15 s, extension at 72 °C for 30 s (cycling twice for each degree decrease from 65 °C to 56 °C, until 56 °C); denaturation at 95 °C for 15 s, annealing at 55 °C for 15 s, extension at 72 °C for 30 s, cycling 20 times; and extension at 72 °C for 5 min.

After PCR amplification, 1.5 μL of the amplification product was drawn. Then, electrophoresis was performed using an 8% nondenaturing polyacrylamide gel at a constant voltage (180 V) for 1.5 h. The buffer used for electrophoresis was 0.5 × TBE. After electrophoresis was completed, G-Red nucleic acid dye was used to dye the gel, observe, take photos, and read the tape.

2.2.4. Data Analysis

The manual band reading method was adopted for the SSR band pattern statistics, recording the bands as 0, 1, and 9. At the same migration rate position, present bands are marked as “1”, lanes with no bands were marked as “0”, and those with missing bands are marked as “9”. A 0–1 matrix (binary data) was created for each sample.

Data processing for different sampling strategies: The number of all alleles amplified from 534 DNA samples by using 14 SSR primer pairs was calculated. The genetic distance within various quality resources in Group A was calculated using MEGA 7.0 software. PopGene version 1.32 software was used to calculate the effective number of alleles, Ne; Shannon’s diversity index, I; and Nei’s expected heterozygosity (He) for Group C.

Genetic diversity data analysis of 47 red sugar beet germplasms: The number of all alleles amplified from 47 DNA samples were counted using 21 SSR primer pairs. Using PopGene version 1.32 software, we calculated and statistically analyzed genetic diversity parameters among various quality resources, including the observed allele number (Na), effective allele (Ne), Shannon’s diversity index (I), Nei’s expected heterozygosity (He), interpopulation gene flow (Nm), interpopulation genetic differentiation index (Fst), and observed heterozygosity (Ho). The polymorphic information content between different populations was calculated using PowerMarker 3.25. The population structure analysis was performed on the red sugar beet germplasm resources by using STRUCTURE 2.3.4, and the optimal number of subpopulations was calculated. MEGA7.0 software was used to calculate the genetic distance between populations. A cluster diagram of the genetic relationships between the 47 red sugar beet varieties (lines) was created.

3. Result and Analysis

3.1. Analysis of Sampling Strategies for Red Beet Germplasm Resources

3.1.1. Verification of Feasibility of Mixed Sampling

The number of amplified bands for samples 1–5, 1–10, 1–20, 1–30, 1–40, and 1–50 in the single plant treatment (Group A) and the fixed number mixed treatment (Group B) for the six test populations was calculated (

Table 3. Total number of amplified bands under single plant treatment and fixed number 1–50 treatment.

Single Plant and Fixed Number Gradient Processing	Name of Red Beet Variety (Series)
	Marunouchi Mating	Inner Mongolia Red	ACTION-F1-ORI:FR	Yulu Red Beet	Heida Red No.30	CYCLHDAR
Single plant 1–5	28	28	35	31	30	32
Fixed numbers 1–5	31	28	35	31	34	41
Single plant 1–10	34	32	35	42	36	39
Fixed numbers 1–10	36	33	42	42	34	37
Single plant 1–20	36	35	36	42	39	41
Fixed numbers 1–20	37	35	43	41	38	43
Single plant 1–30	38	37	35	42	35	41
Fixed numbers 1–30	41	36	44	40	42	47
Single plant 1–40	37	36	36	44	37	42
Fixed numbers 1–40	41	39	45	35	44	43
Single plant 1–50	33	30	34	39	29	35
Fixed numbers 1–50	44	41	45	38	47	46

). The number of bands amplified by the 14 SSR primer pairs in the fixed number mixed treatment for the six test populations (lines) was almost always equal to or greater than the number of bands amplified in the corresponding numbering treatment for each individual plant. The mixed sampling method used for extracting DNA from the red sugar beets could completely replace individual sampling for extraction, as it can save time, material resources, and labor costs.

3.1.2. Analysis of Genetic Diversity within Six Test Material Populations

The study on the amplification and genetic diversity parameters of 50 individual plants from the six red sugar beet varieties, namely Wannei Mating, Yulu Red Sugar Beet, ACTION F1 ORI:FR, Inner Mongolia Red, Black Dahong 30, and CYCLHDAR, highlighted key findings regarding their genetic diversity and polymorphism rates. The polymorphism rate of amplified bands for all six varieties was over 80% (

Table 4. Analysis of internal amplification parameters of 50 individual plants of six red sugar beet varieties (lines) using 14 SSR primer pairs.

Amplification Parameters	Name of Red Beet Variety (Series)
	Marunouchi Mating	Inner Mongolia Red	ACTION-F1-ORI:FR	Heida Red No.30	Yulu Red Beet	CYCLHDAR
Total number of amplified bands	52	43	46	52	50	55
Number of polymorphic bands	48	38	41	45	46	45
Heterogeneity ratio (%)	92%	88%	89%	87%	92%	82%
Na	2	2	2	2	2	2
Ne	1.6784	1.6882	1.7526	1.8121	1.8175	1.8308
I	0.5696	0.5674	0.5981	0.6196	0.6329	0.6414
Genetic distance within the population	0.0417~0.6875	0.0357~0.3889	0.0385~0.6250	0.0357~0.4444	0.0455~0.6000	0.0384~0.5500

Na: Observed number of alleles; Ne: effective number of alleles; I: Shannon’s information index.

). Among the six varieties, the CYCLHDAR strain had the highest number of amplified alleles at 55, whereas the Inner Mongolia Red strain had the lowest number with 43 alleles. The highest average Shannon’s information index, I, within the population was observed in CYCLHDAR with 0.6414, whereas the lowest average was observed in Inner Mongolia Red with 0.5674. The average genetic distance between individual plants within each population varied, with Yulu Red Beet showing the highest genetic distance at 0.2616 and Inner Mongolia Red showing the lowest genetic distance at 0.1759. The amplification results revealed some genetic variation within all six test populations. Notably, the overall genetic diversity within the strains (lines) was higher than that of the varieties, but with poorer consistency. Therefore, the established technical system used for analyzing these six test populations appears suitable for studying genetic diversity across most red sugar beet populations.

3.1.3. Comparison of Amplification Polymorphism between Red Sweet Menu Strain Samples (Group A) and Fixed Mixed Samples (Group B)

The use of 14 SSR primer pairs to amplify the polymorphism between 50 individual plant DNA samples and six fixed mixed DNA samples from the six red sugar beet varieties (lines) provided valuable insights into the efficiency of different DNA sampling strategies (

Table 5. Comparison of the number of alleles in a mixed sample of six red sugar beet varieties (lines) with single and fixed plant numbers.

Sampling Method		Name of Red Beet Variety (Series)
		Marunouchi Mating	Inner Mongolia Red	ACTION-F1-ORI:FR	Heida Red No.30	Yulu Red Beet	CYCLHDAR
Individual	A	34.92	33.36	35.14	40.58	34.58	38.7
Fixed number single plant mixed sample	B1	31	28	35	31	34	41
	B2	36	33	42	42	34	37
	B3	37	35	43	41	38	43
	B4	41	36	44	41	42	47
	B5	41	39	45	35	44	43
	B6	44	41	45	38	47	46

Note: Data for individual A listed in the table are the average number of alleles amplified by 14 SSR primer pairs from 50 individuals per plant; B1~B6 represent samples 1–5 to 1–50 processed by Group B in Table 2, respectively.

). Among the six tested materials, the number of alleles amplified from the fixed-number mixed samples of individual plants in Group B (1–5, 1–10, 1–20, 1–30, 1–40, and 1–50) mostly had a gradient greater than the average number of alleles amplified from 50 individual plants by using 14 SSR primer pairs in Group A. Among the six test materials, five mixed samples with fixed numbers (1–5) in Group B amplified fewer alleles than Group A. The number of alleles amplified from three mixed samples with fixed numbers 1–10 was lower than that amplified by Group A. The number of alleles amplified from the mixed samples with fixed numbers 1–20 and 1–30 was greater than that amplified by Group A. The number of alleles amplified from the mixed samples with fixed numbers 1–40 and 1–50 was lower than that of Group A. Only one (Heida Red No.30) was amplified. This is also consistent with the data presented in Section 3.1: single plant mixed samples can basically amplify all banding patterns of single plant DNA. When mixing strains 1–5, 1–10, 1–40, and 1–50, amplification results were missing in the test materials. Therefore, samples of 20 and 30 strains are recommended to be mixed to markedly reduce workload while also including almost all alleles.

3.1.4. Analysis of Amplification Parameters for Randomly Numbered (Group C) Mixed DNA Samples

From

Table 6. Comparison of amplification parameters for random mixed amplification of six tested red sugar beet varieties (lines) with different sample sizes.

Amplification Parameters	Variety (Series) Name	Randomly Numbered Sample Mix Number
		5	10	20	30	40	50
Expand the number of sites	27	43.7	44.5	44.8	45.4	42.5	44
	4	45.3	48.9	49.2	50	49.5	46
	12	38.8	40.9	39.6	40.6	40.5	39
	30	44.7	45.9	36.8	41.6	40	50
	1	43.4	46.2	46	46.2	45.5	47
	34	44.8	46	41.8	46.6	44	45
Ne	27	1.9105	1.9921	1.9193	1.9376	1.9000	1.9333
	4	1.9411	1.9718	1.9425	1.9890	1.8714	1.7143
	12	1.8563	1.9461	1.9806	1.8867	1.8333	1.7500
	30	1.8654	1.9375	1.8857	1.8805	1.6857	1.8462
	1	1.9400	1.9890	1.9714	2.0000	1.9714	2.0000
	34	1.9451	1.9868	1.9748	1.9945	1.9667	1.8571
I	27	0.6652	0.6911	0.6604	0.6755	0.6343	0.6469
	4	0.6767	0.6855	0.6735	0.6903	0.6249	0.4951
	12	0.6330	0.6768	0.6880	0.6472	0.5776	0.5199
	30	0.6409	0.6738	0.6302	0.6498	0.4858	0.5865
	1	0.6658	0.6903	0.6838	0.6931	0.6838	0.6931
	34	0.6778	0.6895	0.6858	0.6917	0.6822	0.5941
Nei’s	27	0.4731	0.4980	0.4707	0.4827	0.4554	0.4667
	4	0.4838	0.4924	0.4814	0.4971	0.4464	0.3571
	12	0.4460	0.4842	0.4949	0.4586	0.4167	0.3750
	30	0.4525	0.4813	0.4514	0.4599	0.3482	0.4231
	1	0.4761	0.4971	0.4911	0.5000	0.4911	0.5000
	34	0.4849	0.4964	0.4929	0.4986	0.4896	0.4268

Note: Data in the table are the average values of repeated samples with different sampling sizes. The average number of amplification sites in 10 repeated samples with a sampling size of five and so on; The Variety (Series) number section in the table corresponds to the numbers and names in Table 1; Ne: number of effective alleles; I: Shannon’s information index; Nei’s: expected heterozygosity.

, the analysis of randomly numbered mixed samples (Group C) of six tested red sugar beet varieties (lines) using 14 SSR primer pairs revealed significant trends in SSR amplification and genetic diversity parameters across the mixed samples. The average number of SSR amplification sites increased with the increase in the number of random mixed samples. Three of the six varieties (lines) peaked at a random mixed number of 30 and then showed a decrease. Inner Mongolia Red peaked at a random mixing number of 20 and then exhibited a decrease, but the overall increase or decrease was not significant. For Yulu Red Beet and CYCLHDAR, the number of SSR amplification sites continued to increase with the number of mixed samples, reaching the maximum at a random mixing number of 50. The effective allele count (Ne), Shannon’s information index (I), and Nei’s expected heterozygosity (He) generally followed a trend of first increasing and then decreasing as the number of random mixed samples increased. ACTION F1 ORI:FR and Heida Red No.30 reached peak values at a mixing number of 10. Inner Mongolia Red exhibited a peak at a mixing number of 20. Marunouchi mating, Yulu Red Beet, and CYCLHDAR reached peak values at a mixing number of 30. The average genetic distance within these three varieties (lines) was relatively large, suggesting that more individual plants were required to fully capture the genetic variation within the population. Peak genetic parameters for the six materials were generally observed between 10 and 30 random mixtures. When the random mixture number increased to 40 or 50, the genetic parameters (e.g., Ne, I, and He) were generally lower than those observed at a mixing number of 5. Consistent with the findings for mixed samples of fixed numbers, random mixtures of 10 to 30 single plant samples provided the best representation of genetic variation for the study of genetic diversity among these populations.

3.2. Genetic Diversity Analysis

3.2.1. Amplification Results of 21 SSR Primer Pairs and Polymorphism of Loci

In this experiment, 21 SSR primer pairs were used to amplify 47 randomly selected red sugar beet germplasm resources, with 30 individual plants mixed for DNA extraction. Stable and highly polymorphic amplification results were obtained. The results demonstrated the high effectiveness of the selected primers in revealing genetic diversity across the germplasm resources (

Table 7. Polymorphism analysis using 21 SSR primer pairs.

Primers	Total Loci	Polymorphic Loci	PIC
W21	2	1	0.0782
TC94	3	3	0.4326
TC55	6	6	0.7045
SB06	3	2	0.2856
L70	6	6	0.7202
L59	3	2	0.6718
L48	4	3	0.6194
L16	3	2	0.4435
77067	3	2	0.5213
57236	4	3	0.5940
27906	5	3	0.6951
27374	3	2	0.5335
26391	6	5	0.7298
24552	4	3	0.6675
18963	4	3	0.5663
17923	5	5	0.6872
17623	4	2	0.6313
16898	4	3	0.5909
14118	7	5	0.5574
11965	6	5	0.7066
2305	4	3	0.6131
Total	89	69	__
Average	4.24	3.29	0.5738

). The 21 SSR primer pairs amplified 89 bands, of which 69 bands were polymorphic and the polymorphism percentage was 78%. Each SSR primer pair amplified between two and seven bands, with an average of 4.24 bands amplified per primer. The average PIC value for the 21 primer pairs was 0.5738, with a range of 0.0782 (primer W21) to 0.7298 (primer 26391). Only one of the 21 primer pairs (W21, PIC = 0.0782) had a PIC of less than 0.250, accounting for 4.7% of the total primers used. This suggests that the remaining 95.3% of the primers exhibited high polymorphism (PIC > 0.250). Figure 1 presents the electrophoresis images of 47 red sugar beet germplasm resources amplified by primer TC94.

3.2.2. Genetic Diversity Analysis of 47 Red Beet Germplasm Resources

Using 21 SSR primer pairs, the genetic diversity parameters for 47 red sugar beet germplasm resources were calculated using PopGene version 1.32 software, with the results summarized in

Table 8. Genetic diversity parameters of 47 red beet germplasm resources.

Mean Value of Genetic Diversity Parameters	Numerical Value
Observing the number of alleles (Na)	4.1905
Effective number of alleles (Ne)	2.8962
Shannon’s information index (I)	1.1299
Expected heterozygosity rate (He)	0.6127
Observation of heterozygosity rate (Ho)	0.3806
Expected heterozygosity (Nei’s)	0.6127
Gene flow between germplasms (Nm)	0.43
Genetic differentiation index between populations (Fst)	0.3677

. The average value of He was 0.6127 (He > 0.5), which is greater than 0.5, indicating a high level of genetic diversity in the tested materials [30]. The relative size between the observed heterozygosity rate (Ho) and the expected heterozygosity rate (He) reflects the mating characteristics and heterozygosity of the population. The average Ho of the entire population was 0.3806, which was lower than the average He of 0.6127. The lower Ho relative to He indicates a deficit in heterozygotes, suggesting inbreeding within the population [31]. The genetic differentiation index between populations (Fst) is a quantitative indicator used for measuring the degree of genetic differentiation and genetic distance between the populations. The higher the differentiation rate, the greater the genetic differences within the population [32]. Typically, Fst values range from 0 to 0.25, with values above 0.25 indicating significant population differentiation. The Fst value for the 47 germplasm resources was 0.3677, which signifies a high degree of genetic differentiation among this population. When the gene flow (Nm) value is less than 1, genetic drift can cause significant genetic differentiation between populations [33]. In this study, the gene flow value among the 47 red sugar beet germplasm resources was 0.43, indicating significant genetic differentiation among different varieties (lines). The low gene flow indicates limited gene exchange between populations, likely due to factors such as low natural hybridization rates or pollination incompatibility in red sugar beet.

3.2.3. Genetic Distance and Cluster Analysis of Red Beet Germplasm Resources

According to the amplification results, the genetic distance analysis of the 47 red sugar beet germplasm resources was performed using MEGA software. The results revealed important insights into the genetic relationships within the population. The genetic distance between the 47 germplasms ranged from 0.0225 to 0.551, with most germplasms exhibiting a genetic distance between 0.2 and 0.4. The average genetic distance was 0.316. The smallest genetic distance of 0.0225 was observed between PABLO-F1-ORI:AU and Red New One, indicating a close genetic relationship and a relatively high frequency of genetic exchange between these two germplasms. The largest genetic distance of 0.551 was found between the American hybrid strain and the HTC-2403 strain, indicating significant genetic background differences and a distant kinship relationship. This suggests minimal genetic exchange between these two germplasms.

To reflect the genetic relationships more directly, the Unweighted Pair Group Method with Arithmetic Mean (UPGMA) was used to cluster and analyze the 47 red sugar beet populations based on genetic distance, and a phylogenetic tree was constructed (Figure 2). Based on a genetic distance threshold of 0.17, the germplasms were divided into six main categories: Class A includes 28 varieties of red sugar beet germplasm; Class B includes 8 varieties of germplasm; Class C includes 2 varieties of red sugar beet germplasm; Class D includes 4 varieties of red sugar beet germplasm; Class E includes 1 red sugar beet germplasm; and Class F includes 4 varieties of red sugar beet germplasm. Several pairs of varieties showed genetic distances of less than 0.1, indicating extremely close relationships. These varieties were clustered into the same group or even the same branch in the phylogenetic tree. Some key close relationships included: PABLO-F1-ORI:AU (28) and Red New One (29); SUBETO-F1-ORI:FR (26) and Red New One (29); SUBETO-F1-ORI:FR (26) and WODAN-F1-ORI:AU (25); strain JHTC 1601 (19) and product line JHTC 1602 (21); Huayu (5) and Jilin Qingfeng (6); HTC 2005-2 (22) and HTC 2006-201 (23); SUBETO-F1-ORI:FR (26) and PABLO-F1-ORI:AU(28); Heida Red No.30 (30) and Heida Red No.5 (31).

3.2.4. Genetic Structure of Red Beet Germplasm Resources

The population structure of the 47 red sugar beet germplasm resources were analyzed using STRUCTURE 2.3.4 software. When K = 6, ΔK exhibited a maximum peak value (Figure 3). A population structure diagram of 47 varieties (lines) was drawn on the basis of the obtained K values (Figure 4). The results of the population structure chart indicated that the 47 red sugar beet resources can be divided into six groups based on their genetic composition. This is consistent with the genetic distance-based UPGMA clustering results.

4. Discussion

Genetic diversity is crucial for the survival, adaptation, and sustainable development of plant populations. Higher genetic variation within a genetic pool enhances the adaptability of plants to environmental changes, increasing their chances of survival [34,35]. Genetic diversity also provides an essential resource for breeding programs, enabling the identification of parents with superior traits and facilitating the development of more efficient hybrid combinations [36]. Numerous studies have been conducted on sugar beet genetic diversity using molecular markers. Liang Xuemei used various of molecular markers, including SSR, restriction site amplification polymorphism, PCR direct amplification of small satellite DNA (DAMD), and SCoT, to assess the genetic structure of 111 sugar beet varieties [37]. Liu Dali assessed the genetic variation level of phenotypic of 215 sugar beet germplasm resources through correlation and cluster analyses and divided them into four groups [38].

Although molecular markers have been widely applied for sugar beet variety identification, the repetitive use of the same parental lines (sterile or pollination) has led to high heterozygosity in individual genotypes and a narrow genetic basis of sugar beet varieties [39]. To maximize the discovery of genetic variation while minimizing cost, a key challenge in genetic diversity studies is optimizing the sampling strategy based on the spatial distribution of genetic variation in the natural population of organisms [40]. Khosro Mehdi Khanlou used AFLP molecular markers in white clover, determining that a sample size of 20 plants was sufficient capture over 95% of the genetic variation. They found that when the sample size was less than 15 plants, the expected heterozygosity (He) and Shannon diversity index (I) were low [6]. Using SSR markers, Li Lei analyzed the genetic diversity and differential distribution of 210 individuals from three cannabis varieties (70 individuals each) through computer simulation. They found that found that sampling 30 individuals from cannabis varieties captured over 93% of genetic diversity, while sampling 50 individuals increased the coverage to 95% [41]. Li Qiaoqiao analyzed and compared the amplification results of four sugar beet germplasm resources under different treatments by using SSR molecular markers. They found that found that a sampling size of 20 plants in sugar beet, with high-frequency allele detection (>90%), was optimal for analyzing genetic diversity [42]. However, no published research has directly addressed the optimal sampling strategy for red sugar beet. This study seeks to fill that gap by developing a sampling strategy tailored to the genetic diversity of red sugar beet. It aims to aid in red sugar beet breeding by offering new related ideas.

A reasonable sampling strategy is crucial for ensuring the representativeness of the research materials and accuracy of the genetic diversity studies. It substantially affects the reliability of research findings and the effectiveness of evaluation of biological genetic diversity and variety identification. This study confirmed that mixed sampling is effective for DNA extraction, as indicated by the total number of amplified bands under different sampling treatments (single plant vs. fixed numbers 1–50). The analysis of genetic differences among individuals underscores the need for a well-constructed sampling strategy to accurately capture genetic diversity. The analysis showed that the genetic diversity metrics (average number of amplification sites; effective allele, Ne; Shannon’s information index, I; and expected heterozygosity, Nei’s) were concentrated in the range of 10–30 individual plants for random mixed samples. This suggests that a sampling size of 10–30 individual plants is optimal for accurately analyzing genetic diversity in red sugar beet. Using 21 SSR primer pairs, the average PIC was 0.5738, indicating high polymorphism and the effectiveness of these primers for studying genetic diversity in red sugar beet germplasm. The genetic distance among the 47 red sugar beet germplasm resources tested ranged from 0.0225 to 0.551, with an average of 0.316. The average expected heterozygosity rate (He) was 0.6127 (He > 0.5), and the average observed heterozygosity rate (Ho) was 0.3806. The finding Ho < He suggests high genetic diversity and differentiation, as well as some degree of inbreeding within the populations. Genetic distance clustering (UPGMA clustering) and STRUCTURE analysis are two different sample clustering methods with different modes of operation. The former clustering is based on genetic distance or genetic similarity, which is usually affected by human operation. The STRUCTURE analysis can classify populations (individual plants) with unknown divisions into corresponding populations, and the phenotype is more clear. Combining these two clustering methods can more accurately determine the genetic relationships between germplasm [43]. The UPGMA clustering method divided the 47 germplasm resources into six main categories based on a genetic distance of 0.17, which reflects genetic relationships but can be influenced by operational choices. The STRUCTURE software, based on the principle of maximum likelihood, identified six groups (K = 6) as having the peak ΔK value, confirming the clustering results obtained from UPGMA analysis.

5. Conclusions

Using SSR molecular markers, this study analyzed and compared the amplification results of DNA samples from six red sugar beet germplasm resources under three treatments. Accordingly, a reasonable and accurate sampling strategy gradient was constructed, and the genetic diversity analysis was conducted on 47 red sugar beet germplasm resources. The genetic diversity, genetic background, and intrinsic genetic structure among the tested materials were further determined. In breeding work, materials can be selected and utilized in a more targeted manner. Selecting and using materials having relatively distant genetic relationships and different genetic backgrounds as parents can help fully leverage hybrid advantages and breed high-performance hybrid innovative varieties. This is of significance for guiding breeding of red sugar beet hybrids and future related research. In the present study, only one molecular marker, SSR, was used. Based on this SSR molecular marker sampling strategy, future studies can use different molecular markers for mutual verification to obtain a more accurate, reliable, and comprehensive optimal sampling size for analyzing the genetic diversity of red sugar beet.