Next Article in Journal
Damages of Skidder and Oxen Logging to Residual Trees in Uneven-Aged Mixed Forest
Next Article in Special Issue
A Stable and Efficient Genetic Transformation Method for Embryogenic Callus of Fraxinus mandshurica
Previous Article in Journal
Resin Canal Traits Variation in Pinus spp. with Different Susceptibility to the Pine Wood Nematode
Previous Article in Special Issue
Optimization of Key Technologies for Induction of Embryogenic Callus and Maturation of Somatic Embryos in Korean Pine (Pinus koraiensis)
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Forests
Volume 14
Issue 5
10.3390/f14050926
forests-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

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

Developments on Core Collections of Plant Genetic Resources: Do We Know Enough?

by
Rui Gu
,
Shaohui Fan
,
Songpo Wei
,
Jiarui Li
,
Shihui Zheng
and
Guanglu Liu
*
International Center for Bamboo and Rattan, Beijing 100102, China
*
Author to whom correspondence should be addressed.
Forests 2023, 14(5), 926; https://doi.org/10.3390/f14050926
Submission received: 21 March 2023 / Revised: 17 April 2023 / Accepted: 27 April 2023 / Published: 29 April 2023
(This article belongs to the Special Issue Advances in Tree Germplasm Innovation and High-Efficiency Propagation)
Browse Figures
Review Reports Versions Notes

Abstract

:
The core collection is a small subset that minimizes genetic redundancy while preserving the maximum genetic diversity of the entire population. Research on the core collection is crucial for the efficient management and utilization of germplasm resources. In this paper, the concept of the core collection and the research progress of its construction have briefly been summarized. Subsequently, some perspectives have been proposed in this research field for the near future. Four novel opinions have been presented, (1) the effective integration of multiple data types and accurate phenotyping methods need to be focused on; (2) the sampling strategy and bioinformatics software should be given attention; (3) the core collection of afforestation tree and bamboo species, with a wide natural distribution range and a large planting area, need to be carried out as soon as possible; (4) we should place a high priority on the study of genes discoveries and utilize these with a rapid, precise and high-throughput pattern based on re-sequencing technology. This paper provides a theoretical and technological reference for further study and the application of the plant core collection.

1. What Is a Core Collection?

Germplasm resources serve as a crucial material basis for genetic research and help in the identification and utilization of genes and traits that are of economic and ecological importance [1]. Therefore, the preservation and utilization of germplasm resources are of significant importance for the development of new crop varieties, and a large number of germplasm banks have been established [2]. However, due to their vast amount, diverse structure and incomplete information on germplasm resources, the available diversity that has been collected may not be fully and effectively utilized [3,4,5].
To obtain a germplasm bank that is both practical and representative, Australian scholars Brown [5] and Frankel [6] proposed the concept of core collection in 1984. Core collection refers to selecting a part of the entire germplasm resource through certain methods with the goal of representing the genetic diversity of the entire germplasm resource with a minimum number of resources. The theoretical basis supporting this concept is the theory of neutral mutations and the hierarchical structure model of genetic diversity [7]. A good core collection should have the following characteristics: representativeness, low redundancy, manageability, data completeness, and usability [8]. A core collection provides more reliable data and samples, makes it easier to optimize genotype/molecular marker-phenotype association studies, improves the utilization efficiency of the germplasm, and accelerates the breeding process [9,10,11]. Recently, with the continuous improvement in research methods, the concept of the core collection has been extended to the fields of asexual propagation crops, DNA germplasm banks and the in situ conservation research of germplasm resources [12].

2. The Progress of Core Collection

In considering world experience and the formation of core collections that the literature reveals, we focus on the following questions:
  • Have core collections been formed for a diversity of plants?
  • How can we effectively construct a representative core collection?
  • How well can the core collection be utilized?
Our responses to these questions are summarized below.

2.1. Diversity of Core Collections

Core collections preserve the genetic diversity of the original population as much as possible, which promotes the effective use and protection of germplasm resources [4,5,6]. Based on this, many core collection research studies have been conducted both domestically and internationally. This study summarizes the development and research of core collections of 146 plant species over the most recent 10 years, which are listed in Table 1 below and Supplementary Table S1. The table shows that core collections have been developed mainly in economic crops and fruit trees; meanwhile, forages have been recently exploited for core collection establishment, including Buchloe dactyloides (Nutt.) [13], Cynodon Rich. [14] and Bromus inermis Leyss. [15]. However, core collections of endemic afforestation tree species are still limited, although some have been reported, such as those of Cunninghamia lanceolata (Lamb.) Hook. [16], Robinia pseudoacacia L. [17], Populus tomentosa Carrière. [18], Pinus massoniana Lamb. [19], etc. In addition, only a few of these studies have focused on spice crops, and the core collection that has been constructed is dominated by Santalum album L. [20].

2.2. Procedure of Constructing a Core Collection

The development of the core collection has been extensively studied from various perspectives, such as sampling strategies, core size determination, and analysis methods, among others. However, due to the wide variation in the growth habits and reproductive characteristics of various plants, there is no universal core collection construction method. Generally, the construction of the core collection mainly includes four steps: the collection and organization of data, the grouping of accessions, the determination of sampling strategies and the testing and evaluation of the core set [1,8].

2.2.1. Data Selection for Core Collection

Initially, passport data, phenotypic characteristics (agronomic and morphological traits), and biochemical traits were used to develop core collections, as these traits can visually represent plant differences and are straightforward to measure [37,157,158]. Based on these, core collections have been reported for many crops (Supplementary Table S1), including Spinacia oleracea L. [51] and Chrysanthemum morifolium Ramat. [95], Helianthus annuus L. [103], Sorghum bicolor(L.) Moench [25], Solanum lycopersicum L. [49] and Coix lacryma-jobi L. [26]. Additionally, this core collection method has been applied to many fruit trees, such as Armeniaca vulgaris Lam. [67], Punica granatum L. [80], Ziziphus mauritiana Lam. [75], Prunus persica L. Bat sch. [87], etc. Nevertheless, using phenotypic data produces certain challenges, such as its potential loss, incompleteness, unreliability, and easy susceptibility to environmental factors [17,124]. As a result, the core collection may not accurately represent the full genetic diversity of the original group, potentially resulting in the exclusion of important and valuable germplasms in the core collection [17,159].
With the increased availability and efficacy of molecular markers in uncovering genetic diversity, the development of more robust core collections has been made possible [124,139]. These markers include SRAPs for Fallopia multiflora (Thunb.) Nakai [105], Sapium sebiferum (Linn.) Roxb. [133]; ISSRs for Chimonanthus praecox (L.) Link [98], Tetracentron sinense Oliv. [130], Cornus officinalis Sieb. et Zucc. [109], Argania spinosa (L.) Skeels [151]; AFLPs for Ginkgo biloba L. [153], Prunus mume Sieb. et Zucc. J [97]; SSRs for Raphanus sativus L. [62], Cunninghamia lanceolata (Lamb.) Hook [16], Actinidia chinensis Planch. [79], Astragalus membranaceus Moench [106], Olea europaea L. [83], Castanea mollissima Bl. [149], Corylus avellana L. [137], etc. Additionally, SNPs for Amaranthus tricolor L. [52], Brassica napus L. [47], and Litchi chinensis Sonn. [81], Glycine max (L.) Merr. [41] and Cucumis sativus L. [56]. Among these various molecular markers, SSR markers are globally used, which can be attributed to their numerous benefits, including simple operation, high levels of polymorphism, a co-dominant inheritance, stability and reproducibility [160]. In recent years, SNP markers have become increasingly important in molecular detection, largely due to their high abundance in genomes and high-throughput genotyping on automated platforms [62].
Although molecular markers have several advantages in developing core collections, they cannot fully embody the genetic diversity of species due to their partial polymorphism of DNA fragments. Thus, breeders often employ multiple data types to construct a more robust and reliable core collection, which can greatly prevent the loss of crucial germplasms and enhance the precision and comprehensiveness of core sets [17,124]. For instance, Peng et al. [117] used phenotype, genotype and favorable alleles to develop a core collection of Gossypium hirsutum L. The core set of Carthamus tinctorius L. [120] and Polygonum fagopyrum L. [31] were established by molecular, phenotypic, and geographical diversity; Guo et al. [17] developed the Robinia pseudoacacia L. core collection using a combination of phenotype, physiology, and genotyping markers. The core collection of Perilla frutescens L. [65], Catalpa fargesii f. duclouxii (Dode) Gilmour [124], Juglans regia L. [139], Castanopsis hystrix Hook. f. and Thomson ex A. DC. [143], Pinus yunnanensis Franch. [152], Camellia oleifera Abel [155], etc., were developed by utilizing a combination of molecular markers and various phenotypic data.

2.2.2. Separating the Accessions into Meaningful Groups

To develop core collections effectively, it is crucial to create distinct and internally consistent groups that ensure representative sampling and reflect differences in genetic diversity. This can be achieved based on various factors such as plant taxonomy, geographical origin, ecological zone, phenotypic characteristics, and genetic distance [3,88]. Cluster analysis is a widely used tool for grouping accessions to construct core collections [161,162]. However, the choice of cluster methods greatly influences the resultant core collection, and several common cluster methods are available, such as single linkage, the complete method, the median method, the centroid method, and UPGMA and Ward’s method [161]. Generally, the UPGMA method is widely used for its moderate nature and monotonicity [163]. Ward’s method is also effective but requires the use of Euclidean distances. In addition to these commonly used methods, STAT software (StataCorp, College Station, TX, USA) provides other clustering methods, such as the maximum likelihood hierarchical clustering method (EML), density estimation method (DEN), and two-stage objective estimation method (TWO). Of course, the selection of a clustering method must be in combination with the corresponding sampling methods to improve the accuracy and rationality of the constructed core collection [163,164].

2.2.3. Sampling Strategies of Core Collection

The construction of core collection requires the careful consideration of sampling strategies to ensure maximal diversity and the minimal redundancy of selected accessions [4,16]. Sampling methods can be roughly divided into simple random sampling and stratification cluster sampling [4,8]: the former treats all germplasm materials equally, with a random selection of core accessions, ignoring the uneven distribution of genetic diversity and the frequency of different alleles in the entire genetic resource. Hence, this method may overlook certain accessions with low frequency but high variability in the entire group. By contrast, the stratification cluster sampling method, which is based on grouping and preserves the spatial and genetic distribution structure of original populations, is the most commonly used and effective method for the construction of core collections. It involves several steps to create a core collection. The first step is to determine the desired size of the core collection. Then, accessions can be grouped or stratified based on their characteristics, such as passport data, phenotypic data and genetic distance. Next various sample allocation methods, such as proportional (P), logarithmic (L), constant (C), square root (S), or genetic diversity-dependent allocation (G), are applied to determine the number of entries that are to be chosen from each group. Finally, one can either randomly select entries within each group or use a stepwise clustering approach to make the final selection [28,82,161]. Hu et al. [159] proposed a stepwise clustering method for selecting the final entries, where two materials were chosen at the lowest level of the clustering tree using random sampling, preferred sampling or deviation sampling. If there is only one germplasm, it is automatically included in the core set. The remaining materials were screened using the same method until the desired sampling size was achieved. Each of the three sampling strategies has unique characteristics [124,159]. Huang et al. [14] reported that the preferred sampling method was the most effective method for developing a core collection of Cynodon Rich, with both preferred and deviation sampling appearing more effective than random sampling in reducing the variance and coefficient of variation in the core collection [165]. Meanwhile, it is worth noting that cluster methods significantly affect the representation of core collections developed by three stepwise cluster sampling strategies [161]. Later, Wang et al. [161] proposed a minimum distance stepwise clustering strategy (LDSS) for selecting the final materials. This method selected one of two materials with the smallest genetic distance on the clustering diagram each time, resulting in a more effective reduction in genetic redundancy compared to other methods [161]. Figure 1 shows a diagram of the methodology used to establish the core collection.
In recent years, the maximization (M) strategy, which selects materials with a high allele abundance and low redundancy by maximizing the number of alleles at each locus, has been widely applied in core collection construction [16,166]. This approach does not rely on grouping or clustering methods, and the sampling proportion is automatically generated [166,167]. Currently, there are several software programs available for the construction of core collection based on the M strategy [16,137]: Power Mstrat [168], Power Core [169], CoreFinder [170] and Core Hunter [171,172]. Some studies have found that with the same sampling ratio, PowerCore retained a higher percentage of alleles and genetic diversity compared to other software programs [1,119]. However, Chen et al. [173] compared the PowerCore and the CoreFinder algorithms and showed that the core collection developed by CoreFinder was more suitable for NARO rapeseed (Brassica napus L.). While the M strategy often selects poorly representative germplasm due to the need to maximize allelic diversity, the distance-based method selects materials with good representativeness but may not be ideal in terms of allelic retention. To address this issue, Core Hunter software (Department of Computer Science, University of British Columbia, Vancouver, Canada) optimized genetic distance and allelic diversity simultaneously by weighting the modified Rogers distance and Shannon diversity index differently based on two optimization criteria [172].
Figure 2 and Supplementary Table S1 provide an overview of the sampling strategies used to develop core collections, which show that multiple cluster sampling based on a genetic structure is the most widely used, followed by the M strategy. I think that the optimal core collection should exhibit two characteristics: maximum genetic distance and maximum genetic diversity. The M strategy focuses on selecting diverse loci, while the genetic distance method aims to select diverse germplasm. Combining both approaches may lead to the best results. Of course, different plants are suitable for different sampling methods, and the strategy to be adopted can usually be determined by the purpose of the core collection [16,137].

2.2.4. Sampling Proportion of Core Collection

A suitable sampling ratio is another critical factor to consider in the process of constructing core collections. A high sampling ratio may result in the inclusion of redundant samples in the core collection, while a low sampling ratio may lead to the loss of key materials [17]. Brown et al. [4] proposed that a sampling ratio of 10% could represent 70% of the genetic diversity in the original collection when the number of original collection samples was no less than 3000. Van Hintum et al. [3] argued that sampling proportions should range from 5 to 20%. Yonezawa et al. [174] suggested that a sample proportion of 20%–30% would be appropriate. In addition, germplasm with excellent traits, as well as backbone parents or varieties that have played a significant role in production, should be directly selected into the core collection to avoid the loss of excellent germplasm or genes.
However, Supplementary Table S1 shows that the sampling proportion for forming a core collection is generally about 1.70%–66.46% of the original collection, and the size of core collections can vary significantly, with some consisting of as few as 12 samples while others include up to 1956 samples. Clearly, the sampling ratio is not fixed but varies depending on the characteristics of the plant. The optimal fraction depends largely upon the original germplasm size, germplasm accessibility, germplasm similarity, and sampling strategy [7]. Under any circumstances, the preservation of a significant proportion of germplasm diversity should be the main consideration when determining the optimal fraction [148].

2.3. Evaluation of Core Collection

While the core collection is constructed based on available data, the important question remains: does the core set accurately represent the diversity of the original population?
Brown proposed that the core collection should represent 70% or more of the trait characteristics and genetic variations of the entire germplasm [4]. To validate the effectiveness of the core collection, it should be evaluated from two aspects: firstly, to test the representativeness of the genetic diversity of the entire collection and, secondly, to assess its practicality in production [59]. Generally, at the molecular level, the main genetic diversity indices include the allele number (Na), effective allele number (Ne), Shannon’s information index (I), Nei’s genetic diversity index (H), polymorphism information content (PIC), observed heterozygosity (Ho) and expected heterozygosity (He) [159]; among these, allelic richness is considered the most relevant indicator. Maximizing allelic richness means preserving the germplasm resource with the most abundant genetic diversity. At the phenotypic level, the evaluation parameters include the mean difference percentage (MD), variance difference percentage (VD), coincidence rate of range (CR) and variation coefficient changing rate (VR) [8,69,159]. Usually, the core collection is considered representative only when the MD is less than 20%; the CR is more than 80% [159]. A lower value in MD and a higher value in VD, CR, and VR could be considered to indicate a more representative core collection [14,59]. In addition, principal component analysis (PCA) plots have been widely used to compare the distribution characteristics between the core collection and the initial population [14]. Moreover, correlation analysis is commonly conducted to infer whether the inherent relationship between traits in the original collection is well retained in the core group [14]. Recently, Odong et al. [8] proposed two new criteria based on genetic distance to evaluate the quality of the core collections. These criteria offer the advantage of simultaneously considering all variables describing the accessions and provide intuitive and interpretable results compared to the univariate criteria generally used in core collection evaluations. Additionally, after establishing a core collection, it is essential to establish a comprehensive management system for breeding, seed supply, and exchange as soon as possible to ensure the distribution, sharing, and effective utilization of the core set.
In short, the evaluation criteria of core collection should be variable, and flexible evaluation methods should be tried according to the new situation. The selection of the most suitable evaluation method should depend upon the purpose of core collections [8]. Moreover, core collection establishment is a dynamic process [148] that needs to be regularly updated by the addition of new entries and the removal of duplicates to improve representativeness and maintain dynamism [120].

3. The Use of Core Collection

The main purpose of constructing core collections is to effectively address the conflict between a large number of germplasm resources in gene banks and their effective preservation, to improve the utilization rate of excellent germplasm, to promote the exploration of excellent gene and molecular markers, and to achieve innovation and genetic improvement in crop germplasm [17,175]. Currently, with the development of next-generation sequencing technology, domestic and foreign research institutions are conducting deep resequencing of the core collection to accelerate breeding efficiency.

3.1. Genomic Study and Marker Development

Molecular markers are valuable tools in studies such as genotype determination, genetic diversity analysis, marker-assisted breeding, the fine mapping of QTLs, and phylogenetic analysis. Due to the existence of reference genome sequences for various species and their access to core collections, it is now possible to create a vast number of DNA markers, such as SNP, insertions/deletion (InDel), cleaved amplified polymorphic sequence (CAPS), etc. Molecular markers have broad prospects for application in the core collection, which can promote the protection and utilization of germplasm resources.

3.1.1. Evaluation of Genetic Diversity

Developing molecular markers for core collection is beneficial for evaluating genetic variation, gaining insights into the genetic background of germplasm resources, and thus protecting and enhancing genetic diversity. For example, Wu et al. [176] used genotyping-by-sequencing to identify 4680 high-quality SNPs in a core collection of 150 Chinese Ziziphus jujuba Mill. accessions, and characterized their genetic diversity, population structure, and linkage disequilibrium based on SNPs. Additionally, Lu et al. [177] developed 23 novel markers using restriction site-associated DNA sequencing to analyze the genetic diversity of Amomum tsao-ko.

3.1.2. Classification of Species and Varieties

Molecular markers can help identify the species and varieties of core collections that are difficult to distinguish morphologically. Li et al. [178] designed 134 primers based on cucumber genome re-sequencing, with 116 showing polymorphisms in 16 germplasms, which were used for the identification and evaluation of cucumber. Similarly, Guo et al. [179] identified InDels throughout the genome of Capsicum spp. and developed three PCR-based markers that could distinguish interspecific hybrids of five domesticated Capsicum species (C. annuum, C. chinense Jacq., C. baccatum, C. pubescens Ruiz and Pavon and C. frutescens).

3.1.3. Construction of Genetic Maps

Molecular marker analysis can construct genetic maps of germplasm resources, which help understand their genetic structure and evolutionary history, providing a basis for comparative genomics research. For example, Meng et al. [180] aligned the re-sequencing results of two Brassica campestris accessions, ‘R-O-18’ and ‘L58’, to the genome sequence of ‘Chiifu-401-42’, and developed 99 InDel markers to construct a genetic linkage map of Brassica rapa. Yu et al. [181] genotyped 150 recombinant Brassica rapa inbred lines using genome resequencing and generated a high-density genetic map containing 2209 SNPs. They observed a high recombination rate of up to 20% for the genetic markers and detected 18 QTLs for the leafy head traits and three candidate genes. In addition, Song et al. [182] performed resequencing on six non-heading Chinese cabbage variants(Pak-choi, Taitsai, flowering Chinese cabbage, turnip, Tillering cabbage, and Wutatsai) and analyzed the distribution and quantity differences of SSRs and SNPs among these variants, which provided important marker information for the construction of high-density genetic maps and conducted comparative genomics among non-heading Chinese cabbage variants.

3.1.4. Discovery of Specific Genes

Currently, novel molecular markers derived from functional genes play a crucial role in improving agronomic characteristics such as yield, quality, disease resistance, and abiotic stress tolerance. For instance, Kim et al. [183] developed functional markers for Ty-2 and Ty-3 resistance in yellow leaf curl virus-resistant tomato varieties, with two InDels and three CAPS markers, which were developed for Ty-2 and Ty-3, respectively, to aid in the integration of Ty-2 and Ty-3 genes into high-performing tomato cultivars. Similarly, Qing et al. [184] developed the InDel marker “MM28T” for the brown planthopper (BPH) resistance gene (Bph3) in rice to produce BPH-resistance rice cultivars and reduce damage caused by BPH. Furthermore, Kroc et al. [185] developed the co-dominant-derived cleavage amplification polymorphic sequence (dCAPS) marker “iuc_RAP2-7”, utilizing SNPs in RAP2-7: a candidate gene linked to the seed alkaloid content in narrow-leafed lupin (Lupinus angustifolius L.) to produce low-alkaloid cultivars. Additionally, Liu et al. [186] and Wang et al. [187] developed SSR and Indel markers using the whole-genome sequencing of 9930 and re-sequencing data of 100 or 115 core cucumber germplasms in the primary mapping region of the black spine color gene or heavy netting gene, respectively. Their study revealed that the black fruit spine trait and the heavy netting trait were controlled by one dominant nuclear gene, (B) and (H), respectively, that were located on chromosomes 4 and 5 in cucumber. These findings were crucial for the fine mapping and gene cloning of B and H genes in cucumbers.
Collectively, these novel molecular markers provide an effective approach for classifying germplasm resources, conducting genetic research, identifying the specific genes affecting important traits, and facilitating marker-assisted breeding.

3.2. Identification of Disease or Pest Resistance

Core collections, which represent the entire genetic diversity of germplasm resources, have been shown to improve the efficiency of identifying disease or pest-resistant accessions or genes. In recent years, many researchers have conducted studies on disease or pest resistance in crop core collections, and a list of some of the more common disease or pest-resistant QTLs/genes identified is provided in Table 2.
Mini-core collections of various crops have been evaluated to identify sources of resistance against different diseases or pests. For instance, in the mungbean, three accessions (VI001509AG, VI001400AG, and VI001244AG) were found to be resistant to most isolates of dry root rot out of 296 evaluated accessions [199]. In pigeonpea, six, twenty-four, and five accessions were found to be resistant to fusarium wilt and sterility mosaic disease, with combined resistance, respectively, out of 146 evaluated accessions [203]. In the cowpea, three genotypes (TVu-6464, TVu-1583, and TVu-15445) were found to be resistant to Aphis craccivora Koch [200]. In watermelon, Yang et al. [209] analyzed the genetic variation and relationships of 35 core collections and identified one SRAP locus that was associated with fusarium wilt resistance.
Furthermore, GWAS studies have also been conducted to identify novel sources of disease resistance by exploiting a natural variation in crop core collections. For example, Fu et al. [188] identified sheath blight disease resistance in Ting’s rice core collection of 150 varieties and discovered 13 resistance gene-based SNPs from approximately five million SNPs. A core set of 121 bread wheat accessions were evaluated for their resistance against the tan spot and the stagonospora nodorum blotch (SNB); then, ten, five, and five genomic regions were associated with resistance to tan spot race 1, race 5, and SNB, respectively [191]. In the common bean core collection, Binagwa et al. [196] identified several SNPs which were responsible for powdery mildew disease resistance, revealing four putative resistance genes. Shi et al. [197] evaluated 315 accessions for soybean cyst nematode disease resistance and observed 11 SNPs in HG Type 0 responsive chromosomes. In the African core groundnut collection, 32 SNPs were associated with the detected groundnut rosette disease resistance [203], two of which were located on the exons of a putative TIR-NBS-LRR disease resistance gene.
In conclusion, core collections provide a valuable genetic resource for the identification of disease-resistant QTLs/genes, which can greatly aid in disease-resistance breeding efforts and may even lead to gene editing and the introduction of novel alleles for single or multiple disease resistance in various crops.

3.3. Gene Discovery and Allele Mining

Core collections are valuable genetic resources for the identification of elite genes and mining alleles. Table 3 provides information on the characteristics of marker-trait associations in the core collections of representative crops.
Many genes that affect agronomic traits have been discovered in rice core collections [226,227,228,229]; these include Rc, waxy, and GS3 genes, which were found to be associated with grain color, amylose content, and grain length, respectively [230]. In addition, 65 QTLs and four candidate genes (LOC_Os06g47720, LOC_Os06g47820, LOC_Os06g47850, LOC_Os06g47970) were identified for salt tolerance [212]. Soybean core collections have also been genotyped using SNP markers to study linkage disequilibrium and associations with agronomic, yield and yield components; 12 genes, 19 SNPs, and five haplotypes were identified and linked to these traits [216,217]. QTLs in relation to low temperature, salt tolerance, and drought stress were also identified [218,219,220]. Additionally, genes that influenced upland cotton fiber quality, such as, GhCIP1 and GhUCE for days to flowering, GhFL1 and GhFL2 for fiber length, and Gh_A07G1769 for fiber strength, were discovered [222]. In vegetable and fruit core collections, various genes controlling key fruit traits have been identified. For example, Qi et al. [231] conducted deep resequencing on 115 cucumber accessions to generate a genetic map and revealed the domestication and diversity of cucumbers; they identified ~3.6 million variants, including a natural genetic variant in a β-carotene hydroxylase gene that could improve the nutritional value of cucumbers. Lin et al. [223] constructed a genetic map of tomato genome variation based on the genome sequences of 360 core accessions and discovered two independent QTLs (fw2. 2 and lcn2. 1), which resulted in modern tomato fruits that were 100 times larger than their wild counterparts. They also identified a key causative variant site in the promoter region of the SlMYB12 gene that conferred the pink fruit color. Moreover, studies on melons have identified a candidate white-flesh gene (CmPPR1) that affects fruit flesh color and a ThAT gene (CmThAT1) that mediates thioester production [224]. In Louisiana, sugarcane core collection analysis has discovered 56 markers to be consistent with cane yield components and sucrose traits [226]. Guo et al. [232] reported a high-quality draft genome sequence of the east Asia watermelon cultivar ‘97103’ and resequenced 20 accessions of watermelon from three different subspecies(lanatus, Mucosospermus and vulgaris), revealing the loss of many disease resistance genes during domestication and identifying several genes that were related to valuable fruit-quality traits, including sugar accumulation and citrulline.
In summary, functional genes or markers can improve the efficiency and precision of selecting desirable traits and aid in accumulating favorable alleles for high-yield crop breeding. Further work is necessary to mine more underlying genes, manipulating the desired traits.

4. Conclusions and Perspectives

Considering the current status of core cultivation, the following aspects deserve attention.

4.1. Effective Data Integration and Accurate Phenotyping Methods

The use of molecular markers in conjunction with phenotypic and passport data are becoming increasingly common in the establishment of core sets, but how to scientifically and effectively integrate these multiple types of data is still a problem faced by the construction of core collection, which needs more attention. In addition, phenotyping is the key to core collection success construction, as phenotypes are regarded as the most reliable indicators for genotypes. Therefore, it is necessary to improve phenotyping tools to enhance the efficiency of phenotyping.

4.2. Appropriate Sampling Strategy and Software

Optimizing the sampling strategy is crucial for constructing a core collection that is both representative and effective. Moreover, the successful construction of the core collection has been accompanied by the development of different software Hence, on the basis of mastering multiple types of data, the type of sampling strategies and software to use has become a hot spot and focus of future core collection research.

4.3. Broaden the Scope of Core Collection

Currently, as the number of endangered tree species and low-quality tree species gradually increases, there is an urgent need to collect and establish a core collection for the protection and effective utilization of germplasm resources. However, core collections are mainly concentrated on grain crops or horticultural plants. Few studies have focused on afforestation and endangered tree species: this is especially true for bamboo species. The development of core collections for timber trees and bamboo, especially those native and special species in China, is a top priority for the near future.

4.4. Identify Abundant Functional Genes and Markers

The core collection is essentially the gene bank of a species, and one of the most important goals in constructing core sets is to explore and utilize high-quality genes, such as those for high yield, good quality, resistance to disease and pests and stress tolerance. Currently, the widespread application of second-generation sequencing technologies has made it possible to re-sequence every resource in the core collection. Considering the wide variety and rich genetic diversity of plants that exist, in the future, we should make full use of genome sequencing information to develop large-sample, low-abundance genotyping methods and technologies and aim to improve the efficiency of molecular markers and gene discovery based on the core collection.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/f14050926/s1, Table S1: Core collection construction of partial plant species.

Author Contributions

Conceptualization, S.F. and S.W.; methodology, S.W.; formal analysis, S.Z.; data curation, J.L.; writing—original draft preparation, R.G.; writing—review and editing, R.G. and G.L.; visualization, G.L.; supervision, G.L.; project administration, G.L.; funding acquisition, G.L. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported financially by Important bamboo and rattan germplasm biomass formation geographical differentiation, the National Key Research and Development Project of China of the 14th Five-Year Plan (Grant Number 2021YFD2200501).

Data Availability Statement

The data are included in the article or Supplemental Materials.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Liu, Y.L.; Geng, Y.P.; Xie, X.D.; Zhang, P.F.; Hou, J.L.; Wang, W. Core collection construction and evaluation of the genetic structure of Glycyrrhiza in China using markers for genomic simple sequence repeats. Genet. Resour. Crop Evol. 2020, 67, 1839–1852. [Google Scholar] [CrossRef]
  2. Belaj, A.; del Carmen Dominguez-García, M.; Atienza, S.G.; Urdíroz, N.M.; De la Rosa, R.; Satovic, Z.; Martín, A.; Kilian, A.; Trujillo, I.; Valpuesta, V.; et al. Developing a core collection of olive (Olea europaea L.) based on molecular markers (DArTs, SSRs, SNPs) and agronomic traits. Tree Genet. Genomes 2012, 8, 365–378. [Google Scholar] [CrossRef]
  3. Van Hintum, T.J.L.; Brown, A.H.D.; Spillane, C.; Hodgkin, T. Core Collections of Plant Genetic Resources; IPGRI Technical Bulletin No. 3; International Plant Genetic Resources Institute: Rome, Italy, 2000. [Google Scholar]
  4. Brown, A.H.D. Core collections—A practical approach to genetic-resources management. Genome 1989, 31, 818–824. [Google Scholar] [CrossRef]
  5. Brown, A.H.D. The case for core collections. In The Use of Plant Genetic Resources; Brown, A.H.D., Frankel, O.H., Marshal, D.R., Eds.; University Cambridge Press: Cambridge, UK, 1989; pp. 136–156. [Google Scholar]
  6. Frankel, O.H. Genetic perspectives of germplasm conservation. In Genetic Manipulation: Impact on Man and Society; Arber, W.K., Illmensee, K., Peacock, W.J., Starlinger, P., Ehrlich, S.D., Dagert, M., Romac, S., Michel, B., Levy, S.B., Goebel, W., et al., Eds.; Cambridge University Press: Cambridge, UK, 1984; pp. 161–170. [Google Scholar]
  7. Li, Z.C.; Zhang, H.L.; Cao, Y.S.; Qiu, Z.E.; Wei, X.H.; Tang, S.X.; Yu, P.; Wang, X.K. Studies on the sampling strategy for primary rice. Acta Agron. Sin. 2003, 29, 20–24. [Google Scholar]
  8. Odong, T.L.; Jansen, J.; van Eeuwijk, F.A.; van Hintum, T.J.L. Quality of core collections for effective utilisation of genetic resources review, discussion and interpretation. Theor. Appl. Genet. 2013, 126, 289–305. [Google Scholar] [CrossRef] [PubMed]
  9. Balfourier, F.; Roussel, V.; Strelchenko, P.; Exbrayat-Vinson, F.; Sourdille, P.; Boutet, G.; Koenig, J.; Ravel, C.; Mitrofanova, O.; Beckert, M.; et al. A worldwide bread wheat core collection arrayed in a 384-well plate. Theor. Appl. Genet. 2007, 11, 1265–1275. [Google Scholar] [CrossRef] [PubMed]
  10. Richards, C.M.; Volk, G.M.; Reeves, P.A.; Reilley, A.A.; Henk, D.; Forsline, P.L.; Aldwinckle, H.S. Selection of stratified core sets representing wild apple (Malus sieversii). J. Am. Soc. Hortic. Sci. 2009, 134, 228–235. [Google Scholar] [CrossRef]
  11. Razieh, M.; Mohammad, R.D.; Darab, H.; Mehrshad, Z.; Elisa, V.; Sabrina, M.; Fariborz, Z.N. Development of a core collection in Iranian walnut (Juglans regia L.) germplasm using the phenotypic diversity. Sci. Hortic. 2019, 249, 439–448. [Google Scholar]
  12. Li, X.X.; Fang, Z.Y. From the research of core germplasm, the mining and utilization of excellent genes of crops are carried out. China Veg. 2005, S1, 1–7. (In Chinese) [Google Scholar]
  13. Jiang, M. Researches on Constructing Strategy of Core Germplasm Construction Based on Phenotype and Genetic Diversity of Buchloe dactyloides (Nutt.) Engelm. Master’s Thesis, Chinese Academy of Forestry, Beijing, China, 2020. [Google Scholar]
  14. Huang, C.Q.; Long, T.; Bai, C.J.; Wang, W.Q.; Tang, J.; Liu, G.D. Establishment of a core collection of Cynodon based on morphological data. Trop. Grassl.-Forrajes Trop. 2020, 8, 203–213. [Google Scholar] [CrossRef]
  15. Zhou, Y.C. Genetic Diversity Analysis and Construction of Core Germplasm in Bromus Inermis Leyss. Ph.D. Thesis, Northeast Normal University, Changchun, China, 2020. [Google Scholar]
  16. Wu, H.; Duan, A.; Wang, X.; Chen, Z.; Zhang, X.; He, G.; Zhang, J. Construction of a Core Collection of Germplasms from Chinese Fir Seed Orchards. Forests 2023, 14, 305. [Google Scholar] [CrossRef]
  17. Guo, Q.; Liu, J.; Li, J.K.; Cao, S.; Zhang, Z.J.; Zhang, J.T.; Zhang, Y.S.; Deng, Y.P.; Niu, D.S.; Su, L.Z.; et al. Genetic diversity and core collection extraction of Robinia pseudoacacia L. germplasm resources based on phenotype, physiology, and genotyping markers. Ind. Crops Prod. 2022, 178, 114627. [Google Scholar] [CrossRef]
  18. Mao, X.H.; Zhu, S.L.; Li, S.W.; Hua, H.; Tian, S.Y.; Zhong, W.G.; Dong, Y.F.; An, X.M. Core germplasm construction of Populus tomentosa based on the fluorescent SSR markers. J. Beijing For. Univ. 2020, 42, 40–47. (In Chinese) [Google Scholar]
  19. Feng, Y.; Yang, Z.; Tan, J.; Li, H.; Chen, X. Selection of first generation nucleus population of Pinus massoniana in Guangxi. J. Northeast For. Univ. 2018, 46, 20–24. [Google Scholar]
  20. Shashidhara, G.; Hema, M.V.; Koshy, B.; Farooqi, A.A. Assessment of genetic diversity and identification of core collection in sandalwood germplasm using RAPDs. J. Hortic. Sci. Biotechnol. 2003, 78, 528–536. [Google Scholar] [CrossRef]
  21. Li, R.; Shang, X.; Shang, C.S.; Chang, L.F.; Yan, L.; Bai, J.R. Core germplasm construction of Shanxi maize inbred line based on SSR markers. Seed 2021, 9, 72–76. (In Chinese) [Google Scholar]
  22. Chang, L.F.; Bai, J.R.; Li, R.; Zhang, C.Z.; Zhang, X.M.; Yang, R.J. Construction of a Core Collection of Sweet Corn Populations Based on SSR Markers. J. Maize Sci. 2018, 26, 40–49. (In Chinese) [Google Scholar]
  23. Li, Y.; Shi, Y.S.; Cao, Y.S.; Wang, T.Y. Establishment of a core collection for maize germplasm preserved in Chinese National Genebank using geographic distribution and characterization data. Genet. Resour. Crop Evol. 2004, 51, 845–852. [Google Scholar] [CrossRef]
  24. Ronaldo, R.C.; Glauco, V.M.; Cosme, D.C. Development of a Brazilian maize core collection. Genet. Mol. Biol. 2009, 32, 538–545. [Google Scholar]
  25. Li, M.; Qin, H.B.; Wang, Y.N.; Mu, Z.X.; Du, H.L. A Core Collection of Sorghum Landraces Formed by Taking Use of Agronomic Traits in Shanxi Province. J. Plant Genet. Resour. 2021, 22, 174–182. (In Chinese) [Google Scholar]
  26. Li, X.S.; Fu, Y.H.; Zhou, X.; Li, Q.; Liu, F.Z.; Yang, C.L.; Zhou, M.Q. Establishment of Coix lacryma-jobi L. Core Germplasm Collection Based on Phenotypic Characters. Chin. J. Trop. Crops 2020, 41, 669–675. (In Chinese) [Google Scholar]
  27. Yuan, H.J.; Zeng, X.Q.; Xu, Q.J.; Wang, Y.L.; Zhang, S.; Nyima, T.S. Genetic diversity of germplasm resources and core construction development in Hulless Barley. J. Triticeae Crops 2018, 38, 922–928. (In Chinese) [Google Scholar]
  28. Zhang, H.L.; Zhang, D.L.; Wang, M.X.; Sun, J.L.; Qi, Y.W.; Li, J.J.; Wei, X.H.; Han, L.Z.; Qiu, Z.E.; Tang, S.X.; et al. A core collection and mini core collection of Oryza sativa L. in China. Theor. Appl. Genet. 2011, 122, 49–61. [Google Scholar] [CrossRef]
  29. Gao, X.F.; Zhou, Y.; Song, D.Y.; Li, J.X.; Chen, W.; Li, S.P. Construction of Core Collection of Chinese Aegilops tauschii Coss. Germplasm Resource Based on Spike Morphological Traits and Molecular Markers. J. Plant Genet. Resour. 2021, 22, 361–370. (In Chinese) [Google Scholar]
  30. Boczkowska, M.; Lapinski, B.; Kordulasinska, I.; Dostatny, D.F.; Czembor, J.H. Promoting the use of common oat genetic resources through diversity analysis and core collection construction. PLoS ONE 2016, 11, e0167855. [Google Scholar] [CrossRef]
  31. Li, J.L.; Fan, Y.; Zhao, M.Y. Construction of Primary Core Collection of Buckwheat Germplasm Resources Based on Phenotypic Traits and SSR. J. Plant Genet. Resour. 2021, 22, 1240–1247. (In Chinese) [Google Scholar]
  32. Bhattacharjee, R.; Khairwal, I.S.; Bramel, P.J.; Reddy, K.N. Establishment of a pearl millet [Pennisetum glaucum (L.) R. Br.] core collection based on geographical distribution and quantitative traits. Euphytica 2007, 155, 35–45. [Google Scholar] [CrossRef]
  33. Choi, Y.M.; Kim, K.M.; Lee, S.; Oh, S.; Lee, M.C. Development of a core collection based on EST-SSR markers and phenotypic traits in foxtail millet [Setaria italica (L.) P. Beauv.]. J. Crop Sci. Biotechnol. 2019, 21, 395–405. [Google Scholar] [CrossRef]
  34. Upadhyaya, H.D.; Bramel, P.J.; Ortiz, R.; Singh, S. Developing a mini core of peanut for utilization of genetic resources. Crop Sci. 2002, 42, 2150–2156. [Google Scholar] [CrossRef]
  35. Su, W.; Wang, L.; Lei, J.; Chai, S.; Liu, Y.; Yang, Y.; Yang, X.; Jiao, C. Genome-wide assessment of population structure and genetic diversity and development of a core germplasm set for sweet potato based on specific length amplified fragment (SLAF) sequencing. PLoS ONE 2017, 12, e0172066. [Google Scholar] [CrossRef]
  36. Oliveira, E.J.; Ferreira, C.F.; Santos, V.S.; Oliveira, G.A.F. Development of a cassava core collection based on single nucleotide polymorphism markers. Genet. Mol. Res. 2014, 13, 6472–6485. [Google Scholar] [CrossRef] [PubMed]
  37. Upadhyaya, H.D.; Bramel, P.J.; Singh, S. Development of a chickpea core subset using geographic distribution and quantitative traits. Crop Sci. 2001, 41, 206–210. [Google Scholar] [CrossRef]
  38. Reddy, L.J.; Upadhyaya, H.D.; Gowda, C.L.L.; Singh, S. Development of core collection in pigeonpea [Cajanus cajan (L.) Millspaugh] using geographic and qualitativemorphological descriptors. Genet. Resour. Crop Evol. 2005, 52, M1049–M1056. [Google Scholar] [CrossRef]
  39. Regina, L.F.G.; Marcones, F.C.; Alessandro, A.-P. A lima bean core collection based on molecular markers. Sci. Agric. 2020, 77, e20180140. [Google Scholar]
  40. Wang, L.X.; Guan, Y.; Guan, R.X.; Li, Y.; Ma, Y.; Dong, Z.; Liu, X.; Zhang, H.; Zhang, Y.; Liu, Z.; et al. Establishment of Chinese soybean (Glycine max) core collections with agronomic traits and SSR markers. Euphytica 2006, 151, 215–223. [Google Scholar] [CrossRef]
  41. Jeong, N.; Kim, K.S.; Jeong, S.; Kim, J.Y.; Park, S.K.; Lee, J.S.; Jeong, S.C.; Kang, S.T.; Ha, B.K.; Kim, D.Y.; et al. Korean soybean core collection: Genotypic and phenotypic diversity population structure and genomewide association study. PLoS ONE 2019, 14, e0224074. [Google Scholar] [CrossRef]
  42. Wang, L.X.; Cheng, X.Z.; Wang, S.H. Genetic Diversity Analysis and a Core Collection of Rice Bean (Vigna umbellata) in China. J. Plant Genet. Resour. 2014, 15, 242–247. (In Chinese) [Google Scholar]
  43. Hao, X.P.; Wang, Y.; Tian, X. Construction of Primary Core Collection of Common Bean (Phaseolus vulgaris L.) Based on Agronomic Traits in Shanxi Province. J. Plant Genet. Resour. 2016, 17, 815–823. (In Chinese) [Google Scholar]
  44. Jiang, J.Y.; Yang, T.; Wang, F.G. Diversity Analysis of Germplasm Resources and Construction of Mini-core Collections for Vicia faba L. at Home and Abroad. Acta Agron. Sin. 2014, 40, 1311–1319. (In Chinese) [Google Scholar] [CrossRef]
  45. Schafleitner, R.; Nair, R.M.; Rathore, A.; Wang, Y.W.; Lin, C.Y.; Chu, S.H.; Lin, P.Y.; Chang, J.C.; Ebert, A.W. The AVRDC-The World Vegetable Center mungbean (Vigna radiata) core and mini core collections. BMC Genom. 2015, 16, 0111. [Google Scholar] [CrossRef]
  46. Wang, M.M.; Sun, D.L.; Chen, R.; Yang, Y.; Zhang, G.; Lv, M.; Wang, Q.; Xie, T.; Niu, G.; Shan, X.; et al. Construction and Evaluation of Cauliflower Core Collection. Acta Hortic. Sin. 2023, 50, 421–431. (In Chinese) [Google Scholar]
  47. Zhang, G.G.; Liu, H.D.; Du, D.Z. Population structure analysis and core germplasm construction of Brassica napus restorer lines. Chin. J. Oil Crop Sci. 2022, 175, 1–9. (In Chinese) [Google Scholar]
  48. Li, L.; He, W.M.; Ma, L.P.; Liu, P.; Xu, H.; Xu, J.; Zheng, X. Construction Chinese Cabbage (Brassica rapa L.) Core Collection and its EST-SSR Fingerprint Database by EST-SSR Molecular Markers. Genom. Appl. Biol. 2009, 28, 76–88. (In Chinese) [Google Scholar]
  49. Zheng, F.S.; Wang, X.M.; Li, G.H. Core collection construction of Ningxia tomato germplasm resources based on phenotypic traits. J. Zhejiang Univ. 2021, 47, 171–181. (In Chinese) [Google Scholar]
  50. Deng, X.B.; Liu, L.; Yan, Z.; Li, T.; Liu, X.Y.; Feng, J.J.; Chi, H.J.; Zheng, Z.; Li, J.M. Development of a Core Collection of Processing Tomato Germplasms and Analysis of Genetic Background. Acta Hortic. Sin. 2015, 42, 1299–1312. (In Chinese) [Google Scholar]
  51. Peng, F.; Li, Y.; Dai, Y.R.; Wang, X.M.; Yang, J.; Wang, Q.H.; Cai, X.F. Construction of spinach’s core germplasms based on its phenotypic traits. J. Shanghai Norm. Univ. 2022, 51, 9–19. (In Chinese) [Google Scholar]
  52. Hoshikawa, K.; Lin, Y.P.; Schafleitner, R.; Shirasawa, K.; Isobe, S.; Nguyen, D.C.; Ohsawa, R.; Yoshioka, Y. Genetic diversity analysis and core collection construction for Amaranthus tricolor germplasm based on genome-wide single-nucleotide polymorphisms. Sci. Hortic. 2023, 307, 111428. [Google Scholar] [CrossRef]
  53. Cui, J.J.; Cheng, J.W.; Cao, Y. Studies on Construction of Bitter Gourd Core Collection Based on SSR Markers and Phenotypic Traits. China Veg. 2022, 2, 25–32. (In Chinese) [Google Scholar]
  54. Hou, Z.Q.; Wang, L.H.; Zhao, M.L.; Yang, S.P.; Sun, X.M.; Gao, J.M.; Zhong, Q.W. Preliminary Construction of Core Collection of Jerusalem Artichoke Based on Phenotypic Data. Mol. Plant Breed. 2021, 19, 3463–3472. (In Chinese) [Google Scholar]
  55. Yang, F.; Zhang, Y.F.; Wang, J.H. SSR analysis of Chinese yam (Dioscorea opposita Thunb.) germplasm resources and construction of primary core collection. J. North. Agric. 2022, 50, 18–27. (In Chinese) [Google Scholar]
  56. Wang, X.; Bao, K.; Reddy, U.K.; Bai, Y.; Hammar, S.A.; Jiao, C.; Wehner, T.C.; Ramírez-Madera, A.O.; Weng, Y.Q.; Grumet, R.; et al. The USDA cucumber (Cucumis sativus L.) collection: Genetic diversity, population structure, genome-wide association studies, and core collection development. Hortic. Res. 2018, 5, 64. [Google Scholar] [CrossRef]
  57. Ge, R.D. Construction of Pumpkin Core Germplasm for Anvils. Master’s Thesis, Nanjing Agricultural University, Nanjing, China, 2020. [Google Scholar]
  58. Jiao, X.X. Construction of Core Collections of Wax Gourd. Master’s Thesis, Guangxi University, Nanning, China, 2018. [Google Scholar]
  59. Lee, H.Y.; Ro, N.Y.; Jeong, H.J.; Kwon, J.K.; Jo, J.; Ha, Y. Genetic diversity and population structure analysis to construct a core collection from a large Capsicum germplasm. BMC Genet. 2016, 17, 142. [Google Scholar] [CrossRef] [PubMed]
  60. Liu, Z.J.; Cao, Z.M.; Zhu, J.; Shen, L.B. Comparative study on the construction of sweet pepper core collection. J. Northeast Agric. Univ. 2016, 47, 21–29. (In Chinese) [Google Scholar]
  61. Miyatake, K.; Shinmura, Y.; Matsunaga, H.; Fukuoka, H.; Saito, T. Construction of a core collection of eggplant (Solanum melongena L.) based on genome-wide SNP and SSR genotypes. Breed. Sci. Preview 2019, 69, 498–502. [Google Scholar] [CrossRef] [PubMed]
  62. Li, X.; Cui, L.; Zhang, L.; Huang, Y.; Zhang, S.; Chen, W.; Deng, X.; Jiao, Z.; Yang, W.; Qiu, Z.; et al. Genetic Diversity Analysis and Core Germplasm Collection Construction of Radish Cultivars Based on Structure Variation Markers. Int. J. Mol. Sci. 2023, 24, 2554. [Google Scholar] [CrossRef] [PubMed]
  63. Li, R.; Zhou, F.; Gao, Y.; Liu, C.; Yu, S.; Zhao, K.; Gong, W.; Lang, J.; Zhang, H.; Yu, X. Genetic Diversity and Primary Core Collection Construction of Turnip (Brassica rapa L. ssp. rapifera Matzg) Landraces in Tibet Revealed via Morphological and SSR Markers. Agronomy 2021, 11, 1901. [Google Scholar] [CrossRef]
  64. Li, J.; Liu, X.B.; Zhao, Z.W.; Yang, Z.L. Genetic diversity, core collection and breeding history of Pleurotus ostreatus in China. Mycoscience 2019, 60, 14–24. [Google Scholar] [CrossRef]
  65. Sa, K.J.; Kim, D.M.; Oh, J.S.; Park, H.; Hyun, D.Y.; Lee, S.; Rhee, J.H.; Lee, J.K. Construction of a core collection of native Perilla germplasm collected from South Korea based on SSR markers and morphological characteristics. Sci. Rep. 2021, 11, 23891. [Google Scholar] [CrossRef]
  66. Xu, Y.; Chen, C.S.; Ji, D.H.; Xu, K.; Xie, X.X.; Xie, C.T. Developing a core collection of Pyropia haitanensis using simple sequence repeat markers. Aquaculture 2016, 452, 351–356. [Google Scholar] [CrossRef]
  67. Sun, Y.Q.; Chen, J.H.; Zhang, J. Construction A Core Collection of Armeniaca sibirica Based on Phenotypic Traits. J. Shenyang Agric. Univ. 2022, 53, 43–54. (In Chinese) [Google Scholar]
  68. Liu, J.; Liao, K.; Zhao, S.R. The Core Collection Construction of Xinjiang Wild Apricot Based on ISSR Molecular Markers. Sci. Agric. Sin. 2015, 48, 2017–2028. (In Chinese) [Google Scholar]
  69. Wang, Y.Z.; Zhang, J.H.; Sun, H.Y.; Ning, N. Construction and evaluation of a primary core collection of apricot germplasm in China. Sci. Hortic. 2011, 128, 311–319. [Google Scholar] [CrossRef]
  70. Sun, Y.; Dong, S.; Liu, Q.; Chen, J.; Pan, J.; Zhang, J. Selection of a core collection of Prunus sibirica L. germplasm by a stepwise clustering method using simple sequence repeat markers. PLoS ONE 2021, 16, e0260097. [Google Scholar] [CrossRef] [PubMed]
  71. Lu, M.; Zhang, H.; Bai, J. Genetic Diversity Analysis and Core Collection Construction in Rosa roxburghii Based on Fruits Quality and EST-SSR Markers. Mol. Plant Breed. 2020, 18, 3098–3106. (In Chinese) [Google Scholar]
  72. Bu, H.D.; Zhang, B.B.; Song, H.W.; Liang, Y.H.; Liu, Y.J.; Cheng, X.M.; Gu, G.J.; Liu, C. Construction Core Collections of Pear Germplasms in Cold Region by SSR and Phenotypic Traits. Acta Hortic. Sin. 2012, 39, 2113–2123. (In Chinese) [Google Scholar]
  73. Sun, Y.Q. Genetic Diversity Analysis and Core Collection Construction of Ziziphus jujuba var. spinosa (Bunge) Hu ex H. F. Chow. Master’s Thesis, Tarim University, Aral, China, 2016. [Google Scholar]
  74. Xu, C.; Gao, J.; Du, Z.; Li, D.; Wang, Z.; Li, Y.; Pang, X. Identifying the genetic diversity, genetic structure and a core collection of Ziziphus jujube Mill. Var. jujube accessions using microsatellite markers. Nat. Sci. Rep. 2016, 6, 31503. [Google Scholar]
  75. Sivalingam, P.N.; Singh, D.; Chauhan, S.; Changal, H.K.; Bhan, C.; Mohapatra, T.; More, T.A.; Sharma, S.K. Establishment of the core collection of Ziziphus mauritiana Lam. from India. Plant Genet. Resour. 2013, 12, 140–142. [Google Scholar] [CrossRef]
  76. Le Cunff, L.; Fournier-Level, A.; Laucou, V.; Vezzulli, S.; Lacombe, T.; Adam-Blondon, A.F.; Boursiquot, J.-M.; This, P. Construction of nested genetic core collections to optimize the exploitation of natural diversity in Vitis vinifera L. subsp. sativa. BMC Plant Biol. 2008, 8, 31. [Google Scholar] [CrossRef]
  77. Hu, J.; Wang, P.; Su, Y.; Wang, R.; Li, Q.; Sun, K. Microsatellite diversity, population structure, and core collection formation in Melon germplasm. Plant Mol. Biol. Rep. 2015, 33, 439–447. [Google Scholar] [CrossRef]
  78. Pal, S.; Revadi, M.; Thontadarya, R.N.; Reddy, D.C.L.; Varalakshmi, B.; Pandey, C.D.; Rao, E.S. Understanding genetic diversity, population structure and development of a core collectionof Indian accessions of watermelon (Citrullus lanatus (Thunb.) Matsum and Nakai). Plant Genet. Resour. Charact. Util. 2020, 18, 359–368. [Google Scholar] [CrossRef]
  79. Hu, G.G.; Jiang, Q.; Wang, Z. Genetic Diversity Analysis and Core Collection Construction of the Actinidia chinensis Complex (Kiwifruit) Based on SSR Markers. Agronomy 2022, 12, 3078. [Google Scholar] [CrossRef]
  80. Razi, S.; Soleimani, A.; Zeinalabedini, M.; Vazifeshenas, M.R.; Martinez-Gomez, P.; Kermani, A.M.; Raiszadeh, A.R.; Tayari, M.; Martinez-Garcia, P.J. Development of a Multipurpose Core Collection of New Promising Iranian Pomegranate (Punica granatum L.) Genotypes Based on Morphological and Pomological Traits. Horticulturae 2021, 7, 350. [Google Scholar] [CrossRef]
  81. Huang, X.F.; Wei, Y.L.; Yuan, Y. Genetic diversity analysis and core collection construction of 221 cultivars (strains) of Litchi chinensis based on SNP molecular markers. J. Plant Resour. Environ. 2022, 31, 74–84. (In Chinese) [Google Scholar]
  82. Sun, Q.; Bai, L.; Ke, L.; Xiang, X.; Zhao, J.; Ou, L. Developing a core collection of litchi (Litchi chinensis Sonn.) based on EST-SSR genotype data and agronomic traits. Sci. Hortic. 2012, 146, 29–38. [Google Scholar] [CrossRef]
  83. Dervishi, A.; Jakse, J.; Ismaili, H.; Javornik, B.; Stajner, N. Genetic structure and core collection of Olive germplasm from Albania revealed by microsatellite markers. Genes 2021, 12, 256. [Google Scholar] [CrossRef]
  84. Zhang, C.Y.; Chen, X.S.; Zhang, Y.M.; Yuan, Z.H.; Liu, Z.C.; Wang, Y.L.; Lin, Q. Method of Constructing Core Collection for Malus sieversii in Xinjiang, China Using Molecular Markers. Agric. Sci. China 2009, 8, 267–284. [Google Scholar] [CrossRef]
  85. Liang, W.; Dondini, L.; Franceschi, P.D.; Paris, R.; Sansavini, S.; Tartarini, S. Genetic diversity, population structure and construction of a core collection of apple cultivars from Italian germplasm. Plant Mol. Biol. Rep. 2015, 33, 458–473. [Google Scholar] [CrossRef]
  86. Kim, J.H.; Oh, Y.; Lee, G.A.; Kwon, Y.S.; Kim, S.A.; Kwon, S.I.; Do, Y.S.; Choi, C. Genetic Diversity, Structure, and Core Collection of Korean Apple Germplasm Using Simple Sequence Repeat Markers. Hortic. J. 2019, 88, 329–337. [Google Scholar] [CrossRef]
  87. Li, Y.X.; Gao, Q.J.; Li, T.H. Sampling strategy based on fruit char acteristics for a primary core collection of peach cultivars. J. Fruit Sci. 2006, 23, 359–364. [Google Scholar]
  88. Escribano, P.; Viruel, M.A.; Hormaza, J.I. Comparison of different methods to construct a core germplasm collection in woody perennial species with simple sequence repeat markers. A case study in cherimoya (Annona cherimola, Annonaceae), an underutilised subtropical fruit tree species. Ann. Appl. Biol. 2008, 153, 25–32. [Google Scholar] [CrossRef]
  89. Balas, F.C.; Osuna, M.D.; Domínguez, G.; Pérez-Gragera, F.; López-Corrales, M. Ex situ conservation of underutilised fruit tree species: Establishment of a core collection for Ficus carica L. using microsatellite markers (SSRs). Tree Genet. Genomes 2014, 10, 703–710. [Google Scholar] [CrossRef]
  90. Campoy, J.A.; Lerigoleur-Balsemin, E.; Christmann, H.; Beauvieux, R.; Girollet, N.; Quero-Garcia, J.; Dirlewanger, E.; Barreneche, T. Genetic diversity, linkage disequilibrium, population structure and construction of a core collection of Prunus avium L. landraces and bred cultivars. BMC Plant Biol. 2016, 16, 49. [Google Scholar] [CrossRef]
  91. Liu, Y.; Sun, Z.; Liu, D.; Wu, B.; Zhou, Q. Screening the core collection of pomelo germplasm based on molecular marker. J. Fruit Sci. 2006, 23, 339–345. (In Chinese) [Google Scholar]
  92. Zhang, Y.F.; Zhang, Q.L.; Yang, Y.; Luo, Z.R. Development of primary core collection for Japanese persimmon originated in China (Diospyros kaki Thunb.) by stepwise clustering. Acta Hortic. 2007, 760, 6976. [Google Scholar]
  93. Liu, X.L.; Liu, H.B.; Ma, L.; Li, X.J.; Xu, C.H.; Su, H.S.; Ying, X.M.; Cai, Q.; Fan, Y.H. Construction of sugarcane hybrids core collection by using stepwise clustering sampling approach with molecular marker data. Acta Agron. Sin. 2014, 40, 1885–1894. [Google Scholar] [CrossRef]
  94. Chen, M.K.; Chen, L.; Sun, W.H.; Ma, S.H.; Lan, S.; Peng, D.H.; Liu, Z.J.; Ai, Y. Genetic Diversity Analysis and Core Collection of Cymbidium ensifolium Germplasm Resources. Acta Hortic. Sin. 2022, 49, 175–186. (In Chinese) [Google Scholar]
  95. Li, J.W.; Su, J.S.; Zhang, F.; Fang, W.M.; Guan, Z.Y.; Chen, S.M.; Chen, F.D. Construction of Core Collection of Traditional Chrysanthemum morifolium Based on Phenotypic Traits. Sci. Agric. Sin. 2021, 54, 3514–3526. (In Chinese) [Google Scholar]
  96. Zhao, L.M.; Li, J.W.; Zhang, F.; Su, J.S.; Fang, W.M.; Wang, H.B.; Jiang, J.F.; Chen, S.M.; Chen, F.D.; Guan, Z.Y. Construction of a Core Collection of Spray Cut Chrysanthemum Based on Phenotypic Data. Acta Hortic. Sin. 2022, 49, 2273–2284. (In Chinese) [Google Scholar]
  97. Ming, J.; Zhang, Q.X.; Lan, Y.P. Core collection of Prunus mume Sieb. et Zucc. J. Beijing For. Univ. 2005, 27, 65–69. (In Chinese) [Google Scholar]
  98. Hu, H. Studies on the Construction of Core Collection for Wintersweet (Chimonanthus praeco) and Its Vegetative Propagation Technology. Master’s Thesis, Southwest University, Chongqing, China, 2020. [Google Scholar]
  99. Jiang, L.Y. The Wild Germplasm Resources Evaluation and Core Collection Establishment of Rosa rugosa. Master’s Thesis, Shandong Agricultural University, Tai’an, China, 2018. [Google Scholar]
  100. Li, M.Q.; Shi, G.Y.; Ye, S.H.; Huang, Y.W.; Bian, X.R. Methods of establishing Lanzhou lily core collection based on ISSR molecular marker data. J. Desert Res. 2015, 35, 1573–1578. (In Chinese) [Google Scholar]
  101. Li, B.Y. Studies on Genetic Diversity and Construction of Core Collection of Tree Peony Cultivars from Chiese Central Plains. Master’s Thesis, Beijing Forestry University, Beijing, China, 2007. [Google Scholar]
  102. Wang, Z.Y.; Li, Z.F.; Peng, C.; Chen, Y.; Zhang, X.Y. Construction of Lagerstroemia indica Core Collection based on SSR Markers and Comparison of Sampling Strategies. Mol. Plant Breed. 2022, 1, 1–13. (In Chinese) [Google Scholar]
  103. Wang, L.; Wang, J.M.; Wang, W.; Wang, L.; Wang, L.J.; Yan, X.C.; Tan, M.L. Development of a core collection in sunflower (Helianthus annuus L.) germplasm using phenotypic diversity. Chin. J. Oil Crop Sci. 2021, 43, 1052–1060. (In Chinese) [Google Scholar]
  104. Yang, L.; Li, H.; Guo, Q.Q. Construction of core collections of Sophora moorcroftiana endemic to the Qinghai–Tibet Plateau. Plant Physiol. J. 2022, 58, 1133–1144. (In Chinese) [Google Scholar]
  105. Li, J.H.; Ou, X.H.; Deng, W.J.; Luo, K.K.; Zhang, H.Y.; He, M.L.; Yan, H.J. Construction of core germplasm bank of Fallopia multiflora using SRAP molecular markers. Guihaia 2021, 41, 1920–1930. (In Chinese) [Google Scholar]
  106. Gong, F.S.; Geng, Y.P.; Zhang, P.F.; Zhang, F.; Fan, X.F.; Liu, Y.L. Genetic Diversity and Structure of Core Collection of Huangqi (Astragalus) Developed by Genomic Simple Sequence Repeat Markers. Res. Sq. 2022, 1, 1–12. [Google Scholar]
  107. Liu, R.X. Study on Construction of Scutellaria baicalensis Core Collection and Screening of Excellent Germplasm. Master’s Thesis, Beijing University of Chinese Medicine, Beijing, China, 2016. [Google Scholar]
  108. Liu, M.; Hu, X.; Wang, X.; Zhang, J.; Peng, X.; Hu, Z.; Liu, Y. Constructing a Core Collection of the Medicinal Plant Angelica biserrata Using Genetic and Metabolic Data. Front. Plant Sci. 2020, 11, 600249. [Google Scholar] [CrossRef] [PubMed]
  109. Yang, M.L. The Germplasm Evaluation and Core Collection Construction of Cornus officinalis Based on ISSR Markers. Master’s Thesis, Henan Agricultural University, Zhengzhou, China, 2016. [Google Scholar]
  110. Liu, F.M.; Zhang, N.N.; Liu, X.J.; Yang, Z.J.; Jia, H.Y.; Xu, D.P. Genetic diversity and population structure analysis of Dalbergia Odorifera germplasm and development of a core collection using microsatellite markers. Genes 2019, 10, 281. [Google Scholar] [CrossRef]
  111. Niu, S.; Koiwa, H.; Song, Q.; Qiao, D.; Chen, J.; Zhao, D.; Chen, Z.; Wang, Y.; Zhang, T. Development of core collections for Guizhou tea genetic resources and GWAS of leaf size using SNP developed by genotyping-by-sequencing. PeerJ 2020, 8, e8572. [Google Scholar] [CrossRef] [PubMed]
  112. Zhao, Z.F. Analysis of Genetic Diversity and Core Collection Construction of Cultivated Tea Germplasm Resources in Guizhou. Master’s Thesis, Guizhou University, Guizhou, China, 2022. [Google Scholar]
  113. Wang, X.C.; Chen, L.; Yang, Y.J. Establishment of core collection for Chinese tea germplasm based on cultivated region grouping and phenotypic data. Front. Agric. China 2011, 5, 344–350. [Google Scholar] [CrossRef]
  114. Leroy, T.; de Bellis, F.; Legnate, H.; Musoli, P.; Kalonji, A.; Loor Solórzano, R.G.; Cubry, P. Developing core collections to optimize the management and the exploitation of diversity of the coffee Coffea canephora. Genetica 2014, 142, 185–199. [Google Scholar] [CrossRef]
  115. Martínez, I.B.; de la Cruz, V.M.; Nelson, M.R.; Bertin, P. Establishment of a core collection of traditional Cuban Theobroma cacao plants for conservation and utilization purposes. Plant Mol. Biol. Report. 2014, 35, 47–60. [Google Scholar] [CrossRef]
  116. Qian, Y.Y.; Liu, Y.; Cui, S.F.; Wang, G.G.; Zhang, X.; Jin, W.P.; Li, J.L. Analysis of Genetic Diversity of Cotton Germplasm Resources and Extraction of Core Germplasm Based on Phenotypic Traits. Acta Agric. Boreali-Simiga 2019, 34, 29–35. (In Chinese) [Google Scholar]
  117. Han, P.; Tian, X.M.; Wang, Y.; Huang, C.; Ma, Y.Z.; Zhou, X.F.; Yu, Y.; Zhang, D.W.; Xu, H.J.; Cao, Y.; et al. Construction of a core germplasm bank of upland cotton (Gossypium hirsutum L.) based on phenotype, genotype and favorable alleles. Genet. Resour. Crop Evol. 2022, 69, 2399–2411. [Google Scholar] [CrossRef]
  118. Mei, Y.J.; Zhou, J.P.; Xu, H.M. Development of Sea Island cotton (Gossypium barbadense L.) core collection using genotypic values. AJCS 2012, 6, 673–680. [Google Scholar]
  119. Luan, M.B.; Zou, Z.Z.; Zhu, J.J. Development of a core collection for ramie by heuristic search based on SSR markers. Biotechnol. Biotechnol. Equip. 2014, 28, 798–804. [Google Scholar] [CrossRef] [PubMed]
  120. Kumar, S.; Ambreen, H.; Variath, M.T.; Rao, A.R.; Agarwal, M.; Kumar, A.; Goel, S.; Jagannath, A. Utilization of molecular, phenotypic, and geographical diversity to develop compact composite core collection in the Oilseed Crop, Safflower (Carthamus tinctorius L.) through maximization strategy. Front. Plant Sci. 2016, 7, 1554. [Google Scholar] [CrossRef] [PubMed]
  121. Liu, Y.Y.; Mei, H.X.; Du, Z.W.; Wu, K.; Zheng, Y.; Cui, X.; Zheng, L. Construction of Core Collection of Sesame Based on Phenotype and Molecular Markers. Sci. Agric. Sin. 2017, 50, 2433–2441. (In Chinese) [Google Scholar]
  122. Ronfort, J.; Bataillon, T.; Santoni, S.; Delalande, M.; David, J.L.; Prosperi, J.M. Microsatellite diversity and broad scale geographic structure in a model legume: Building a set of nested core collection for studying naturally occurring variation in Medicago truncatula. BMC Plant Biol. 2006, 6, 28. [Google Scholar] [CrossRef]
  123. Fang, L.C.; Xia, H.M.; Ma, W.J.; Zhang, X.Y. Genetic Diversity Analysis and Primary Core Collection of Catalpa bungei Germplasm with SSR Markers. J. Northeast. For. Univ. 2017, 45, 1–5. (In Chinese) [Google Scholar]
  124. Xue, H.; Yu, X.; Fu, P.; Liu, B.; Zhang, S.; Li, J.; Zhai, W.; Lu, N.; Zhao, X.; Wang, J.; et al. Construction of the Core Collection of Catalpa fargesii f. duclouxii (Huangxinzimu) Based on Molecular Markers and Phenotypic Traits. Forests 2021, 12, 1518. [Google Scholar] [CrossRef]
  125. Xu, C.H.; Liu, X.L.; Mao, J.; Liu, H.B.; Lin, X.Q.; Lu, X.; Su, H.S. Construction of a core-collection of Saccharum spontaneum based on SSR molecular markers. J. Hunan Agric. Univ. 2020, 46, 657–663. (In Chinese) [Google Scholar]
  126. Peng, C.; Fan, X.P.; Su, X.H.; Long, K.L.; Zhang, X.Y. Construction of core collection in southern type of Populus deltoides using SSR makers. Acta Bot. Boreali-Occident. Sin. 2019, 39, 250–257. (In Chinese) [Google Scholar]
  127. Zhong, Y.D.; Liu, L.P.; Li, Y.Q.; Wu, Z.X.; Yang, A.; Liu, S.J.; Liu, Q.L.; Liu, T.Y.; Yu, F. Sampling Strategy of Primary Core Germplasm of Cinnamomum camphora in China. J. Southwest For. Univ. 2020, 40, 1–13. (In Chinese) [Google Scholar]
  128. Huang, Y.Q.; Yin, G.G.; Yang, J.C.; Yu, N.; Zou, W.T.; Li, R.S. Developing a Mini Core Germplasm of Phoebe bournei Based on SSR Molecular Marker. Mol. Plant Breed. 2020, 18, 2641–2648. (In Chinese) [Google Scholar]
  129. Wu, H.; Yu, W.W.; Wu, H.; Dong, L.M.; Dai, W.S.; Ye, S.Y.; Shen, D.F. Determination of Core Germplasm in Torreya grandis Fort. ex Lindl. Based on Seed Traits and Molecular Markers. Mol. Plant Breed. 2019, 17, 5513–5520. (In Chinese) [Google Scholar]
  130. Duan, F.; Zhang, H.; Li, S.; Tian, Z.Q.; Gan, X.H. Core Collection Construction of Endangered Plant Tetracentron sinense Based on ISSR Molecular Markers. Subtrop. Plant Sci. 2018, 47, 101–106. (In Chinese) [Google Scholar]
  131. Shen, Z.; Ma, L.Y.; Ao, Y.; Duan, J. Analysis of the genetic diversity and construction of core collection of Xanthoceras sorbifolia Bunge. Using microsatellite marker data. Mol. Plant Breed. 2017, 15, 3341–3350. (In Chinese) [Google Scholar]
  132. Yang, H.; Zhang, R.; Wang, B.; Xu, Z.; Zhou, Z. Construction of core collection of Schima superba based on SSR molecular markers. Sci. Silvae Sin. 2017, 53, 37–46. (In Chinese) [Google Scholar]
  133. Peng, C.; Li, Z.F.; Xiang, S.S.; Zhang, X.Y. Molecular Marker Evaluation and Construction of Primary Core Collection of Sapium sebiferum. Mol. Plant Breed. 2017, 15, 1455–1460. (In Chinese) [Google Scholar]
  134. Yan, L.P.; Wu, D.J.; Mao, X.H.; Yao, J.X.; Ren, F.; Li, S.W.; Wang, K.F.; Wang, Y.H.; Liu, C.L. Construction of core collection of Fraxinus based on SSR molecular markers. J. Cent. South Univ. For. Technol. 2019, 39, 1–9. (In Chinese) [Google Scholar]
  135. Li, H.G. Genetic Diversity Analysis, Core Collection Construction and Molecular Identification of Eucommia ulmoides Oliver. Ph.D. Thesis, Chinese Academy of Forestry, Beijing, China, 2017. [Google Scholar]
  136. Mao, J.; Liu, X.L.; Su, H.S.; Lu, X.; Lin, X.Q.; Cai, Q.; Fan, Y.H. Constructing Core Collection of Saccharum arundinaceum L. Based on Phenotype and Molecular Markers. J. Plant Genet. Resour. 2016, 17, 607–615. (In Chinese) [Google Scholar]
  137. Boccacci, P.; Aramini, M.; Ordidge, M.; van Hintum, T.J.L.; Marinoni, T.D.; Valentini, N.; Sarraquigne, J.-P.; Solar, A.; Rovira, M.; Bacchetta, L.; et al. Comparison of selection methods for the establishment of a core collection using SSR markers for hazelnut (Corylus avellana L.) accessions from European germplasm repositories. Tree Genet. Genomes 2021, 17, 48. [Google Scholar] [CrossRef]
  138. Bernard, A.; Barreneche, T.; Donkpegan, A.; Lheureux, F.; Dirlewanger, E. Comparison of structure analyses and core collections for the management of walnut genetic resources. Tree Genet. Genomes 2020, 8, 365–378. [Google Scholar] [CrossRef]
  139. Mahmoodi, R.; Dadpour, M.R.; Hassani, D.; Zeinalabedini, M.; Vendramin, E.; Leslie, C.A. Composite coreset construction and diversity analysis of Iranian walnut germplasm using molecular markers and phenotypic traits. PLoS ONE 2021, 3, e0248623. [Google Scholar]
  140. Wei, Z.G.; Gao, Y.C.; Yang, C.P. Sampling method for constructing germplasm core collections of Betula platyphylla. J. Northeast For. Univ. 2009, 37, 1–4. (In Chinese) [Google Scholar]
  141. Li, H.Y.; Liang, Z.W.; Chen, C.; Huang, H.H.; Tong, Z.K.; Lu, Y.Q. Core Germplasm of Betula luminifera Screened by Molecular Markers. J. Zhejiang For. Sci. Technol. 2011, 3, 1–4. (In Chinese) [Google Scholar]
  142. Zhao, J.; Tong, Y.Q.; Ge, T.; Ge, J. Genetic diversity estimation and core collection construction of Sinojackia huangmeiensis based on novel microsatellite markers. Biochem. Syst. Ecol. 2016, 64, 74e80. [Google Scholar] [CrossRef]
  143. Li, N.; Yang, Y.; Xu, F.; Chen, X.; Wei, R.; Li, Z.; Pan, W.; Zhang, W. Genetic Diversity and Population Structure Analysis of Castanopsis hystrix and Construction of a Core Collection Using Phenotypic Traits and Molecular Markers. Genes 2022, 13, 2383. [Google Scholar] [CrossRef]
  144. Zhang, Y.; Hu, D.; Zuo, J.; Zhang, P.; Wang, Z.; Chen, C. Development of a mulberry core collection originated in China to enhance germplasm conservation. Crop Breed. Appl. Biotechnol. 2019, 19, 55–61. [Google Scholar]
  145. Nie, X.H.; Wang, Z.H.; Liu, N.W.; Song, L.; Yan, B.Q.; Xing, Y.; Zhang, Q.; Fang, K.F.; Zhao, Y.L.; Chen, X.; et al. Fingerprinting 146 Chinese chestnut (Castanea mollissima Blume) accessions and selecting a core collection using SSR markers. J. Integr. Agric. 2021, 20, 1277–1286. [Google Scholar] [CrossRef]
  146. Pereira-Lorenzo, S.; Ramos-Cabrer, A.M.; Barreneche, T.; Mattioni, C.; Villani, F.; Díaz-Hernández, M.B.; Martín, L.M.; Martín, A. Database of European chestnut cultivars and definition of a core collection using simple sequence repeats. Tree Genet. Genomes 2017, 13, 114. [Google Scholar] [CrossRef]
  147. Miyamoto, N.; Ono, M.; Watanabe, A. Construction of a core collection and evaluation of genetic resources for Cryptomeria japonica (Japanese cedar). J. For. Res. 2015, 20, 186–196. [Google Scholar] [CrossRef]
  148. Lv, J.B.; Li, C.R.; Zhou, C.P.; Chen, J.B.; Li, F.G.; Wang, Q.G.; Li, M.; Wang, Y.; Chen, S.; Chen, J.; et al. Genetic diversity analysis of a breeding population of Eucalyptu scloeziana F.Muell. (Myrtaceae) and extraction ofacore germplasm collection using microsatellite markers. Ind. Crops Prod. 2020, 145, 112157. [Google Scholar] [CrossRef]
  149. Liu, D.H.; Zhang, F.Q.; Zhang, W.H. Establishment of Eucalyptus urophylla Core Collection Based on Geographical Distribution and Phenotypic Data. J. Southwest For. Univ. 2013, 33, 1–8. (In Chinese) [Google Scholar]
  150. Di Guardo, M.; Scollo, F.; Ninot, A.; Rovira, M.; Hermoso, J.F.; Distefano, G.; La Malfa, S.; Batlle, I. Genetic structure analysis and selection of a core collection for carob tree germplasm conservation and management. Tree Genet. Genomes 2019, 15, 41. [Google Scholar] [CrossRef]
  151. Ghazal, H.; Mouhaddab, J.; Ait Aabd, N.; Msanda, F.; Filali-Maltouf, F.; Belkadi, B.; Ferradouss, A.; El Modafar, C.; Ibnsouda Koraichi, S.; Ghazal, H.; et al. Assessing genetic diversity and constructing a core collection of an endangered Moroccan endemic tree [Argania spinosa (L.) Skeels]. Moroc. J. Biol. 2016, 13, 1–12. [Google Scholar]
  152. Wang, X.; Cao, Z.; Gao, C.; Li, K. Strategy for the construction of a core collection for Pinus yunnanensis Franch. to optimize timber based on combined phenotype and molecular marker data. Genet. Resour. Crop Evol. 2021, 68, 3219–3240. [Google Scholar] [CrossRef]
  153. Wang, X.; Liu, X.; Xing, S.; Kong, Q.; Zhang, Y.; Sun, L.; Gao, Y. AFLP analysis of genetic diversity and a construction of the core collection of partial ancient ginkgo trees in China. Acta Hortic. Sin. 2016, 43, 249–260. (In Chinese) [Google Scholar]
  154. Zhang, Z.; Yang, Q.; Niu, Y.; Zhang, Y.; Dong, S.; Zhang, W.; Wang, Z. Diversity analysis and establishment of core collection among Akebia trifoliata (Thunb.) Koidz. in Qinba mountain area of China using ISSR and SRAP markers. Genet. Resour. Crop Evol. 2020, 68, 1085–1102. [Google Scholar] [CrossRef]
  155. Zhu, Y.; Liang, D.; Song, Z.; Tan, Y.; Guo, X.; Wang, D. Genetic Diversity Analysis and Core Germplasm Collection Construction of Camellia oleifera Based on Fruit Phenotype and SSR Data. Genes 2022, 13, 2351. [Google Scholar] [CrossRef]
  156. Zhang, L.B. Analysis of Population Genetic Variation of Plus Trees and Construction of Core Collection in Cornus wilsoniana. Master’s Thesis, Beijing Forestry University, Beijing, China, 2020. [Google Scholar]
  157. Amalraj, V.A.; Balakrishnan, R.; Jebadhas, A.W.; Balasundaram, N. Constituting a core collection of Saccharum spontaneum L. and comparison of three stratified random sampling procedures. Genet. Resour. Crop Evol. 2006, 53, 1563–1572. [Google Scholar] [CrossRef]
  158. Chandra, S.; Huaman, Z.; Krishna, S.H.; Ortiz, R. Optimal sampling strategy and core collection size of Andean tetraploid potato based on isozyme data: A simulation study. Theor. Appl. Genet. 2002, 104, 1325–1334. [Google Scholar] [CrossRef] [PubMed]
  159. Hu, J.; Zhu, J.; Xu, H.M. Methods of constructing core collections by stepwise clustering with three sampling strategies based on the genotypic values of crops. Theor. Appl. Genet. 2000, 101, 264–268. [Google Scholar] [CrossRef]
  160. Adal, A.M.; Demissie, Z.A.; Mahmoud, S.S. Identification, validation and cross-species transferability of novel Lavandula EST-SSRs. Planta 2015, 241, 987–1004. [Google Scholar] [CrossRef] [PubMed]
  161. Wang, J.C.; Hu, J.; Xu, H.M.; Zhang, S. A strategy on constructing core collections by least distance stepwise sampling. Theor. Appl. Genet. 2007, 115, 1–8. [Google Scholar] [CrossRef] [PubMed]
  162. Zhang, G.Y.; Wang, X.F.; Liu, S.J.; Ma, Z.Y. Cluster analysis and sampling methods for core collection construction in glandless cotton. Cotton Sci. 2004, 16, 8–12. [Google Scholar]
  163. Zhang, X.; Zhao, Y.; Cheng, Y.; Feng, X.; Guo, Q.; Zhou, M.; Hodgkin, T. Establishment of sesame germplasm core collection in China. Genet. Resour. Crop Evol. 2000, 47, 273–279. [Google Scholar]
  164. Li, C.T.; Shi, C.H.; Wu, J.G.; Xu, H.M.; Zhang, H.Z.; Ren, Y.L. Methods of developing core collections based on the predicted genotypic value of rice (Oryza sativa L.). Theor. Appl. Genet. 2004, 108, 1172–1176. [Google Scholar] [CrossRef]
  165. Xu, H.; Qiu, Y.; Hu, J.; Wang, J. Mothods of constructing core collection of crop germplasm by comparing different genetic distances, cluster methods and sampling strategies. Acta Agron. Sin. 2004, 30, 932–936. [Google Scholar]
  166. Schoen, D.J.; Brown, A.H.D. Conservation of allelic richness in wild crop relatives is aided by assessment of genetic markers. Proc. Natl. Acad. Sci. USA 1993, 90, 10623–10627. [Google Scholar] [CrossRef]
  167. Schoen, D.J.; Brown, A.H.D. Maximising genetic diversity in core collections of wild relatives of crop species. In Core Collections of Plant Genetic Resources; Hodgkin, T., Bronw, A.H.D., van Hintum, T.J.L., Morales, E.A.V., Eds.; Wiley: New York, NY, USA, 1995; pp. 55–76. [Google Scholar]
  168. Gouesnard, B.; Bataillon, T.M.; Decoux, G.; Rozale, C.; Schoen, D.J.; David, J.L. MSTRAT: An algorithm for building germplasm core collections by maximizing allelic or phenotypic richness. J. Hered. 2001, 92, 93–94. [Google Scholar] [CrossRef] [PubMed]
  169. Kim, K.-W.; Chung, H.-K.; Cho, G.-T.; Ma, K.-H.; Chandrabalan, D.; Gwag, J.-G.; Kim, T.-S.; Cho, E.-G.; Park, Y. PowerCore: A program applying the advanced M strategy with a heuristic search for establishing core sets. Bioinformatics 2007, 23, 2155–2162. [Google Scholar] [CrossRef]
  170. Cipriani, G.; Spadotto, A.; Jurman, I.; Di Gaspero, G.; Crespan, M.; Meneghetti, S.; Frare, E.; Vignani, R.; Cresti, M.; Morgante, M. The SSR-Based Molecular Profile of 1005 Grapevine (Vitis vinifera L.) Accessions Uncovers New Synonymy and Parentages, and Reveals a Large Admixture amongst Varieties of Different Geographic Origin. Theor. Appl. Genet. 2010, 121, 1569–1585. [Google Scholar] [CrossRef]
  171. De Beukelaer, H.; Smykal, P.; Davenport, G.F.; Fack, V. Core Hunter II: Fast core subset selection based on multiple genetic diversity measures using Mixed Replica search. BMC Bioinform. 2012, 13, 312. [Google Scholar] [CrossRef] [PubMed]
  172. De Beukelaer, H.; Davenport, G.F.; Fack, V. Core Hunter 3: Flexible core subset selection. BMC Bioinform. 2018, 19, 203. [Google Scholar] [CrossRef]
  173. Chen, R.; Hara, T.; Ohsawa, R.; Yoshioka, Y. Analysis of genetic diversity of rapeseed genetic resources in Japan and core collection construction. Breed. Sci. 2017, 67, 239–247. [Google Scholar] [CrossRef]
  174. Yoneazwa, K.; Nomura, T.; Morish, H. Sampling strategies for use in stratified germplasm collection. In Core Collection of Plant Genetic Resources; Hodgkin, T., Brown, A.H.D., van Hintum, T.H.L., Eds.; John Wily & Sons: Chichester, UK, 1995; pp. 35–53. [Google Scholar]
  175. Qiu, L.J.; Xing, L.L.; Guo, Y.; Wang, J.; Jackson, S.A.; Chang, R.-Z. A platform for soybean molecular breeding: The utilization of core collections for food security. Plant Mol. Biol. 2013, 83, 41–50. [Google Scholar] [CrossRef] [PubMed]
  176. Chen, W.; Hou, L.; Zhang, Z.; Pang, X.; Li, Y. Genetic Diversity, Population Structure, and Linkage Disequilibrium of a Core Collection of Ziziphus jujuba Assessed with Genome-wide SNPs Developed by Genotyping-by-sequencing and SSR Markers. Front. Plant Sci. 2017, 8, 575. [Google Scholar] [CrossRef]
  177. Lu, B.Y.; Ma, M.L.; Zhang, W.; Meng, H.L.; Lei, E.; Wang, T.; Li, C. Development of 23 novel microsatellite markers of Amomum tsao-ko (Zingiberaceae) based on restriction-site-associated DNA sequencing. Mol. Biol. Rep. 2021, 48, 1943–1949. [Google Scholar] [CrossRef] [PubMed]
  178. Li, S.G.; Shen, D.; Liu, B.; Qiu, Y.; Zhang, X.H.; Zhang, Z.H.; Wang, H.P.; Li, X.X. Development and Application of Cucumber InDel Markers Based on Genome Re-sequencing. J. Plant Genet. Resour. 2013, 14, 278–283. [Google Scholar]
  179. Guo, G.J.; Zhang, G.L.; Pan, B.G.; Diao, W.P.; Liu, J.B.; Ge, W.; Gao, C.; Zhang, Y.; Jiang, C.; Wang, S. Development and Application of InDel Markers for Capsicum spp. Based on Whole-Genome Re-Sequencing. Sci. Rep. 2019, 9, 3691–3794. [Google Scholar] [CrossRef]
  180. Meng, L.; Liu, B.; Lin, L.B.; Cheng, F.; Wang, X.W.; Wu, J. Development of InDel Markers for Brassica campestris and Genetic Linkage Map Construction of the RILs Population. Acta Hortic. Sin. 2012, 8, 1491–1500. [Google Scholar]
  181. Yu, X.; Wang, H.; Zhong, W.L.; Bai, J.J.; Liu, P.L. QTL mapping of leafy heads by genome resequencing in the RIL population of Brassica rapa. PLoS ONE 2013, 8, e76059. [Google Scholar] [CrossRef] [PubMed]
  182. Song, X.; Ge, T.; Li, Y.; Hou, X. Genome-wide identification of SSR and SNP markers from the non-heading Chinese cabbage for comparative genomic analyses. BMC Genom. 2015, 16, 328–346. [Google Scholar] [CrossRef] [PubMed]
  183. Kim, M.; Park, Y.; Lee, J.; Sim, S.C. Development of molecular markers for Ty-2 and Ty-3 selection in tomato breeding. Sci. Hortic. 2020, 265, 109230. [Google Scholar] [CrossRef]
  184. Qing, D.J.; Dai, G.X.; Zhou, W.Y.; Huang, S.S.; Liang, H.F.; Gao, L.; Gao, J.; Huang, J.; Zhou, M.; Chen, R.; et al. Development of molecular marker and introgression of Bph3 into elite rice cultivars by marker-assisted selection. Breed. Sci. 2019, 69, 40–46. [Google Scholar] [CrossRef]
  185. Kroc, M.; Czepiel, K.; Wilczura, P.; Mokrzycka, M.; Święcicki, W. Development and Validation of a Gene-Targeted dCAPS Marker for Marker-Assisted Selection of Low-Alkaloid Content in Seeds of Narrow-Leafed Lupin (Lupinus angustifolius L.). Genes 2019, 10, 428. [Google Scholar] [CrossRef]
  186. Liu, S.L.; Gu, X.F.; Miao, H.; Wang, Y.; Weng, Y.Q.; Wehner, T.C.; Zhang, S.P. Molecular Mapping and Candidate Gene Analysis of Black Fruit Spine Gene in Cucumber (Cucumis sativus L.). Sci. Agric. Sin. 2014, 47, 122–132. [Google Scholar]
  187. Wang, M.; Gu, X.F.; Miao, H.; Liu, S.L.; Wang, Y.; Todd C, W.; Zhang, S.P. Molecular Mapping and Candidate Gene Analysis for Heavy Netting Gene (H) of Mature Fruit of Cucumber (Cucumis sativus L.). Sci. Agric. Sin. 2014, 47, 1550–1557. [Google Scholar]
  188. Fu, D.; Zhong, K.Z.; Zhong, Z.Z.; Hu, G.C.; Zhang, P.; Tong, H. Genome-Wide Association Study of Sheath Blight Resistance within a Core Collection of Rice (Oryza sativa L.). Agronomy 2022, 12, 1493. [Google Scholar] [CrossRef]
  189. Xie, X.F.; Zheng, Y.; Lu, L.B.; Yuan, J.Z.; Hu, J.; Bu, S.; Lin, Y.; Liu, Y.; Guan, H.; Wu, W. Genome-Wide Association Study of QTLs Conferring Resistance to Bacterial Leaf Streak in Rice. Plants 2021, 10, 2039. [Google Scholar] [CrossRef]
  190. Coan, M.M.D.; Senhorinho, H.J.C.; Pinto, R.J.B.; Scapim, C.A.; Tessmann, D.J.; Williams, W.P.; Warburton, M.L. Genome-Wide Association Study of Resistance to Ear Rot by in a Tropical Field Maize and Popcorn Core Collection. Crop Sci. 2018, 58, 564–578. [Google Scholar] [CrossRef]
  191. Halder, J.; Zhang, J.F.; Ali, S.; Sidhu, J.S.; Gill, H.S.; Talukder, S.K.; Kleinjan, J.; Turnipseed, B.; Sehgal, S.K. Mining and genomic characterization of resistance to tan spot, Stagonospora nodorum blotch (SNB), and Fusarium head blight in Watkins core collection of wheat landraces. BMC Plant Biol. 2019, 19, 480. [Google Scholar] [CrossRef] [PubMed]
  192. Sapkota, S.; Hao, Y.F.; Johnson, J.; Buck, J.; Aoun, M.; Mergoum, M. Genome-Wide Association Study of a Worldwide Collection of Wheat Genotypes Reveals Novel Quantitative Trait Loci for Leaf Rust Resistance. Plant Genome 2019, 12, 190033. [Google Scholar] [CrossRef] [PubMed]
  193. Silvar, C.; Casas, A.M.; Kopahnke, D.; Habekuß, A.; Schweizer, G.; Gracia, M.P.; Lasa, J.M.; Ciudad, F.J.; Molina-Cano, J.L.; Igartua, E.; et al. Screening the Spanish Barley Core Collection for disease resistance. Plant Breed. 2010, 129, 45–52. [Google Scholar] [CrossRef]
  194. Cuevas, H.E.; Prom, L.K. Evaluation of genetic diversity, agronomic traits, and anthracnose resistance in the NPGS Sudan Sorghum Core collection. BMC Genom. 2020, 21, 88. [Google Scholar] [CrossRef]
  195. Sharma, R.; Upadhyaya, H.D.; Manjunatha, S.V.; Rao, V.P.; Thakur, R.P. Resistance to Foliar Diseases in a Mini-Core Collection of Sorghum Germplasm. Plant Dis. 2012, 96, 1629–1633. [Google Scholar] [CrossRef]
  196. Binagwa, P.H.; Traore, S.M.; Egnin, M.; Bernard, G.C.; Ritte, I.; Mortley, D.; Kamfwa, K.; He, G.; Bonsi, C. Genome-Wide Identification of Powdery Mildew Resistance in Common Bean (Phaseolus vulgaris L.). Front. Genet. 2021, 12, 673069. [Google Scholar] [CrossRef]
  197. Shi, A.; Gepts, P.; Song, Q.; Xiong, H.; Michaels, T.E.; Chen, S. Genome-Wide Association Study and Genomic Prediction for Soybean Cyst Nematode Resistance in USDA Common Bean (Phaseolus vulgaris) Core Collection. Front. Plant Sci. 2021, 12, 624156. [Google Scholar] [CrossRef]
  198. Zia, B.; Shi, A.; Olaoye, D.; Xiong, H.Z.; Ravelombola, W.; Gepts, P.; Schwartz, H.F.; Brick, M.A.; Otto, K.; Ogg, B.; et al. Genome-Wide Association Study and Genomic Prediction for Bacterial Wilt Resistance in Common Bean (Phaseolus vulgaris) Core Collection. Front. Genet. 2022, 13, 853114. [Google Scholar] [CrossRef]
  199. Pandey, A.K.; Yee, M.; Win, M.M.; Moh, L.H.M.; Adapala, G.; Rathore, A.; Sheu, Z.-M.; Nair, R.M. Identification of new sources of resistance to dry root rot caused by Macrophomina phaseolina isolates from India and Myanmar in a mungbean mini-core collection. Crop Prot. 2021, 143, 105569. [Google Scholar] [CrossRef]
  200. Togola, A.; Boukar, O.; Servent, A.; Chamarthi, S.; Tamò, M.; Fatokun, C. Identification of sources of resistance in cowpea mini core accessions to Aphis craccivora Koch (Homoptera: Aphididae) and their biochemical characterization. Euphytica Neth. J. Plant Breed. 2020, 216, 88. [Google Scholar] [CrossRef]
  201. Sharma, M.; Rathore, A.; Mangala, U.N.; Ghosh, R.; Sharma, S.; Upadhyay, H.; Pande, S. New sources of resistance to Fusarium wilt and sterility mosaic disease in a mini-core collection of pigeonpea germplasm. Eur. J. Plant Pathol. 2012, 133, 707–714. [Google Scholar] [CrossRef]
  202. Das, S.; Porter, L.D.; Ma, Y.; Coyne, C.J.; Chaves-Cordoba, B.; Naidu, R.A. Resistance in lentil (Lens culinaris) genetic resources to the pea aphid (Acyrthosiphon pisum). Entomol. Exp. Appl. 2022, 170, 755–769. [Google Scholar] [CrossRef]
  203. Achola, E.; Wasswa, P.; Fonceka, D.; Clevenger, J.P.; Bajaj, P.; Ozias-Akins, P.; Rami, J.-F.; Deom, C.M.; Hoisington, D.A.; Edema, R.; et al. Genome-wide association studies reveal novel loci for resistance to groundnut rosette disease in the African core groundnut collection. Theor. Appl. Genet. 2023, 136, 35. [Google Scholar] [CrossRef]
  204. Ding, Y.B.; Qiu, X.K.; Luo, H.Y.; Huang, L.; Guo, J.B.; Yu, B.; Sudini, H.; Pandey, M.; Kang, Y.; Liu, N.; et al. Comprehensive evaluation of Chinese peanut mini-mini core collection and QTL mapping for aflatoxin resistance. BMC Plant Biol. 2022, 22, 207. [Google Scholar] [CrossRef] [PubMed]
  205. Chen, B.; Shi, R.K.; Wang, Z.C.; Liu, S.; Li, Q.; Liu, Z.; Wang, G.; Wu, J.; Ma, S. Identification of Verticillium Wilt Resistance of Core Collection of Upland Cotton and Screening of Elite Resistant Germplasm. J. Agric. Sci. Technol. 2021, 23, 45–51. (In Chinese) [Google Scholar]
  206. You, F.M.; Rashid, K.Y.; Zheng, C.; Khan, N.; Li, P.; Xiao, J.; He, L.; Yao, Z.; Cloutier, S. Insights into the Genetic Architecture and Genomic Prediction of Powdery Mildew Resistance in Flax (Linum usitatissimum L.). Int. J. Mol. Sci. 2022, 23, 4960. [Google Scholar] [CrossRef] [PubMed]
  207. Kumar, V.; Paillard, S.; Fopa-Fomeju, B.; Falentin, C.; Deniot, G.; Baron, C.; Vallée, P.; Manzanares-Dauleux, M.J.; Delourme, R. Multi-year linkage and association mapping confirm the high number of genomic regions involved in oilseed rape quantitative resistance to blackleg. Theor. Appl. Genet. 2018, 131, 1627–1643. [Google Scholar] [CrossRef] [PubMed]
  208. McClure, K.A.; Gardner, K.M.; Douglas, G.M.; Song, J.; Forney, C.F.; DeLong, J.; Fan, L.; Du, L.; Toivonen, P.M.; Somers, D.J.; et al. A Genome-Wide Association Study of Apple Quality and Scab Resistance. Plant Genome 2018, 11, 170075. [Google Scholar] [CrossRef]
  209. Yang, X.P.; Liu, G.; Hou, X.L.; Xu, J.H.; Zhang, M. Association Analysis of Fusarium wilt Resistance of Core Collection of Watermelon Germplasms Based on SRAP Markers. Acta Hortic. Sin. 2013, 40, 1298–1308. (In Chinese) [Google Scholar]
  210. Natarajan, S.; Kim, H.T.; Thamilarasan, S.K.; Veerappan, K.; Park, J.I.; Nou, I.S. WholeGenome Re-Sequencing and Characterization of Powdery Mildew Disease-Associated Allelic Variationin Melon. PLoS ONE 2016, 11, e0157524. [Google Scholar] [CrossRef]
  211. Singh, K.N.; Rawat, S.; Kumar, K.; Agarwal, S.K.; Goel, S.; Jagannath, A.; Agarwal, M. Identification of significant marker-trait associations for Fusarium wilt resistance in a genetically diverse core collection of safflower using AFLP and SSR markers. J. Appl. Genet. 2022, 63, 447–462. [Google Scholar] [CrossRef]
  212. Wang, X.L.; Li, J.Q.; Sun, J.; Gu, S.; Wang, J.B.; Su, C.; Li, Y.; Ma, D.; Zhao, M.; Chen, W. Mining Beneficial Genes for Salt Tolerance From a Core Collection of Rice Landraces at the Seedling Stage Through Genome-Wide Association Mapping. Front. Plant Sci. 2022, 13, 847863. [Google Scholar] [CrossRef]
  213. Liu, S.; Zhong, H.; Meng, X.X.; Sun, H.; Li, Y.S.; Pinson, S.R.M.; Chang, S.K.C.; Peng, Z. Genome-wide association studies of ionomic and agronomic traits in USDA mini core collection of rice and comparative analyses of different mapping methods. BMC Plant Biol. 2020, 20, 441. [Google Scholar] [CrossRef]
  214. Zhang, P.; Zhong, K.Z.; Zhong, Z.Z.; Tong, H.H. Genome-wide association study of important agronomic traits within a core collection of rice (Oryza sativa L.). BMC Plant Biol. 2019, 19, 259. [Google Scholar] [CrossRef]
  215. Song, J.; Li, J.; Sun, J.; Hu, T.; Wu, A.; Liu, S.; Wang, W.; Ma, D.; Zhao, M. Genome-Wide Association Mapping for Cold Tolerance in a Core Collection of Rice (Oryza sativa L.) Landraces by Using High-Density Single Nucleotide Polymorphism Markers From Specific-Locus Amplified Fragment Sequencing. Front. Plant Sci. 2018, 9, 875. [Google Scholar] [CrossRef]
  216. Li, X.N.; Zhou, Y.; Bu, Y.P.; Wang, X.F.; Zhang, Y.M.; Guo, N.; Zhao, J.; Xing, H. Genome-wide association analysis for yield-related traits at the R6 stage in a Chinese soybean mini core collection. Genes Genom. 2021, 43, 897–912. [Google Scholar] [CrossRef]
  217. Hao, D.; Cheng, H.; Yin, Z.; Cui, S.; Zhang, D.; Wang, H.; Yu, D. Identification of single nucleotide polymorphisms and haplotypes associated with yield and yield components in soybean (Glycine max) landraces across multiple environments. Theor. Appl. Genet. 2012, 124, 447–458. [Google Scholar] [CrossRef]
  218. Qiu, P.C.; Zhang, W.B.; Li, C.D.; Jiang, H.W.; Liu, C.Y.; Fan, D.M.; Zeng, Q.L.; Hu, G.H.; Chen, Q.S. Genetic overlap of droughttolerance loci between germination stage and seedling stage analyzed using introgression lines in soybean. Acta Agron. Sin. 2011, 37, 477–483. [Google Scholar] [CrossRef]
  219. Li, Y.H.; Li, W.; Zhang, C.; Yang, L.; Chang, R.Z.; Gaut, B.S.; Qiu, L.J. Genetic diversity in domesticated soybean (Glycine max) and its wild progenitor (Glycine soja) for simple sequence repeat and single-nucleotide polymorphism loci. New Phytol. 2010, 188, 242–253. [Google Scholar] [CrossRef]
  220. Jiang, H.W.; Li, C.D.; Liu, C.Y.; Zhang, W.B.; Qiu, P.C.; Li, W.F.; Gao, Y.L.; Hu, G.H.; Chen, Q.S. Genotype analysis and QTL mapping for tolerance to low temperature in germination by introgression lines in soybean. Acta Agron. Sin. 2009, 35, 1268–1273. [Google Scholar] [CrossRef]
  221. Garcia, M.; Eckermann, P.; Haefele, S.; Satija, S.; Sznajder, B.; Timmin, A.; Baumann, U.; Wolters, P.; Mather, D.E.; Fleury, D. Genome wide association mapping of grain yield in adiverse collection of spring wheat (Triticum aestivum L.) evaluated in southern Australia. PLoS ONE 2019, 14, e0211730. [Google Scholar] [CrossRef]
  222. Ma, Z.Y.; He, S.P.; Wang, X.F.; Sun, J.L.; Zhang, Y.; Zhang, G.; Wu, L.; Li, Z.; Liu, Z.; Sun, G.; et al. Resequencing a core collection of upland cotton identifies genomic variation and loci influencing fiber quality and yield. Nat. Genet. 2018, 50, 803–813. [Google Scholar] [CrossRef] [PubMed]
  223. Lin, T.; Zhu, G.; Zhang, J.; Xu, X.; Yu, Q.; Zheng, Z.; Zhang, Z.H.; Lun, Y.Y.; Li, S.; Wang, X.X.; et al. Genomic analyses provide insights into the history of tomato breeding. Nat. Genet. 2014, 46, 1220–1226. [Google Scholar] [CrossRef]
  224. Galpaz, N.; Gonda, I.; Shem-Tov, D.; Barad, O.; Tzuri, G.; Lev, S.; Fei, Z.; Xu, Y.; Mao, L.; Jiao, C.; et al. Deciphering genetic factors that determine melon fruit-quality traits using RNA-Seq-based high-resolution QTL and eQTL mapping. Plant J. 2018, 94, 169–191. [Google Scholar] [CrossRef]
  225. Nathanael, F.; Andres, G.; Mohit, V.; Michael, P.; Anna, H.; Collins, K.; Niranjan, B. Genome-wide association mapping identifies markers associated with cane yield components and sucrose traits in the Louisiana sugarcane core collection. Genomics 2019, 111, 1794–1801. [Google Scholar]
  226. Jiao, Y.; Wang, Y.; Xue, D.; Wang, J.; Yan, M.; Liu, G.; Dong, G.; Zeng, D.; Lu, Z.; Zhu, X.; et al. Regulation of OsSPL14 by OsmiR156 defines ideal plant architecture in rice. Nat. Genet. 2010, 42, 541–544. [Google Scholar] [CrossRef]
  227. Jin, J.; Huang, W.; Gao, J.P.; Yang, J.; Shi, M.; Zhu, M.Z.; Luo, D.; Lin, H.X. Genetic control of rice plant architecture under domestication. Nat. Genet. 2008, 40, 1365–1369. [Google Scholar] [CrossRef]
  228. Tan, L.; Li, X.; Liu, F.; Sun, X.; Li, C.; Zhu, Z.; Fu, Y.; Cai, H.; Wang, X.; Xie, D.; et al. Control of a key transition from prostrate to erect growth in rice domestication. Nat. Genet. 2008, 40, 1360–1364. [Google Scholar] [CrossRef]
  229. Wei, X.; Xu, J.; Guo, H.; Jiang, L.; Chen, S.; Yu, C.; Zhou, Z.; Hu, P.; Zhai, H.; Wan, J. DTH8 suppresses flowering in rice, influencing plant height and yield potential simultaneously. Plant Physiol. 2010, 153, 1747–1758. [Google Scholar] [CrossRef] [PubMed]
  230. Tanaka, N.; Shenton, M.; Kawahara, Y.; Kumagai, M.; Sakai, H.; Kanamori, H.; Yonemaru, J.; Fukuoka, S.; Sugimoto, K.; Ishimoto, M.; et al. Whole-Genome Sequencing of the NARO World Rice Core Collection (WRC) as the Basis for Diversity and Association Studies. Plant Cell Physiol. 2020, 61, 922–932. [Google Scholar] [CrossRef]
  231. Qi, J.J.; Liu, X.; Shen, D.; Miao, H.; Xie, B.Y.; Li, X.X.; Zeng, P.; Wang, S.; Shang, Y.; Gu, X.F.; et al. A genomic variation map provides insights into the genetic basis of cucumber domestication and diversity. Nat. Genet. 2013, 45, 1510–1515. [Google Scholar] [CrossRef] [PubMed]
  232. Guo, S.G.; Zhang, J.G.; Sun, H.H.; Salse, J.; Lucas, W.J.; Zhang, H.Y.; Zheng, Y.; Mao, L.Y.; Ren, Y.; Wang, Z.W.; et al. The draft genome of watermelon (Citrullus lanatus) and resequencing of 20 diverse accessions. Nat. Genet. 2013, 45, 51–58. [Google Scholar] [CrossRef] [PubMed]
Forests 14 00926 g001 550
Figure 1. Stratification cluster sampling scheme of core collection for plant species.
Figure 1. Stratification cluster sampling scheme of core collection for plant species.
Forests 14 00926 g001
Forests 14 00926 g002 550
Figure 2. Statistical results of data collection and sampling strategies for core collection construction of 146 plant species. (a) Data collection; (b) Sampling strategies. Here, R random sampling, CD Multiple cluster deviation sampling, CP Multiple clusters preferred sampling, CR Multiple cluster random sampling, LDSS Least distance stepwise sampling, MLST Maximum length sub-tree method, MM M strategy algorithm of MSTRAT, MP M strategy algorithm of PowerCore(National Institute of Agricultural Biotechnology, Suwon, South Korea), MC M strategy algorithm of CoreFinder, CH Core Hunter method, GC GenoCore method, SAGD Simulated annealing algorithm based on the maximizing genetic diversity, SANA Simulated annealing algorithm based on maximizing allelic richness, CM Multiple cluster most representative sampling, GD Genetic diversity based on Genetic Subsetter, CP and CH Multiple clusters preferred sampling combined with Core Hunter method, and MC and R M strategy algorithm of Core Finder combined with Random sampling method are represented.
Figure 2. Statistical results of data collection and sampling strategies for core collection construction of 146 plant species. (a) Data collection; (b) Sampling strategies. Here, R random sampling, CD Multiple cluster deviation sampling, CP Multiple clusters preferred sampling, CR Multiple cluster random sampling, LDSS Least distance stepwise sampling, MLST Maximum length sub-tree method, MM M strategy algorithm of MSTRAT, MP M strategy algorithm of PowerCore(National Institute of Agricultural Biotechnology, Suwon, South Korea), MC M strategy algorithm of CoreFinder, CH Core Hunter method, GC GenoCore method, SAGD Simulated annealing algorithm based on the maximizing genetic diversity, SANA Simulated annealing algorithm based on maximizing allelic richness, CM Multiple cluster most representative sampling, GD Genetic diversity based on Genetic Subsetter, CP and CH Multiple clusters preferred sampling combined with Core Hunter method, and MC and R M strategy algorithm of Core Finder combined with Random sampling method are represented.
Forests 14 00926 g002
Table
Table 1. List of plant species that have been core collection-developed in recent years.
Table 1. List of plant species that have been core collection-developed in recent years.
Species CategoryName
Grain
crops		Cerealsmaize [21,22,23,24], sorghum [25], coix [26], hulless barley [27], rice [28], wheat [9,29], oat [30], buckwheat [31], pearl millet [32], foxtail millet [33], peanut [34]
Potatossweet potato [35], cassava [36]
Pulseschickpea [37], Pigeonpea [38], lima bean [39], soybean [40,41], rice bean [42], commom bean [43], faba bean [44], mung bean [45]
Horticultural
crops		Vegetablescauliflower [46], rapeseed [47], Cabbage [48], tomato [49,50], spinach [51], amaranth [52], bitter gourd [53], Jerusalem artichoke [54], yam [55], cucumber [56], pumpkin [57], white gourd [58], pepper [59], sweet pepper [60], eggplant [61], radish [62], Turnip [63], oyster mushroom [64], perilla [65], Pyropia haitanensis [66]
Fruitspricot [67,68,69,70], pear [71,72], jujube [73,74,75], grape [76], melon [77], watermelon [78], kiwifruit [79], pomegranate [80], litchi [81,82], olive [83], apple [84,85,86], peach [87], cherimoya [88], fig [89], sweet cherry [90], pomelo [91], persimmon [92], sugarcane [93]
Ornamental
plants		Cymbidium ensifolium [94], Chrysanthemum morifolium [95,96], Prunus mume [97], Chimonanthus praecox [98], Rosa rugosa [99], Lilium brownii [100], Paeonia suffruticosa [101], Lagerstroemia indica [102], Helianthus annuus [103], Sophora moorcroftiana [104]
HerbsFallopia multiflora [105], Astragalus [106], Scutellaria baicalensis [107], Angelica biserrata [108], Glycyrrhiza [1], Cornus officinalis [109], Dalbergia Odorifera [110]
SpiceSantalum album [20]
TeasGuizhou tea [111], Chinese tea [112,113]
BeveragesCoffee [114], Theobroma cacao [115]
Fiberscotton [116], upland cotton [117], island cotton [118], ramie [119]
Oilseedssafflower [120], sesame [121]
ForagesBuchloe dactyloides [13], Cynodon [14], Medicago truncatula [122], Bromus inermis [15]
TreesCatalpa bungei [123], Catalpa fargesii [124], Saccharum spontaneum [125], Populus deltoides [126], Populus tomentosa [18], Cinnamomum camphora [127], Phoebe bournei [128], Robinia pseudoacacia [17], Torreya grandis [129], Tetracentron sinense [130], Xanthoceras sorbifolia [131], schima superba [132], Sapium sebiferum [133], Fraxinus chinensis [134], Eucommia ulmoides [135], Saccharum arundinaceum [136], Corylus avellana [137], Juglans regia [138,139], Betula platyphylla [140], Betula luminifera [141], Sinojackia huangmeiensis [142], Castanopsis hystrix [143], Morus alba [144], Castanea mollissima [145], Castanea sativa [146], Cunninghamia lanceolata [16], Cryptomeria japonica [147], Eucalyptus cloeziana [148], Eucalyptus urophylla [149], Ceratonia siliqua [150], Argania spinosa [151], Pinus massoniana [19], Pinus yunnanensis [152], Ginkgo biloba [153], Akebia trifoliata [154], Camellia oleifera [155], Cornus wilsoniana [156]
Table
Table 2. Disease and the number of resistant QTLs/genes/resistant samples in representative crop core collections.
Table 2. Disease and the number of resistant QTLs/genes/resistant samples in representative crop core collections.
Core CollectionNo. of SampleType of DiseaseNo. of QTLs/Genes/
Resistant Samples		Reference
rice150sheath blight13 genes[188]
rice510bacterial leaf streak69 QTLs[189]
maize183fusarium ear rots4 genes[190]
wheat121tan spot Pt race 110 QTLs[191]
tan spot Pt race 55 QTLs
stagonospora nodorum blotch5 QTLs
wheat331leaf rust11 QTLs[192]
barley159powdery mildew58 resistant accessions[193]
leaf rust42 resistant accessions
net blotch2 resistant accessions
mild mosaic virus13 resistant accessions
yellow dwarf virus32 resistant accessions
sorghum318anthracnose4 QTLs[194]
sorghum242anthracnose13 resistant accessions[195]
leaf blight27 resistant accessions
leaf rust6 resistant accessions
anthracnose, leaf blight, leaf rust3 resistant accessions
common bean211powdery mildew4 genes[196]
common bean315cyst nematode HG Type 011 SNPs[197]
common bean168bacterial wilt14 genes[198]
mungbean296dry root rot29 resistant accessions[199]
cowpea375Aphis craccivora Koch pest3 resistant accessions[200]
pigeonpea146fusarium wilt6 resistant accessions[201]
sterility mosaic24 resistant accessions
fusarium wilt and sterility mosaic5 resistant accessions
lentils188pea aphid14 genes and 13 SNPs[202]
groundnut213groundnut rosette32 SNPs[203]
peanut99aflatoxin16 SNPs[204]
upland cotton419verticillium wilt12 resistant accessions[205]
flax447powdery mildew1 gene and 3 QTLs[206]
oilseed rape166blackleg8 QTLs[207]
tomato171yellow leaf curl virus2 genes[183]
apple176apple scab10 genes[208]
watermelon35fusarium wilt1 SRAPs[209]
melon4powdery mildew112 SNPs and 12 InDels[210]
safflower84fusarium wilt3 AFLPs and 1 SSRs[211]
Table
Table 3. Marker-trait association using core collections in representative crops.
Table 3. Marker-trait association using core collections in representative crops.
Core CollectionNo. of SampleTraitNo. of QTLs/GenesReference
rice150salt tolerance65 QTLs[212]
rice191yield and heavy metal content250 QTLs[213]
agronomic traits97 QTLs
rice150agronomic32 QTLs[214]
rice150cold tolerance26 QTLs[215]
soybean224agronomic16 QTLs[216]
soybean189yield and yield component19 QTLs[217]
soybean23salt tolerance22 QTLs[218]
low temperature tolerance15 QTLs
soybean159high oil content6 QTLs[219]
high protein content1 QTLs
drought tolerance5 QTLs
soybean46low temperature resistant13 QTLs[220]
wheat568yield and yield components17 QTLs[221]
upland cotton419fiber-related5 genes[222]
leaf heads of cabbage150agronomic18 QTLs[181]
tomato360agronomic2 QTLs[223]
melon79fruit quality241 QTLs[224]
sugarcane97yield components and sucrose56 QTLs[225]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Gu, R.; Fan, S.; Wei, S.; Li, J.; Zheng, S.; Liu, G. Developments on Core Collections of Plant Genetic Resources: Do We Know Enough? Forests 2023, 14, 926. https://doi.org/10.3390/f14050926

AMA Style

Gu R, Fan S, Wei S, Li J, Zheng S, Liu G. Developments on Core Collections of Plant Genetic Resources: Do We Know Enough? Forests. 2023; 14(5):926. https://doi.org/10.3390/f14050926

Chicago/Turabian Style

Gu, Rui, Shaohui Fan, Songpo Wei, Jiarui Li, Shihui Zheng, and Guanglu Liu. 2023. "Developments on Core Collections of Plant Genetic Resources: Do We Know Enough?" Forests 14, no. 5: 926. https://doi.org/10.3390/f14050926

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop