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Article

Genetic Diversity Assessment of Sweetpotato Germplasm in China Using InDel Markers

by
Lukuan Zhao
1,2,
Zhanghua Qi
3,
Shizhuo Xiao
2,
Fen Tang
4,
Yang Liu
1,2,
Yitong Deng
1,2,
Xibin Dai
1,2,
Zhilin Zhou
2,
Wenjun Ou
5 and
Qinghe Cao
2,*
1
School of Life Science, Jiangsu Normal University, Xuzhou 221116, China
2
Xuzhou Institute of Agricultural Sciences in Jiangsu Xuhuai District, Xuzhou 221131, China
3
The First Clinical Medical College, Guangdong Medical University, Zhanjiang 524023, China
4
College of Tropical Crops, Hainan University, Haikou 570228, China
5
Tropical Crops Genetic Resources Institute, Chinese Academy of Tropical Agricultural Sciences, Haikou 571101, China
*
Author to whom correspondence should be addressed.
Agronomy 2022, 12(12), 3074; https://doi.org/10.3390/agronomy12123074
Submission received: 8 November 2022 / Revised: 29 November 2022 / Accepted: 2 December 2022 / Published: 5 December 2022
(This article belongs to the Special Issue Genetic Diversity and Population Structure in Crop and Woody Plants)
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Abstract

:
Sweetpotato (Ipomoea batatas (L.) Lam.), whose roots are rich in starch, is widely grown around the world and plays a prominent role in ensuring food security. At present, there are no reports on the genetic diversity of sweetpotato germplasm revealed by InDel markers. In this study, we developed a set of 30 InDel markers to evaluate the genetic diversity and relationships of 240 accessions, comprising 77 landraces, 80 introduced accessions, 82 improved varieties released in China, and a diploid wild relative Ipomoea trifida. A total of 94 reliable loci were obtained, with a mean of 3.13 loci per primer, and the PIC value ranged from 0.143 to 0.821. The whole population could be divided into three sub-populations according to a structure analysis based on the Bayesian model, which was consistent with the results of principal component analysis (PCA). A neighbor-joining tree was constructed based on Nei’s genetic distance ranging from 0 to 0.556 and discriminated the panel of the population into three main groups (Ⅰ, Ⅱ, Ⅲ). Group Ⅲ was further split into seven subgroups (ⅢA–ⅢG). The clustering pattern of the 240 accessions was unrelated to their geographic origins. Most of the accessions, whether landraces, improved varieties released in China or introduced germplasm, were mixed, which revealed the high level of genetic similarity among accessions from different regions. There was little difference in the level of genetic diversity between landraces and improved varieties, which was probably due to the exchange and utilization of accessions from different regions. More efforts should be made to collect and utilize sweetpotato germplasm resources and further broaden the genetic basis of sweetpotato cultivars.

1. Introduction

Germplasm collection and conservation are the basis of crop breeding programs. Faced with numerous germplasm resources, germplasm evaluation should be conducted before utilization. A better knowledge of the genetic diversity and population structure of germplasm will help enable effective conservation and breeding strategies.
Sweetpotato (Ipomoea batatas (L.) Lam.) is a root crop that has characteristics of high yield, wide ecological adaptability, and drought tolerance. In addition to being rich in starch, sweetpotato also contains carotenoids, anthocyanins, and other valuable substances that play an essential role in reducing hunger, alleviating malnutrition and ensuring food security [1,2,3]. Besides the storage roots of sweetpotato, its leaves and shoot tips are more abundant in proteins and polyphenols, making it a tasty vegetable in China and Korea [2]. A growing interest in the health benefits of sweetpotato has promoted its production and consumption [3]. At present, sweetpotato is the seventh most important food crop in terms of yield around the world. It is cultivated extensively throughout the world, especially in developing countries. In China, sweetpotato yield was about 49.2 million tons, accounting for more than 50% of the total yield worldwide in 2020 [4].
Owing to its hexaploid and complex genome, sweetpotato omics studies lag behind those of major food crops [3]. The origin and domestication of sweetpotato remain unclear. However, it is generally believed that central and south America is the origin center of sweetpotato, and the secondary origin centers have been formed in Asia, Africa, and Oceania [5]. Sweetpotato is not native to China, and it was first introduced to the south of China at the end of the 16th century, then spread to other parts of the country [6]. In addition, diploid Ipomoea trifida is the putative ancestor of sweetpotato, and its genome serves as the reference genome in some sweetpotato studies [7,8,9].
Molecular markers are used as genomic tools, of which there are various types based on DNA alterations, such as restriction fragment length polymorphisms (RFLPs), simple sequence repeats (SSRs), insertions and deletions (InDels), and single nucleotide polymorphisms (SNPs). Molecular markers have been widely used in variety identification, genetic diversity assessment, and association analysis [10,11,12]. In recent years, SSR markers have played a central role in the evaluation of sweetpotato germplasm resources. Zawedde et al. [11] assessed 260 sweetpotato germplasm resources in Uganda and determined the relationship between landraces and sweetpotato cultivars using SSR markers. Anglin et al. [13] identified about 6000 sweetpotato accessions in the International Potato Center (CIP) collection with a set of 20 SSR markers providing evidence that sweetpotato originated from Peru, Ecuador, and Africa. Among these molecular markers, increasing attention has been given to InDel markers. Wang et al. [14] constructed a genetic linkage map of soybean with 300 polymorphic InDel markers. Liu et al. [15] developed novel InDel markers associated with disease resistance traits in peanut. To date, no research has been reported on the genetic diversity assessment of sweetpotato based on InDel markers. Furthermore, the genetic relationship among landraces, varieties released in China, and introduced sweetpotato germplasm has not been studied. In this study, the genetic diversity of sweetpotato germplasm from broad geographical regions was evaluated using novel InDel markers.

2. Materials and Methods

2.1. Plant Material

A total of 240 accessions were evaluated in this study, comprising 77 landraces, 82 improved varieties, 80 introduced accessions, and one wild germplasm resource. Of the 80 introduced accessions, 46 accessions were from the CIP, 34 accessions were from the USA, Brazil, Japan, and other counties, and 159 accessions were from 18 provinces of China. According to the geographical distribution of 240 accessions, they were divided into six groups (CH-Lan, landraces in China; CH-Var, improved varieties in China; AME, from the CIP, the USA, and Brazil; EA, from the Asian Vegetable Research and Development Centre, Japan, and Korea; AFR, from the International Institute of Tropical Agriculture, and Morocco; SEA, from Indonesia, Philippines, Cambodia, and Vanuatu). The geographical distribution of 240 accessions is listed in Supplementary Table S1.

2.2. DNA Extraction and InDel Genotyping

Fresh leaves were collected, frozen in liquid nitrogen, and stored at −80 °C. Genomic DNA was isolated from frozen leaves using the modified cetyltrimethylammonium bromide (CTAB) method [16]. The concentration and quality of the DNA samples were measured using NanoDrop 1000 (Thermo Fisher Scientific, Wilmington, DE, USA). DNA samples were diluted to 50 ng/μL for InDel analysis. PCR amplification was performed using a set of 30 InDel primers designed and selected by our group [17]. The panel of 30 InDel primers covered the haploidy chromosomes (n = 15) of sweetpotato, and the specific primer information is presented in Table 1. The total reaction volume was 20 μL, and contained 10 μL 2 × Taq mix (CWBIO, Taizhou, Jiangsu, China), 1.0 μL forward primers (10 μM), 1.0 μL reverse primers (10 μM), and 2.0 μL DNA template. The PCR conditions were as follows: 94 °C for 5 min, 35 cycles of denaturation at 94 °C for 30 s, annealing at 55–56 °C for 30 s and polymerization at 72 °C for 45 s; and a final extension at 72 °C for 5 min. The PCR products were separated by capillary electrophoresis on a fragment analyzer (Agilent Technologies, Inc., Santa Clara, CA, USA).

2.3. Allele Scoring and Data Analysis

The sizes of the amplified bands were scored according to internal standard DNA, and the InDel data were transformed into binary arrays of the presence (1) or absence (0) of an allele for each individual using PROsize 3.0 software. Analysis of molecular variance (AMOVA) was performed using GenAlex version 6.5 to estimate the variance among accessions within or between regions [18]. The Na (observed number of alleles per locus), Ne (effective number of alleles per locus) and Ave. het (the mean heterozygosity per locus) were estimated by Popgene version 1.32 [19]. We analyzed the population structure of the 240 accessions using Structure v2.3.4 [20]. The binary data were subjected to calculating genetic distance (Nei&Li (Czekanowski (1913)) using DPS 7.05 software [21]. Cluster analysis was conducted based on genetic distance (Nei&Li (Czekanowski (1913)) using the neighbor-joining (NJ) method [22,23], and principal component analysis (PCA) was performed using Origin 2018 [24].

3. Results

3.1. InDel Marker Polymorphism and Analysis of Molecular Variance (AMOVA)

A total of 93 polymorphic loci were generated from 240 accessions using 30 InDel markers. The number of alleles per primer ranged from 2 to 8, with an average of 3.1. Primer Ib-6-2 produced the most bands (eight bands). The polymorphic information content (PIC) of each primer spanned from 0.143 to 0.821 (Table 1). Analysis of molecular variance (AMOVA) indicated that only 3% difference among the six groups (CH-Lan, CH-Var, AME, EA, AFR, and SEA) contributed to the total variance. The largest contribution to the total variance was 97% differences among accessions within groups (Table 2).

3.2. Population Structure and Principal Component Analysis

To acknowledge the proportion of an individual’s genome, STRUCTURE software was run with successive K values based on the Bayesian model [25]. The LnP(D) values, ‘log probability of data’, increased continuously as the K values varied from 1 to 15. The highest Delta K was obtained at K = 3, suggesting that the population could be divided into three sub-populations: Pop1, Pop2, and Pop3 (Figure 1). Pop1 contained 24 accessions, of which 1 was from America, 2 were from East Asia, and the rest were from China. Pop2 consisted of 88 accessions from Southeast Asia (n = 2), Africa (n = 3), East Asia (n = 5, except China), America (n = 8), and the remaining 70 accessions were landraces or improved varieties from China. Pop3 was composed of 128 accessions, which were from all the studied regions. Overall, each individual was more or less heterogeneous in the genome. All sub-populations were composed of accessions from different agro-ecological regions, and contained different types of germplasm resources (landraces, improved varieties, and introduced cultivars), indicating the highly complex nature of sweetpotato in China.
Principal component analysis (PCA) also divided the whole panel into three groups, which was consistent with the results of the population structure. Most accessions from different regions clustered together into mixed groups (Figure 2).

3.3. Genetic Distance and Phylogenetic Relationship Analysis

The genetic distance between 240 accessions ranged from 0 to 0.556, with an average of 0.208. Within the six groups, the genetic distance between accessions was close to average. The accessions within the SEA group had the greatest genetic variation (0.250), whereas the shortest genetic variation (0.169) was the ARF group. Meanwhile, the AME and SEA groups had the greatest genetic variation (0.232), and the lowest was the ARF and CH-Lan groups (Table 3).
To evaluate the relationships and similarities between accessions, a neighbor-joining tree was constructed based on Nei’s coefficient calculated from InDel data. Akin to population structure analysis, the whole population was divided into three groups (Ⅰ, Ⅱ, Ⅲ), while group Ⅲ was further split into seven subgroups (ⅢA–ⅢG) (Figure 3). Diploid wild relative I. trifida was clustered in the group Ⅰ, along with seven improved varieties, three landraces and only one introduced accession. Group Ⅱ, similar to group Ⅰ, contained 12 accessions. The majority of sweetpotato accessions were clustered in group Ⅲ and about half of the CIP’s accessions clustered in subgroup ⅢF together with improved varieties from China. Some accessions from the same regions were grouped together into the same subgroups of the tree, such as ⅢC and ⅢF. In addition, sweetpotato accessions of both Pop2 and Pop3 were scattered into some groups or subgroups according to population structure, while all accessions of Pop1 were clustered in subgroup ⅢG.

4. Discussion

4.1. Characteristics of InDel Markers and Analysis of Molecular Variance (AMOVA)

InDel markers are widely distributed in the genome and have the characteristics of easy detection and co-dominance. In this study, InDel markers were used to evaluate the genetic diversity of sweetpotato germplasm preserved in China. A total of 94 bands were detected in 240 accessions using 30 pairs of primers, of which 93 bands were polymorphic. An average of 3.1 polymorphic bands were amplified for each pair of primers, and the PIC value was between 0.143 and 0.821. A set of 30 InDel markers in this study showed a relatively high level of polymorphism. The polymorphism level of the InDel markers was higher than the SSR markers in some crops, such as sesame [26]. However, the InDel markers observed in this study were not more polymorphic than SSR markers in sweetpotato accessions. Monteros-Altamirano et al. (2021) amplified 89 alleles in 386 sweetpotato genotypes using eight SSR markers [27]. A total of 88 alleles were generated in 48 Tanzanian sweetpotato accessions by nine SSR markers [28]. In contrast, the polymorphism level of the InDel markers was moderate and lower than that of the multi-locus markers, such as ISSR and SSR [13,27,28]. However, for complex genomes, SSR markers may produce more ambiguous bands and genotyping errors than InDel markers. Furthermore, compared with SNP markers, InDel markers are more practical due to the lack of SNP genotyping equipment [29].
The 240 accessions were defined into six groups (CH-Lan, CH-Var, AME, EA, AFR, and SEA) according to their geographical distributions. AMOVA revealed that variation among accessions within groups accounted for 97% of the total variation, and the remaining 3% of variation came from the differences between groups. The same results were found in other studies using different molecular markers [5,30]. These results showed the complex genome of sweetpotato and the exchange of accessions between different regions.

4.2. Population Structure and Principal Component Analysis

The whole population of 240 accessions was categorized into three major sub-populations (Pop1, Pop2, and Pop3) based on the Bayesian model. Pop1, Pop2, and Pop3 contained 24, 88, and 128 accessions, respectively. All three sub-populations contained improved varieties and landraces, revealing no substantial difference, which was in contrast to previous reports [31]. Ipomoea trifida, as the most probable diploid ancestor of sweetpotato, was grouped with most of the accessions into the Pop3 sub-population. We classified individuals based on the maximum Q value. However, with a Q value of 0.65 as the boundary, 83 accessions had a high degree of admixture in the genome [3]. All accessions, even the diploid I. trifida, contained the genetic components of the three sub-populations, indicating the heterozygous genetic background of sweetpotato. A similar pattern was observed in sweetpotato accessions from Uganda [11].
PCA was consistent with the population structure analysis. Pop1, Pop2, and Pop3 were distinguished in the scatter plot, especially Pop1. The pattern of PCA did not match the geographic origins of accessions.

4.3. Number of Alleles and Genetic Distance among Different Regions

Sweetpotato was domesticated in central and south America about 8000 years ago [32]. We compared the number of alleles of 55 accessions introduced from America and 159 accessions from China. Fifty-five American accessions harbored 94 alleles while 159 Chinese accessions had 93 alleles using 30 InDel markers. The average genetic distance of 55 American accessions was also greater than that of 159 local accessions in China. The reduction of alleles and narrow genetic distances revealed the loss of genetic diversity in sweetpotato during domestication and spread. This phenomenon was also observed in other crops, such as rice and millet [33,34]. The highest genetic distance was observed in the SEA group (0.250), followed by the AME group (0.216), and the lowest level of genetic variation was observed in the ARF group (0.169). Between the groups, the AME and SEA groups had the greatest genetic variation (0.232), and the closest distance was observed between the ARF and CH-Lan groups (0.176). However, these data need to be further confirmed by collecting and evaluating more samples from African and Southeast Asian regions.
In addition, we compared the number of alleles between improved varieties and landraces in China. A total of 93 alleles were detected in the improved varieties, while the landraces contained 92 alleles using 30 InDel markers. The genetic variation of the landraces was smaller than that of the improved varieties. In general, the number of alleles in improved varieties should not be greater than that in landraces for aggregation of favorable genes and elimination of unfavorable genes in the breeding process [35,36]. The utilization of introduced germplasm resources enriched the diversity of improved varieties. In addition, 42 of the 77 landraces in this study were from Guangdong province. Our results supported the hypothesis that modern sweetpotato cultivars in China were formed in two steps. First, sweetpotato cultivars spread to Guangdong as landraces and then hybridized to generate modern cultivars [30,37]. More evidence is required to explore the domestication and spread of sweetpotato cultivars in China.

4.4. Phylogenetic Relationship Analysis

To better determine the genetic relationships among 240 accessions, a neighbor-joining tree was constructed based on Nei’s genetic distance. Phylogenetic relationship analysis also separated the whole population into three groups. Both groups Ⅰ and Ⅱ had fewer number of accessions (12 for each). Group Ⅲ contained 216 accessions and was further divided into seven subgroups. Not surprisingly, the diploid I. trifida was distantly related to most cultivars. Among the 55 accessions from the Americas, 26 accessions had close relationships and clustered in the ⅢF subgroup. The accessions from the same region were scattered in the phylogenetic tree. Therefore, clustering results had no association with the origins of the accessions, which was in line with previous reports [38,39,40]. Landraces and improved varieties were mixed in the phylogenetic tree indicating the relationships between them.
We compared the population structure with the phylogenetic relationship results. As expected, the results of the population structure analysis were not completely consistent with those of the phylogenetic relationship analysis. All the accessions in Pop1 clustered together, while some accessions in Pop2 and Pop3 were not grouped together. Regardless of the differences in algorithms of population structure and phylogenetic relationship analysis, some accessions did not assimilate to specific subpopulations, which were similar to synthetic wheat [41].

5. Conclusions

Sweetpotato is important for ensuring food security because of its nutritional and functional value. A panel of 30 InDel markers with a high level of polymorphism was used to assess the genetic diversity of 240 accessions collected from all over the world. The genetic relationships among sweetpotato accessions were not consistent with their geographic origins. Our work provided comprehensive information for sweetpotato germplasm characterization and further facilitated the efficient conservation and utilization of sweetpotato germplasm resources.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agronomy12123074/s1, Table S1: List of 240 germplasm resources in Ipomoea.

Author Contributions

Conceptualization, L.Z. and Q.C.; data curation, Z.Q.; formal analysis, F.T., Y.L. and Y.D.; methodology, F.T., Y.L. and Y.D.; project administration, Q.C., Z.Z. and W.O.; resources, X.D., Z.Z. and W.O.; writing—original draft, L.Z.; writing—review and editing, Q.C. and S.X. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Key Research and Development Program of China (2018YFD1000705/2018YFD1000700), the Earmarked Fund for CARS-10-Sweetpotato (CARS-10-GW01), the “JBGS” Project of Seed Industry Revitalization in Jiangsu Province (JBGS (2021) 010), and the “AGRP” Project of Collection, preservation and innovative utilization of tropical sweet potato germplasm resources in Hainan Province (Qiong Cainong [2020]829).

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Schafleitner, R.; Tincopa, L.R.; Palomino, O.; Rossel, G.; Robles, R.F.; Alagon, R.; Rivera, C.; Quispe, C.; Rojas, L.; Pacheco, J.A.; et al. A sweetpotato gene index established by de novo assembly of pyrosequencing and Sanger sequences and mining for gene-based microsatellite markers. BMC Genom. 2010, 11, 604. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  2. Bovell-Benjamin, A.C. Sweet Potato: A Review of its Past, Present, and Future Role in Human Nutrition. In Advances in Food and Nutrition Research; Academic Press: Cambridge, MA, USA, 2007; Volume 52, pp. 1–59. [Google Scholar]
  3. Wadl, P.A.; Olukolu, B.A.; Branham, S.E.; Jarret, R.L.; Yencho, G.C.; Jackson, D.M. Genetic Diversity and Population Structure of the USDA Sweetpotato (Ipomoea batatas) Germplasm Collections Using GBSpoly. Front. Plant Sci. 2018, 9, 1166. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  4. Food and Agriculture Organization of the United Nations. FAOSTAT Statistical Database. FAO. 2020. Available online: https://www.fao.org/faostat/en/#data (accessed on 27 August 2022).
  5. Gichuki, S.T.; Berenyi, M.; Zhang, D.; Hermann, M.; Schmidt, J.; Glössl, J.; Burg, K. Genetic diversity in sweetpotato [Ipomoea batatas (L.) Lam.] in relationship to geographic sources as assessed with RAPD markers. Genet. Resour. Crop Evol. 2003, 50, 429–437. [Google Scholar] [CrossRef]
  6. O’Brien, P.J. The Sweet Potato: Its Origin and Dispersal. Am. Anthropol. 1972, 74, 342–365. [Google Scholar] [CrossRef]
  7. Feng, J.Y.; Li, M.; Zhao, S.; Zhang, C.; Yang, S.T.; Qiao, S.; Tan, W.F.; Qu, H.J.; Wang, D.Y.; Pu, Z.G. Analysis of evolution and genetic diversity of sweetpotato and its related different polyploidy wild species I. trifida using RAD-seq. BMC Plant Biol. 2018, 18, 181. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  8. Okada, Y.; Monden, Y.; Nokihara, K.; Shirasawa, K.; Isobe, S.; Tahara, M. Genome-Wide Association Studies (GWAS) for Yield and Weevil Resistance in Sweet potato (Ipomoea batatas (L.) Lam). Plant Cell Rep. 2019, 38, 1383–1392. [Google Scholar] [CrossRef]
  9. Zhang, L.; Yu, Y.; Shi, T.; Kou, M.; Sun, J.; Xu, T.; Li, Q.; Wu, S.; Cao, Q.; Hou, W.; et al. Genome-wide analysis of expression quantitative trait loci (eQTLs) reveals the regulatory architecture of gene expression variation in the storage roots of sweet potato. Hortic. Res. 2020, 7, 90. [Google Scholar] [CrossRef] [PubMed]
  10. Yada, B.; Brown-Guedira, G.; Alajo, A.; Ssemakula, G.; Mwanga, R.; Yencho, C. Simple Sequence Repeat Marker Analysis of Genetic Diversity among Progeny of a Biparental Mapping Population of Sweetpotato. HortScience 2015, 50, 1143–1147. [Google Scholar] [CrossRef] [Green Version]
  11. Zawedde, B.M.; Ghislain, M.; Magembe, E.; Amaro, G.B.; Grumet, R.; Hancock, J. Characterization of the genetic diversity of Uganda’s sweet potato (Ipomoea batatas) germplasm using microsatellites markers. Genet. Resour. Crop Evol. 2015, 62, 501–513. [Google Scholar] [CrossRef] [Green Version]
  12. Pereira-Dias, L.; Vilanova, S.; Fita, A.; Prohens, J.; Rodríguez-Burruezo, A. Genetic diversity, population structure, and relationships in a collection of pepper (Capsicum spp.) landraces from the Spanish centre of diversity revealed by genotyping-by-sequencing (GBS). Hortic. Res. 2019, 6, 54. [Google Scholar] [CrossRef]
  13. Anglin, N.L.; Robles, R.; Rossel, G.; Alagon, R.; Panta, A.; Jarret, R.L.; Manrique, N.; Ellis, D. Genetic Identity, Diversity, and Population Structure of CIP’s Sweetpotato (I. batatas) Germplasm Collection. Front. Plant Sci. 2021, 12, 1860. [Google Scholar] [CrossRef] [PubMed]
  14. Wang, J.; Kong, L.; Yu, K.; Zhang, F.; Shi, X.; Wang, Y.; Nan, H.; Zhao, X.; Lu, S.; Cao, D.; et al. Development and validation of InDel markers for identification of QTL underlying flowering time in soybean. Crop J. 2018, 6, 126–135. [Google Scholar] [CrossRef]
  15. Liu, L.; Dang, P.M.; Chen, C.Y. Development and Utilization of InDel Markers to Identify Peanut (Arachis hypogaea) Disease Resistance. Front. Plant Sci. 2015, 6, 988. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  16. Allen, G.C.; Flores-Vergara, M.A.; Krasynanski, S.; Kumar, S.; Thompson, W.F. A modified protocol for rapid DNA isolation from plant tissues using cetyltrimethylammonium bromide. Nat. Protoc. 2006, 1, 2320–2325. [Google Scholar] [CrossRef]
  17. Xiao, S.; Dai, X.; Zhao, L.; Zhou, Z.; Zhao, L.; Xu, P.; Gao, B.; Zhang, A.; Zhao, D.; Yuan, R.; et al. Resequencing of sweetpotato germplasm resources reveals key loci associated with multiple agronomic traits. Hortic. Res. 2022. [Google Scholar] [CrossRef]
  18. Peakall, R.; Smouse, P.E. GenAlEx 6.5: Genetic analysis in Excel. Population genetic software for teaching and research--an update. Bioinformatics 2012, 28, 2537–2539. [Google Scholar] [CrossRef] [Green Version]
  19. Yeh, F.; Boyle, T. Population genetic analysis of co-dominant and dominant markers and quantitative traits. Belg. J. Bot. 1996, 129, 157. [Google Scholar]
  20. Porras-Hurtado, L.; Ruiz, Y.; Santos, C.; Phillips, C.; Carracedo, A.; Lareu, M.V. An overview of STRUCTURE: Applications, parameter settings, and supporting software. Front. Genet. 2013, 4, 98. [Google Scholar] [CrossRef] [Green Version]
  21. Tang, Q.-Y.; Zhang, C.-X. Data Processing System (DPS) software with experimental design, statistical analysis and data mining developed for use in entomological research. Insect Sci. 2013, 20, 254–260. [Google Scholar] [CrossRef] [PubMed]
  22. Saitou, N.; Nei, M. The neighbor-joining method: A new method for reconstructing phylogenetic trees. Mol. Biol. Evol. 1987, 4, 406–425. [Google Scholar]
  23. Tomiuk, J.; Graur, D. Nei’s Modified Genetic Identity and Distance Measures and their Sampling Variances. Syst. Biol. 1988, 37, 156–162. [Google Scholar] [CrossRef]
  24. Moberly, J.G.; Bernards, M.T.; Waynant, K.V. Key features and updates for Origin 2018. J. Cheminf. 2018, 10, 5. [Google Scholar] [CrossRef]
  25. Evanno, G.; Regnaut, S.; Goudet, J. Detecting the number of clusters of individuals using the software STRUCTURE: A simulation study. Mol. Ecol. 2005, 14, 2611–2620. [Google Scholar] [CrossRef] [Green Version]
  26. Wu, K.; Yang, M.; Liu, H.; Tao, Y.; Mei, J.; Zhao, Y. Genetic analysis and molecular characterization of Chinese sesame (Sesamum indicum L.) cultivars using Insertion-Deletion (InDel) and Simple Sequence Repeat (SSR) markers. BMC Genet. 2014, 15, 35. [Google Scholar] [CrossRef] [Green Version]
  27. Monteros-Altamirano, A.; Paredes, D.; Buitrón-Bustamante, J.; Tapia, C.; Peña, G. Genetic diversity of sweet potatoes [Ipomoea batatas (L) Lam.] in Ecuador. Genet. Resour. Crop Evol. 2021, 68, 307–320. [Google Scholar] [CrossRef]
  28. Ngailo, S.; Shimelis, H.; Sibiya, J.; Amelework, B.; Mtunda, K. Genetic diversity assessment of Tanzanian sweetpotato genotypes using simple sequence repeat markers. S. Afr. J. Bot. 2016, 102, 40–45. [Google Scholar] [CrossRef]
  29. Liu, B.; Wang, Y.; Zhai, W.; Deng, J.; Wang, H.; Cui, Y.; Cheng, F.; Wang, X.; Wu, J. Development of InDel markers for Brassica rapa based on whole-genome re-sequencing. Theor. Appl. Genet. 2013, 126, 231–239. [Google Scholar] [CrossRef] [PubMed]
  30. Yang, X.-s.; Su, W.-j.; Wang, L.-j.; Lei, J.; Chai, S.-s.; Liu, Q.-c. Molecular diversity and genetic structure of 380 sweetpotato accessions as revealed by SSR markers. J. Integr. Agr. 2015, 14, 633–641. [Google Scholar] [CrossRef]
  31. 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]
  32. Lebot, V. Sweet Potato. In Root and Tuber Crops; Bradshaw, J.E., Ed.; Springer: New York, NY, USA, 2010; pp. 97–125. [Google Scholar]
  33. Jia, G.; Liu, X.; Schnable, J.C.; Niu, Z.; Wang, C.; Li, Y.; Wang, S.; Wang, S.; Liu, J.; Guo, E.; et al. Microsatellite Variations of Elite Setaria Varieties Released during Last Six Decades in China. PLoS ONE 2015, 10, e0125688. [Google Scholar] [CrossRef] [Green Version]
  34. Huang, X.; Kurata, N.; Wei, X.; Wang, Z.-X.; Wang, A.; Zhao, Q.; Zhao, Y.; Liu, K.; Lu, H.; Li, W.; et al. A map of rice genome variation reveals the origin of cultivated rice. Nature 2012, 490, 497–501. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  35. Tang, R.; Cui, D.; Zhou, J.; Li, W.; Ma, X.; Han, B.; Guo, X.; Zhao, Z.; Han, L. Comparative analysis of genetic diversity of rice (Oryza sativa L.) varieties cultivated in different periods in China. Genet. Resour. Crop Evol. 2021, 68, 1439–1451. [Google Scholar] [CrossRef]
  36. Liu, M.; Xu, Y.; He, J.; Zhang, S.; Wang, Y.; Lu, P. Genetic Diversity and Population Structure of Broomcorn Millet (Panicum miliaceum L.) Cultivars and Landraces in China Based on Microsatellite Markers. Int. J. Mol. Sci. 2016, 17, 370. [Google Scholar] [CrossRef] [PubMed]
  37. He, X.; Liu, Q.; Wang, Y.; Zhai, H. Analysis of genetic diversity of sweetpotato landraces in China. Sci. Agric. Sin. 2005, 38, 250–257. [Google Scholar]
  38. Zhang, D.; Cervantes, J.; Huamán, Z.; Carey, E.; Ghislain, M. Assessing genetic diversity of sweet potato (Ipomoea batatas (L.) Lam.) cultivars from tropical America using AFLP. Genet. Resour. Crop Evol. 2000, 47, 659–665. [Google Scholar] [CrossRef]
  39. Karuri, H.; Ateka, E.; Amata, R.U.; Nyende, A.B.; Muigai, A.; Mwasame, E.; Gichuki, S. Evaluating Diversity among Kenyan Sweet Potato Genotypes Using Morphological and SSR Markers. Int. J. Agric. Biol. 2010, 12, 33–38. [Google Scholar]
  40. Meng, Y.-S.; Zhao, N.; Li, H.; Zhai, H.; He, S.-Z.; Liu, Q.-C. SSR fingerprinting of 203 sweetpotato (Ipomoea batatas (L.) Lam.) varieties. J. Integr. Agric. 2018, 17, 86–93. [Google Scholar] [CrossRef] [Green Version]
  41. Gordon, E.; Kaviani, M.; Kagale, S.; Payne, T.; Navabi, A. Genetic diversity and population structure of synthetic hexaploid-derived wheat (Triticum aestivum L.) accessions. Genet. Resour. Crop Evol. 2019, 66, 335–348. [Google Scholar] [CrossRef]
Agronomy 12 03074 g001 550
Figure 1. Population structure analysis of 240 accessions. (a) Mean lnP(K) (black) and Delta K(red) values for different assumed populations (K). The highest Delta K was obtained at K = 3, suggesting that the population could be divided into three sub-populations: Pop1, Pop2, and Pop3. (b) Q-values at K = 3 of 240 sweetpotato accessions.
Figure 1. Population structure analysis of 240 accessions. (a) Mean lnP(K) (black) and Delta K(red) values for different assumed populations (K). The highest Delta K was obtained at K = 3, suggesting that the population could be divided into three sub-populations: Pop1, Pop2, and Pop3. (b) Q-values at K = 3 of 240 sweetpotato accessions.
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Agronomy 12 03074 g002 550
Figure 2. Principal component analysis of 240 accessions based on 30 InDel markers. (a) The coloration of each variety was determined by the results of the population structure (Pop1 = red, Pop2 = green, and Pop3 = blue). (b) Each accession was colored according to its geographical origin (CH-Lan, landraces in China; CH-Var, improved varieties in China; AME, from the CIP, the USA, and Brazil; EA, from the Asian Vegetable Research and Development Centre, Japan, and Korea; AFR, from the International Institute of Tropical Agriculture, and Morocco; SEA, from Indonesia, Philippines, Cambodia, and Vanuatu. CH-Lan = blue, CH-Var = green, AME = red, EA = bright blue, AFR = pink, SEA = purple).
Figure 2. Principal component analysis of 240 accessions based on 30 InDel markers. (a) The coloration of each variety was determined by the results of the population structure (Pop1 = red, Pop2 = green, and Pop3 = blue). (b) Each accession was colored according to its geographical origin (CH-Lan, landraces in China; CH-Var, improved varieties in China; AME, from the CIP, the USA, and Brazil; EA, from the Asian Vegetable Research and Development Centre, Japan, and Korea; AFR, from the International Institute of Tropical Agriculture, and Morocco; SEA, from Indonesia, Philippines, Cambodia, and Vanuatu. CH-Lan = blue, CH-Var = green, AME = red, EA = bright blue, AFR = pink, SEA = purple).
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Agronomy 12 03074 g003 550
Figure 3. Neighbor-joining tree based on Nei’s genetic distances of 240 accessions using 30 InDel markers. The whole population was divided into three groups (Ⅰ, Ⅱ, Ⅲ), while group Ⅲ was further split into seven subgroups (ⅢA, ⅢB, ⅢC, ⅢD, ⅢE, ⅢF, and ⅢG). The labels of each accession were colored according to their geographical origins (CH-Lan = blue, CH-Var = green, AME = red, EA = bright blue, AFR = pink, SEA = purple). The branches of the tree were colored based on the results of the population structure (Pop1 = red, Pop2 = green, Pop3 = blue).
Figure 3. Neighbor-joining tree based on Nei’s genetic distances of 240 accessions using 30 InDel markers. The whole population was divided into three groups (Ⅰ, Ⅱ, Ⅲ), while group Ⅲ was further split into seven subgroups (ⅢA, ⅢB, ⅢC, ⅢD, ⅢE, ⅢF, and ⅢG). The labels of each accession were colored according to their geographical origins (CH-Lan = blue, CH-Var = green, AME = red, EA = bright blue, AFR = pink, SEA = purple). The branches of the tree were colored based on the results of the population structure (Pop1 = red, Pop2 = green, Pop3 = blue).
Agronomy 12 03074 g003
Table
Table 1. The information on the 30 InDel markers used in this study.
Table 1. The information on the 30 InDel markers used in this study.
NamePrimer SequencesSize (bp)BandsPIC 1NaNeAve.het
Ib-1-6F: TGAGATCCACGTTGAAGGTAGG188–22130.56531.6430.392
R: TGTTAAAGAAATGAGCTACCCACTG
Ib-1-7F: GCTAAAAAGTTCCAAAAACCTCCC190–22030.52331.6220.384
R: TCTTTGTCTCTGAAGTCTGGGC
Ib-2-6F: TCAACGGCTATTTCGTGTTCTC160–30050.60241.9980.500
R: TTTAGGGCAATCTCATGGGTCC
Ib-2-7F: TCAACTCGTACAAATCCGAATCCT160–17020.36321.7860.440
R: CCGGAGAAAACGCCTTTGTTAA
Ib-3-1F: TACGGTCGTGGGATTCCAAAAA173–18120.32221.4810.325
R: ACATTTGTTTCCTACTTGTGGTTGT
Ib-3-2F: GTGTGGGACTGTGTAATCACCA104–13550.75742.0570.514
R: ACAGTAAAGCCAGAAGTGCCAT
Ib-4-5F: TCCCTTCGTAGTACTTTTAGAGCT166–17620.31321.4190.295
R: ACAACCAGAAAGAAGTCATGCA
Ib-4-6F: TGTCAGATGAATAGATGGCGTCA162–18330.44331.6480.393
R: CCAGTTGCCATGGTAAAAGCAA
Ib-5-4F: TGGAAAGAGCTAGTAGATTAGCCT180–18820.24421.2320.188
R: AGGCCCTGTCTATACGAGATCA
Ib-5-5F: CCAACCTCAACTATTGCATGCC168–26130.52631.8620.463
R: TGTTTTCGCTACGTACTGCCTA
Ib-6-1F: CTTGACCACAGGGACTAGCATT184–25940.58942.1020.524
R: ATCAAGACAAATGGCACCTTGC
Ib-6-2F: CAGTGGAGGCTAGGTGAGAAAG139–20580.82141.2580.205
R: ACAAAGTTGTTCCCCTGTAGCT
Ib-7-7F: TACTCGAGAGGGGAATTGAAGC168–22330.44231.7310.422
R: TGCTGAGCAATAATAAGTGTGGT
Ib-7-8F: ATCAATCGATCCTTGGAAGGGT180–24340.62741.5640.361
R: AGCCTCATTCTTCGTGACAACT
Ib-8-4F: TGGGTTGCCTAGCTAAACTGAC138–16420.37521.9980.499
R: GCAAGAAAAGGTGCAAAGTCCT
Ib-8-5F: CCTCTCACCGGATCTAGTGGT196–24040.63232.4050.584
R: GACGTCACTGACTCAATCCTGA
Ib-9-1F: CCCGTCTAATGAATTTTGCTGCT197–21930.53131.9240.480
R: TTGTGACTGTGTTGCCAATGTC
Ib-9-2F: TCCATTTCTATGCACGCCTTTG113–15720.37321.9370.484
R: ATCTCGACATCTTCCCGACATC
Ib-10-2F: GGGAAACTGGATCGTGAAAACC154–19530.58621.0160.016
R: GAAACTTCGAACAATGCCCACA
Ib-10-4F: GCCACATGATTGTCATCAACCC174–21420.36221.7660.434
R: GATGGGTTTCTTTCACTGGTGC
Ib-11-1F: TGACTATGTTGACCTGACGTGG178–24040.54642.3180.569
R: AGAGTTTCACGCCTATACCGAT
Ib-11-5F: TTAACAGGACCAGAGGCAACAA172–20930.58932.2460.555
R: ACTGCTCCCCGAATGGTATTTT
Ib-12-2F: GCTTGTTATCGGGGTCCTTACT153–18020.14321.0970.089
R: ACCAATGATGCGCCAAAAGAAA
Ib-12-8F: GGCCATGGTTGCTAAAGTCTTG193–23930.45931.5470.354
R: ATGGATGCTCTGACTCGAGTTC
Ib-13-2F: TGTGTTCTTGTTCCTGGAGTGA190–22130.55631.6880.408
R: ATCTCTGCCGACTCCATTTCTG
Ib-13-13F: GGGTAATCTTTCCTTTTGTGGAGT144–15520.35821.7170.418
R: AGCTCTAAGCAGCAGAACCATT
Ib-14-2F: TAAGTAATCTGGGATTGCGCTG120–16030.41831.5470.354
R: CTCGTGCAAAGACCAACACATT
Ib-14-7F: GTCATTGGGGCCTGTTTTTCTC113–12020.33221.5080.337
R: TCAGCAGTTACCAAACCCATGA
Ib-15-1F: AGATCAGAGTCTCATGGGTTCA156–16320.14221.0960.088
R: TGCGTCACCATGCACTACTAAT
Ib-15-2F: TCAATTCAGGAATCCCTAGCGC195–25050.75742.1150.527
R: ATTTCGGGTGACAATCGATTGC
1 PIC, polymorphic information contents; Na, observed number of alleles per locus; Ne, effective number of alleles per locus; Ave. het, the mean heterozygosity per locus.
Table
Table 2. Analysis of molecular variance (AMOVA) of 240 sweetpotato accessions based on InDel data.
Table 2. Analysis of molecular variance (AMOVA) of 240 sweetpotato accessions based on InDel data.
Sourcedf 1SSMSEstimated VariationPercentage Variation%
Among groups5132.29326.4590.4223%
Within groups2342770.02411.83811.83897%
Total2392902.317 12.260100%
1 df = degrees of freedom; SS = sum of squares, MS = mean square.
Table
Table 3. Genetic distance (Nei&Li (Czekanowski(1913)) of 240 accessions within six groups based on InDel markers.
Table 3. Genetic distance (Nei&Li (Czekanowski(1913)) of 240 accessions within six groups based on InDel markers.
GroupsAverage 1CH-Lan SEAAMEEAAFRCH-Var
CH-Lan0.1870.000
SEA0.2500.2120.000
AME0.2160.2190.2320.000
EA0.1830.1950.2210.2140.000
AFR0.1690.1760.1970.2070.1850.000
CH-Var0.2060.2020.2250.2240.2020.1920.000
1 Average, the average genetic distance within each group. CH-Lan, landraces in China; CH-Var, improved varieties in China; AME, from International Potato Center, the USA, and Brazil; EA, from the Asian Vegetable Research and Development Centre, Japan, and Korea; AFR, from the International Institute of Tropical Agriculture and Morocco; SEA, from Indonesia, Philippines, Cambodia, and Vanuatu.
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MDPI and ACS Style

Zhao, L.; Qi, Z.; Xiao, S.; Tang, F.; Liu, Y.; Deng, Y.; Dai, X.; Zhou, Z.; Ou, W.; Cao, Q. Genetic Diversity Assessment of Sweetpotato Germplasm in China Using InDel Markers. Agronomy 2022, 12, 3074. https://doi.org/10.3390/agronomy12123074

AMA Style

Zhao L, Qi Z, Xiao S, Tang F, Liu Y, Deng Y, Dai X, Zhou Z, Ou W, Cao Q. Genetic Diversity Assessment of Sweetpotato Germplasm in China Using InDel Markers. Agronomy. 2022; 12(12):3074. https://doi.org/10.3390/agronomy12123074

Chicago/Turabian Style

Zhao, Lukuan, Zhanghua Qi, Shizhuo Xiao, Fen Tang, Yang Liu, Yitong Deng, Xibin Dai, Zhilin Zhou, Wenjun Ou, and Qinghe Cao. 2022. "Genetic Diversity Assessment of Sweetpotato Germplasm in China Using InDel Markers" Agronomy 12, no. 12: 3074. https://doi.org/10.3390/agronomy12123074

APA Style

Zhao, L., Qi, Z., Xiao, S., Tang, F., Liu, Y., Deng, Y., Dai, X., Zhou, Z., Ou, W., & Cao, Q. (2022). Genetic Diversity Assessment of Sweetpotato Germplasm in China Using InDel Markers. Agronomy, 12(12), 3074. https://doi.org/10.3390/agronomy12123074

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