Next Article in Journal
Changes in the Content of Some Groups of Phenolic Compounds and Biological Activity of Extracts of Various Parts of Heather (Calluna vulgaris (L.) Hull) at Different Growth Stages
Next Article in Special Issue
Macro- and Micronutrients from Traditional Food Plants Could Improve Nutrition and Reduce Non-Communicable Diseases of Islanders on Atolls in the South Pacific
Previous Article in Journal
Physiological Behavior of the Aquatic Plant Azolla sp. in Response to Organic and Inorganic Fertilizers
Previous Article in Special Issue
Evaluation of Wild Potato Germplasm for Tuber Starch Content and Nitrogen Utilization Efficiency
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Plants
Volume 9
Issue 8
10.3390/plants9080925
plants-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:
Article

SNP Markers and Evaluation of Duplicate Holdings of Brassica oleracea in Two European Genebanks

by
Anna E. Palmé
1,
Jenny Hagenblad
2,
Svein Øivind Solberg
3,*,
Karolina Aloisi
1 and
Anna Artemyeva
4
1
Nordic Genetic Resource Centre, Smedjevägen 3, SE-230 53 Alnarp, Sweden
2
Department of Physics, Chemistry and Biology, Linköping University, SE-581 83 Linköping, Sweden
3
Faculty of Applied Ecology, Agricultural Sciences and Biotechnology, Inland Norway University of Applied Sciences, NO-2418 Elverum, Norway
4
N. I. Vavilov Institute of Plant Genetic Resources (VIR), 42-44, B. Morskaya Street, 190000 St. Petersburg, Russia
*
Author to whom correspondence should be addressed.
Plants 2020, 9(8), 925; https://doi.org/10.3390/plants9080925
Submission received: 17 June 2020 / Revised: 19 July 2020 / Accepted: 20 July 2020 / Published: 22 July 2020
(This article belongs to the Special Issue Plant Biodiversity and Genetic Resources)
Browse Figures
Review Reports Versions Notes

Abstract

:
Around the world, there are more than 1500 genebanks storing plant genetic resources to be used in breeding and research. Such resources are essential for future food security, but many genebanks experience backlogs in their conservation work, often combined with limited budgets. Therefore, avoiding duplicate holdings is on the agenda. A process of coordination has started, aiming at sharing the responsibility of maintaining the unique accessions while allowing access according to the international treaty for plant genetic resources. Identifying duplicate holdings based on passport data has been one component of this. In the past, and especially in vegetables, different selections within the same varieties were common and the naming practices of cultivars/selections were flexible. Here, we examined 10 accession pairs/groups of cabbage (Brassica oleracea var. capitata) with similar names maintained in the Russian and Nordic genebanks. The accessions were analyzed for 11 morphological traits and with a SNP (Single Nucleotide Polymorphism) array developed for B. napus. Both proved to be useful tools for understanding the genetic structure among the accessions and for identifying duplicates, and a subset of 500 SNP markers are suggested for future Brassica oleracea genetic characterization. Within five out of 10 pairs/groups, we detected clear genetic differences among the accessions, and three of these were confirmed by significant differences in one or several morphological traits. In one case, a white cabbage and a red cabbage had similar accession names. The study highlights the necessity to be careful when identifying duplicate accessions based solely on the name, especially in older cross-pollinated species such as cabbage.

1. Introduction

A report from the Food and Agriculture Organization of the United Nations (FAO) indicates that up to 70% of the 7.4 million accessions around the world might be duplicate holdings [1]. At the same time, genebanks are struggling with inadequate resources and backlogs in regeneration, characterization, and documentation [2]. Taking a bird′s-eye view, duplication is not an efficient conservation approach. At a local level, each collection holder aims to have a large and influential collection. Requesting and maintaining accessions from other genebanks (duplication) has been one way to do this. The European Genebank Integrated System (AEGIS) has managed to involve institutions in more than 30 countries in an action for coordination and collaboration on plant genetic resource conservation [3]. The main idea is to share responsibility by establishing and operating a European Collection of unique and important germplasm and to increase the conservation efficiency and quality while facilitating the use of the genetic resources [4].
A critical step in this initiative has been the selection of accessions (generally seeds, conserved in genebanks). There are several challenges in such selections, but one is how to handle accessions with the same or similar names [5]. Same or similar names could be due to the duplication of accessions among genebanks, but can also indicate different selections (enterprises’ selections) and/or a flexible naming practice of seed material in the past [6,7]. Official variety lists (cultivar lists) and control came in the mid-twentieth century [8,9], and plant breeders’ rights came after the ratification of the International Union for the Protection of New Varieties of Plants, which was launched in 1969 [10]. In 1969, local companies’ selections were still listed under similar names in Scandinavia, but almost all of them were removed from the national variety lists between 1970 and 1980 [11]. All this has resulted in a large number of older varieties with the same or similar names. An important question is whether accessions of such varieties should be regarded as duplicates when efforts are made to increase the efficiency in genebank conservation.
Recent developments such as using passport data with digital object identifiers (DOI) on accessions and transactions [12], large-scale morphological characterization and phenotyping [13], and molecular studies with next-generation sequencing platforms [14,15,16,17,18] have all improved the possibilities to identify duplicates. Regardless of the approach, proper data and transparent genebank information systems are needed to facilitate duplication assessments [19,20]. Brassica oleracea L. (2n = 18) comprises many important crops, including cauliflower, broccoli, and cabbages as well as wild species and subspecies which are cross-compatible with the cultivars [21,22]. The issue of duplicate genebank holdings of B. oleracea has been raised [23], and genetic diversity has been investigated using, for example, AFLP (Amplified Fragment Length Polymorphism) markers [24,25,26] and microsatellites [27]. In these studies, substantial diversity within accessions was observed, but so was a clear differentiation among accessions. A high-density SNP genotyping array for Brassica napus and its ancestral diploid species has been developed [28], and in this study we test it on B. oleracea.
The main objectives of this study were: (1) to examine the suitability of this Illumina Infinium SNP array to study the genetic diversity and structure in cabbage; (2) to evaluate potential duplicates among genebank accessions with similar names by using morphological traits and the mentioned genetic markers. If the SNP array could be used successfully, we wanted to identify a sub-set of SNP markers to be used for the future screenings of a larger number of accessions in a process of identifying duplicates and incorrectly labelled accessions at a European or global scale.

2. Results

The Brassica Working Group of the European Cooperative Programme for Plant Genetic Resources (ECPGR) has prioritized AEGIS, which means there is an ongoing process of searching for potential duplicates. Based on the cabbage passport data from the N. I. Vavilov Institute of Plant Genetic Resources in St. Petersburg (VIR) and the Nordic Genetic Resource Centre (NGB), we were able to identify 40 pairs, triplets, and groups (hereafter termed groups) based on the “accession name” or “donor name”. Here, we present the results for 10 such groups, with a total of 27 accessions (Table 1).

2.1. Morphological Diversity

Two types of morphological descriptors were analyzed: continuous descriptors, such as plant height and leaf length, and categorical descriptors, such as leaf color and head shape. Many of the continuous, numeric descriptors were positively correlated (arrows pointing to the right in Figure 1). The two first principal components represented 42% and 23% of the total variation. Accessions with the same or similar names grouped together, but only to a certain extent.
Within the Amager Tall group, A1 (Amager Hög) and A4 (Grami) did not cluster with A3 (Amager Høj Grøn Grami), A6 (Amager Høj, Grøn, Toftø 67), and A7 (Amager Tall Resistent). The biplot furthermore indicated that the two Amager Winter accessions (A11 and A12), the two Stavanger Torg accessions (ST1 and ST2), and the two Kissendrup accessions (K1 and K2) did not cluster within their respective group.
The two Stavanger Torv accessions were early maturing and needed around 120 days to maturation, while most of the other accessions needed 140 days or more (Figure 2). There was also a large variation in the time to maturity and other traits within many of the accessions. The results from the Tukey multiple comparisons of means (Table S1) showed that A1 differed significantly from A4, A6, and A7 in the time to maturity (all p < 0.05). Furthermore, A4 differed from A1 and A6 in the leaf lamina length and plant height, and A4 from A1 in head weight (all p < 0.05). A11 and A12 differed in plant height and, notably, also in leaf lamina color, where A11 plants were purple while A12 plants were green. Significant differences were also detected among the Langendijker Summer accessions. The L2 and L3 plants were purple, while L1 had a mixture of purple and green leaves. In addition, L1 differed from L2 in core length (p < 0.05). The Stavanger Torg accessions ST1 and ST2 differed in head height and head density, while the Kissendrup accessions differed in the time to maturity (all p < 0.05). No clear differentiation was detected among the accessions within the Blåtopp, Ruhm von Enkhizen, Loke, or Jåtunsalgets Vinterkål groups (Table S1).

2.2. Marker Efficiency and Accession Diversity

Among the 5965 markers, 3969 failed to amplify in all the genotyped cabbage plants. Of these, mapping data was available for 3750 markers, 99% of which were located on the A genome in B. napus, and therefore were not expected to be found in B. olereacea. The largest proportion of failed markers was found on the B. napus chromosome A04 (0.540), with the lowest on chromosome C06 (0.002). Duplicate samples showed a high consistency across runs. Each re-genotyped individual differed in only two markers. Individual 135 differed in Bn-A01-p16331424 and Bn-scaff_15712_6-p1025930, and individual 136 in Bn-scaff_15877_1-p926737 and Bn-scaff_16553_1-p6743.
The highest average number of alleles across all the loci was found in accession A4 (1.8) and the lowest in accession K1 (1.2) (Table 2). The same accessions had the highest and the lowest observed heterozygosity and the lowest genetic diversity, calculated as Nei’s h (expected heterozygosity under Hardy–Weinberg Equilibrium, HWE) (Table 2). In some cases, accessions with similar names had similar levels of within-accession diversity—for example, ST1 and ST2—but in many cases, different levels of diversity were observed among the accessions within a group (Table 2). The genetic diversity of the accessions from NGB did not differ significantly from those from VIR (t-test, p = 0.734). Genetic diversity was not significantly correlated with acquisition year for the full data (p = 0.177) nor for accessions from NGB (p = 0.687), but was positively correlated with the acquisition year for VIR accessions (c = 0.553, p < 0.05).

2.3. Accession Comparisons

All the pairwise FST values were significantly different from 0 (p < 0.001 for all comparisons), indicating genetic differentiation among all accessions. In general, the average pairwise FST values were lower between pairs of accessions belonging to the same group (average 0.193) than between pairs of accessions belonging to different groups (average 0.270) (t-test, p < 0.001). The lowest FST value (0.087) was found between accessions B6 and B3 (Table 3), both from the Blåtopp group and with a similar morphology (Figure 1, Table S1). The same was true for the Loke pair (LO1 and LO2), with a very low FST between the accessions (Table 3) and no significant morphological differences (Table S1). The highest FST values within groups were found between the pairs A8 and A9 (0.326) and B2 and B3 (0.321, Table 3), accessions showing little morphological differentiation (Figure 1, Table S1). The highest FST value overall was found between the accessions A1 and K1 (0.564, Table S2), the two accessions with the lowest level of genetic diversity. The pattern was reflected when looking at the average number of pairwise shared alleles, where accessions A1 and K1 shared few alleles with many of the other accessions. The lowest number of shared alleles was found between the accessions K1 and A11, and the highest between A1 and A4 (Table S3).
A STRUCTURE analysis showed equally high support for two and three clusters (Figure S1). At K = 2, the first cluster contained A1, A3, A6, A7, A12, B1, B3, B6, J1, and J2 and the second contained A8, K1, K2, L1, L2, and L3. The remaining accessions showed some level mixed clustering, with similar degrees of mixture for R1, R2, R3 and ST1 and ST2 (Figure 3). At K = 3, the cluster consisting of accessions A8, K1, K2, L1, L2, and L3 remained intact. A second cluster consisted of some of the accessions showing mixed clustering at K = 2: R1, R2, R3, and ST1. The third cluster contained accessions A1, A3, A6, A7, A12, B1, B3, B6, J1, and J2, with the remaining accessions showing various degrees of mixed clustering (Figure 3b). In general, accessions within the same group tended to belong to the same clusters (Figure 3). For example, all the Kissendrup, Langendijk, Ruhm von Enkhuizen, Jatunsalgets, and Loke accessions clustered within the same group. The most notable exception was accession A8, Amager Kurzstrunkiger Original. Based on the accession name, A8 was expected to cluster with the other Amager accessions, but instead the accession clearly clustered with the Kissendrup and Langendijker accessions (Figure 3).
The PCA based on the allele frequencies in the different accessions (Figure 4) supported the clustering found with STRUCTURE (Figure 3) to a large degree and the morphology-based clustering to a lesser degree (Figure 1). One cluster contained A8, K1, K2, L1, L2, and L3, (upper right); one cluster contained accessions R1, R2, R3, and ST1 (lower center); and one cluster contained accessions A1, A3, A6, A7, A9, A12, B1, B3, B6, J1, LO1, J2, and LO2 (upper left). Accessions A4, B2, A11, and ST2 were located between the latter two clusters, substantiating the structure analysis at K = 3. There was no evidence of clustering according to the genebank origin. The VIR accessions acquired at an earlier date tended to be located less centrally in the PCA (c = −0.540, p = 0.056) than the NGB accessions. An individual-based PCA showed a good agreement with the accession average-based PCA. Most individuals of each accession clustered together, but two exceptions were found in accession L3 and accession A4 (Figure S2).

2.4. Genotypic and Morphological Comparisons

In the accession-level PCA based on the SNP data, the first principal component (PC1) separated most of the purple-leafed accessions from most of the green-leafed accessions. The green-leafed accession A8, however, clustered among the purple-leafed accessions, while the purple-leafed accession A11 clustered among the green-leafed (Figure 4). The accession L1, with purple/green leaves, clustered among the purple accessions. Neither head density nor head shape showed any clustering in the accession-level PCA (data not shown).

2.5. Limiting the Number of SNP Markers

Subsets of the data were analyzed to determine whether a more limited number of markers could be used to capture a similar amount of information as the full dataset. Already, with as few as 10 randomly chosen markers, the FST values obtained were in most cases similar to those calculated from the full dataset (Table 3). Increasing the number of markers to 50, 100, and 500 markers, respectively, reduced the variance of the FST estimates (Figure S3) from an average of 0.088 for 10 markers to 0.011 for 500 markers. The mean FST values did, however, not change consistently in any given direction, and could either increase or decrease with an increasing number of markers. The mean FST, however, always changed less than 0.01.
Individual-level PCA clustering according to accession could be obtained with a limited number of markers. Already, when subsampling the 20 markers with the alleles providing the largest segregation along PC1 and PC2, a reasonable clustering according to accession could be obtained (Figure S4a). The same number of markers used on the accession-level PCA could only separate accessions along PC1 (Figure S4b).
A single subsample for each 10, 50, 100, and 500 markers was used to investigate whether the same clustering could be obtained in a STRUCTURE analysis as with the full dataset. All subsets indicated K = 2 to be the level of structuring best explaining the data. With only 10 markers, very limited power was obtained to identify structuring, although the clustering of the Jatunsalgets Vinterkål group (J1 and J2) and the Ruhm group (R1, R2, and R3) could be discerned. Surprisingly, as few as 50 markers yielded a structuring similar to that obtained for the full dataset, as well as from 500 markers (Figure S5). A STRUCTURE analysis of 100 randomly chosen markers, however, showed that a lower number of markers was not sufficient to reliably replicate the results of the full dataset.
To explore the efficiency of 50 markers to capture the same structure at K = 2 as the full dataset, an additional 9 datasets of 50 randomly chosen markers were generated. Comparisons of the STRUCTURE analysis of the 10 sets of 50 markers showed that some but not all clusters were reliably identified in each subset (Figure S6). In particular, the clustering of the Jatunsalgets Vinterkål (J1 and J2) and the Loke (LO1 and LO2) groups varied. An analysis with the software CLUMPP showed that the clustering among the 10 sets was not very consistent (H = 0.752).
A subset of markers was chosen with the aim to provide a good resolution for discriminating between accessions. The markers were chosen to provide as high a discriminatory power as possible along the first two principal components in the accession-level PCA (Figure 4). The markers were further evaluated to show a high level of genetic diversity (h > 0.3) and not found to be in high linkage disequilibrium (D′ < 0.25) with each other. In total, 500 markers were chosen (Table S4).

3. Discussion

3.1. Discrimination Power and the Number of Markers

The array used in this study for genotyping B. oleracea was originally developed for B. napus [28]. Not surprisingly, the array showed the greatest efficiency for markers located on the “C” genome in B. napus, with less than 1% of the markers on most chromosomes failing to amplify. Nevertheless, as many as 50% of the markers mapping to the “A” genome in B. napus were able to successfully amplify in our B. oleracea. Further studies are needed to discern the mapping location of these markers in B. oleracea and to evaluate the amount of cross-amplification between the “A” and the “C” genome.
Although the cost of genotyping and sequencing is becoming ever lower, using a limited number of markers while retaining sufficient discriminatory power is still of interest. We found that a random sample of only a few percent of the amplifying markers would have provided us with an overall picture of reasonable similarity to the one obtained with the full dataset. In most cases, the FST value between two accessions in the same group could be estimated with reasonable accuracy with as few as 50 or 100 markers, similar to what was reported by Willing et al. [29].
A STRUCTURE analysis further suggested that as few as 50 randomly chosen markers could often capture a large part of the genetic structuring in the data, although the clustering of some accessions was inconsistent and the single analysis of 100 random markers failed to replicate the clustering obtained with the full dataset. In addition, parameters such as the amount of gene flow and the evenness of sampling have been shown to influence the number of markers needed to discriminate between groups of differently related individuals [30]. The minimum number of markers needed will vary from organism to organism and from case to case, but may be surprisingly low. Choosing the 20 most discriminatory markers for individual-level PCA resulted in an outcome with a high similarity to that of the full dataset. However, these 20 markers might not be the most discriminatory in another sample of cabbage accessions. For this reason, we recommend a subset of at least 500 SNP markers for duplication assessments in cabbage in order to get a robust, detailed result. A list of 500 SNP markers with a high discriminating power, high level of genetic diversity, and low linkage disequilibrium with each other can be found in Table S4).

3.2. Duplication Assessment and Genetic Similarity

This study clearly demonstrates that the same or similar names do not necessarily mean a duplicate holding. Both the STRUCTURE and PCA analyses detected genetic differences among accessions grouped by accession name. In the STRUCTURE analysis, five out of the 10 groups showed genetic differences within the group: A4 vs. A1, A3, A6, and A7 (Amager Tall group); A8 vs. A9 (Amager Short group); A11 vs. A12 (Amager Winter group); B2 vs. B1, B3, and B6 (Blåtopp group); and ST1 vs. ST2 (Stavanger Torv group, Figure 3). A similar pattern could be seen in the PCA based on SNP markers (Figure 4). Three of the five cases were supported by significant differences in one or several morphological traits (Table S1). The difference between the two Amager Winter accessions was obvious from the morphology, where A11 (Amager Winter) was identified as a red cabbage type, while A12 (Amager Vinter Gefion) was a white cabbage. The differences between the two Stavanger Torv accessions (ST1 and ST2) were less obvious, both being early maturing white cabbage types but with significant differences in two head characters. Within the Amager Tall group, morphological data also confirmed that accessions were different (Table S1), while no clear differences in morphology were observed in the Amager Short group or the Blåtopp group.
Genetic similarity can be used as a criterion to identify and handle duplicate holdings in genebanks [23,24]. In clonal and highly inbred material, it can be a relatively easy task to determine whether accessions are duplicates, but in open pollinated crops such as cabbage, the genetic structure is more complex. Even accessions that have the same origin—for example, accessions donated from the same seed lot to different genebanks—are expected to diverge over time [27]. In these species, the overall pattern of genetic diversity needs to be taken into consideration.
In instances where several lines of evidence (e.g., low FST values, a large proportion of shared alleles, morphologic similarity, and common clustering in the STRUCTURE analysis and PCA) suggest a high similarity compared to the average, accessions can be considered duplicates and one of the accessions can be removed from the genebank holdings with a minimum loss of genetic diversity (or bulked) [23]. Examples from our dataset that could be bulked, removed, or given lower priority in the conservation could be B3 and B6 and one of the LO accessions. Our study has shown that using accession names alone is not a good strategy to reduce duplicate holdings, as the same or similar names does not mean identical genetic composition. A combined method using both accession names and other passport data as a first step and then marker evaluations as a second step would be a better approach. Alternatively, morphological evaluations could be used or a more extensive passport data evaluation trying to trace the transactions of accessions between genebanks, for example, by using donor accession numbers or other relevant information. The ECPGR Brassica group has established an online tool for identifying duplicate holdings based on accession names and other passport data. This is a useful first step that could be taken into a next step with an extensive evaluation of the potential duplicates with the developed marker set.

3.3. Cultivation History and Naming Practices

There can be several explanations as to why accessions with the same or similar names are genetically and/or morphologically different. Minor differences could be explained by breeding history and naming practices. As mentioned, selections within a cultivar were common in the 19th and first part of the 20th century, and a cultivar could have many and complicated names [7]. One example is “Jatunsalgets Vinterkål Berbes St. Orginal” (J1) or “Jåtunsalgets Vinterkål” (J2). These two accessions are morphologically close and cluster together both in the STRUCTURE analysis and the SNP-based PCA, but the accessions are not identical, either morphologically or genetically. Jåtunsalget was a small Norwegian seed enterprise with only these two accessions recorded in the ECPGR Brassica database [31]. “Jåtunsalgets Vinterkål” was listed on the Norwegian variety list in 1979 [32], however the pedigree was “Jåtun Amager x en Hollansk sort i 1929”, which means “a selection of Amager crossed with a Dutch cultivar in the year 1929”. Most likely, “Jåtun Vinterkål” was marketed already in the 1930s but was listed much later. The oldest (and most original) accession is J1, acquired by VIR in 1953, while J2 (from NGB) entered the Nordic Genebank more than 20 years later from unknown sources [32].
A more complicated example is the Amager varieties. The ECPGR Brassica database [31] shows 102 records with “Amager” in their accession name. Amager is a geographical area and a village just outside Copenhagen that hosted both seed enterprises and an extensive vegetable production. We divided the Amager accessions in our study into three sub-groups based on naming; one of them was the Amager Tall sub-group, where the Danish word “Høj” or the Swedish “Hög” both mean “Tall” (with 21 accessions in the ECPGR database). Our study included five such accessions where A4 (accession name “Grami”) was genetically clearly different from the other four. In most selections, there is a second name—e.g., “Grön Grami”, “Grön Toftø”, or “Resistent”—describing further selection properties or enterprises’ names. We hypothesized that A4 (“Grami” from VIR) would be similar to A3 (“Amager Høj Grøn Grami” from NGB), but this was not the case. In retrospect, we should maybe not have grouped A4 with the Amager Tall accessions, as the only link to Amager was through the name “Grami”, which was used also in the name of A3.
Within the remaining two Amager sub-groups, genetic differences were also detected. Amager Winter is commonly known to be a white cabbage (green leaf laminas), marketed, amongst others, by A. Hansen Amagerfrø in Denmark in the mid-20th century [7]. In the ECPGR Brassica database [31], there are six accessions fitting this name, and we included two of these. Test cultivations showed that A11 (Amager Winter, K192) was a red cabbage (purple leaf laminas). A11 is from an unknown source in Denmark, acquired by VIR in 1969, and the accession has so far been through at least six regeneration cycles at the VIR experimental field. The accession was listed as purple at the time of entry (as accession number K192 in the VIR catalogue). Certainly, there have been red Amager cabbages traded. According to the Nordic Genetic Resource Center cultivar database [32], a red cabbage cultivar/selection named “Amager” was released in 1959 and was bred by Østergård frøavl (Denmark, breeders name Stenballe P 59 68). Other red Amager cabbage cultivars/selections were “Amager 304”, bred by A. Hansen Amagerfrø (breeders name Tagenhus P 59 69, released in 1959); “Holdbar Amager”, bred by L. Dæhnfeldt (breeders name Toftø S 1960, released in 1960); and “Amager Caro” and “Amager Rega”, both released in 1974 by Ohsens Enke (Denmark). A11 could not be one of the latter, as it was acquired by VIR already in 1969, but it could be one of the earlier developed red “Amager” cultivars. What is certain is that A11 is not a duplicate of A12, which has a similar name (Amager Vinter Gefion, NGB1879) but is a genetically different white cabbage. Although A11 is different from the remaining accessions in the “Amager” group in the PCA, it does show a higher similarity to the “Amager” group than to other red cabbages in the PCA (Figure 4). This, together with the results of the STRUCTURE analysis (Figure 3), tentatively suggests that the accession is the result of breeding the purple color into an Amager background. It is hard to know if the polymorphism observed in accession L1 (compared to L2 and L3; Figure 3) is due to gene flow from accessions with a different leaf color, as there are both green and purple plants in this accession, or if there are other explanations.
The genetic data showed clear differences between the Amager Shor pair (Amager Kurzstunkiger Orginal, A8) and (Amager L NF Orginal, A9). Based on passport data, we know they are from different seed companies (one in Denmark and one in Norway) and were included in the collection at VIR in different years (1935 and 1967, respectively). Accessions with the name “Amager Kurztunkig” are found in in Germany, Poland, the Check Republic, and Belarus [31], most likely duplicated with the original accession from VIR included in this study. The prefix “Kurz” is a German word and means “Lav” (in Scandinavian) or short in English. Our reason for pairing A8 and A9 was the prefix “Kurz” in A8 and the abbreviation L (“Lav”?) in A9. Regarding plant height, they were both short, and they had quite similar morphological characteristics, but were not very close in the morphological PCA (Figure 1). Most likely, A9 is an Amager selection from Norsk Frø (NF). From Norway, a cultivar with a similar name (Amager L1 Orginal, not included in this study) is known, with the pedigree “Jåtun Amager x Jåtunsalgets vinterkål 1932” [32]. The cultivar was marketed from 1933 onward, but it was approved as late as in 1961. Amager Kurzstunkiger came to VIR in 1935 from a Danish enterprise but with a German accession name. Certainly, A8 and A9 have a different history and, as demonstrated, they are genetically different.

3.4. The Effect of Genebank Conservation

Changes in genetic composition, major or minor, may take place during field regeneration [33,34,35,36,37]. Our study was not designed to track changes from generation to generation, but some of the differences we observe are probably the result of this process. Minor changes are expected during regeneration in genebanks, especially if a low number of plants are used [38] or if insufficient isolation is used during flowering.
Genebanks use a standard number of plants, usually 20 to 50 individuals, and net cage isolation with pollinators to reduce the risks of genetic changes during regeneration. The FAO genebank standards do not specify the number of plants [39]. At VIR, regeneration takes place typically every 5–7 years, meaning that material acquired in the 1930s has been through at least 10 regeneration cycles. This is expected to result in an increase in differentiation among accessions and a loss of genetic diversity within accessions. The heterozygosity is predicted to decrease each generation in proportion to the population size [40]. For example, with 10 regeneration cycles and a population size of 20, a 22% decrease in heterozygosity is expected on average (Ht = (1−1/2N) Ht−1; N=diploid population size). We found that accessions acquired a long time ago tended to have lower genetic diversity than more recent additions, a pattern that is in agreement with the loss of genetic diversity from genetic drift. Additionally, more recently acquired accessions tended to cluster more centrally in the PCA plots, which could also be the result of genetic drift acting to differentiate older accessions.
Other factors such as selection and gene flow could also affect genebank accessions. Some selection from local conditions—both those connected to the environment at the regeneration site, such as climate and soil conditions, and those linked to cultivation practices, such as harvesting time and methods—is expected. In addition, if accessions are not completely isolated, gene flow will occur from other cultivated genebank accessions of the same species, from cultivated fields in the area, and from weeds. Bees and flies are the main pollinators of cabbages [41], and to avoid unwanted gene flow through cross-accession pollination, isolation is crucial. Contrary to genetic drift, external gene flow is expected to increase diversity within the accession and decrease divergence among accessions cultivated at the same time.
Van Hintum et al. [24] demonstrated that the genetic changes caused by regeneration within an accession were of similar magnitude to differences among genebank accessions of cabbages with the same or similar names. Therefore, they questioned the rationale behind conserving a large number of accessions with the same or similar names. Our study supports the occurrence of genetic change during regeneration and similarity among some accessions with similar names. At the same time, however, we have shown that similar names do not always imply the same genetic material.

3.5. Implications for Genebank Conservation

All genebanks have limited budgets and need to adapt their operations to their economic frame. One way to adapt is to remove or pool/bulk duplicates and thus make funds available for the high-quality conservation of the remaining unique accessions. This approach has been used in many species, including Brassica crops [23,42].
AEGIS has suggested a roadmap for how to handle duplicate holdings at the European level, identifying the most appropriate accessions based on passport data [4]. However, the decision to remove duplicates is the responsibility of individual genebanks. Our study clearly shows that relying exclusively on accession name when identifying duplicates can be risky, especially with old cross-pollinating cultivars with complex breeding history and naming practices. We find that in five out of the 10 groups, accessions with same or similar names have clear genetic differences. In most cases, such differences were corroborated by significant differences in one or several morphological traits.
Additional passport data such as accession numbers, donor institute, donor accession number, etc. can help pinpoint the origin of the accession and the time of split from other accessions. If such data is available, the chance of correctly identifying duplicates based solely on documentation increases. For recently acquired material, for example—modern cultivars—this can be a safe approach. However, for older cultivars and landraces this is often difficult. Documentation is often missing and, as discussed above, accessions can have diverged substantially from a common origin via complex breeding histories and regeneration in genebanks.
Using detailed morphological characterization has been suggested [43], as has combined morphological and molecular characterization, and molecular characterization alone [18,44]. Our current findings lend support to the need for characterization before deciding to remove or bulk accessions. However, a major challenge is the costs of such characterizations. Most genebanks are underfunded and have backlogs in regeneration and viability monitoring. For tracking future duplications, the introduction of DOI on the accession level [12] would make transactions between genebanks easier, but it cannot capture what is already duplicated. International collaboration and genotyping using the next-generation sequencing could be a cost-effective way forward [14,15,16,17,18]. Here, proper information on the accession level could go hand-in-hand with the facilitation of the use of the germplasm as genetic information is catalogued, linked to the accessions, and made available for the users.

4. Materials and Methods

4.1. Plant Material, Cultivation, and Morphological Characterization

In Europe, there are 35 Brassica collections, located in 24 countries and with more than 11,000 B. oleracea accessions [45]. In total, 980 cabbage accessions are maintained at VIR and 189 at NGB. An initial study [46] characterized six groups for morphological traits, and some differences within the groups could be detected. In this study, another 10 groups (Table 1) were examined to see if there were differences among accessions within a group. For each group 10 plants per accession were planted. These plants were randomized, and each of the 10 plants was characterized. The study consisted of 10 such randomized pair/triplet characterizations. The planting distance was 50 cm between the plants. The work was done at Alnarp, Sweden (55°’ N, 13°’ E); the soil was loamy clay and the fertilization was 100 kg ha-1 PROMAGNA 11-5-18™ (Yara, Norway) at planting and 30 kg ha-1 YaraMila 22-0-12™ (Yara, Norway) one month after planting. Plants were irrigated and biological control and fungicides were applied. Plants were evaluated just before harvesting. SI units were used for plant leaf, head, and core size parameters and UPOV (International Union for the Protection of New Varieties of Plants) [47] descriptors for leaf color, head shape, and head density. Details are provided in Table S1. Principal Component Analysis (PCA) bi-plots were used for an overview of the data and characters. An ANOVA was performed for each numeric character and included data from all the individuals in that group. If the ANOVA indicated significant differences among accessions, a Tukey multiple comparison of means [48] was used to identify accessions that differed from each other. χ2 statistics were used for categorical characters.

4.2. DNA Extraction

As far as we know, the selected accessions have not previously been included in any molecular studies. DNA extraction was conducted on the same 10 plants per accession that were morphologically characterized. Leaf samples (2 cm2) were collected from the plants cultivated in the field, placed in 2 ml Eppendorf tubes, immediately frozen in liquid nitrogen, and subsequently freeze-dried overnight in a LyoLab 3000 (Heto Lab Equipment). The freeze-dried material was powdered in a mixer mill (Merck Retsch MM 300) using steel beads; hereafter, 600 μl of CTAB buffer was added to each powdered sample (0.1 M Tris; pH 8.0, 0.01 M EDTA, 0.7 M NaCl, 1% CTAB, and 1% β-mercaptoethanol) according to Doyle and Doyle [49]. The samples were incubated in a thermomixer (Eppendorf) at 600 rpm and 60 °C for 60 min. DNA was extracted by adding one volume of chloroform/isoamylalcohol (24:1), then they were mixed and centrifuged for 20 min at 13,200 rpm. The supernatants were transferred to new tubes and 5 μl of RNAse (1 mg/ml) was added and incubated in the thermomixer (600 rpm, 37 °C for 30 min). Cold isopropanol (0.8 V) was added, followed by mixing and centrifugation (10 min at 13,200 rpm). The DNA pellet was cleaned in 500 μl of wash-buffer (76% ethanol, 0.2 M sodium acetate) for 20 min at room temperature with subsequent centrifugation for 5 min in 13,200 rpm, followed by 500 μl of rinse-buffer (76% ethanol, 0.01 M ammonium acetate) and mixing and centrifugation for 5 min at 13,200 rpm. The samples were left to dry in room temperature for 1 h and were then re-suspended in 50 μl of ddH2O. The DNA concentration and ratio (at 260 nm and 280 nm) were determined using an Eppendorf BioSpectrometer. The DNA concentrations of the samples were adjusted for further analysis.

4.3. SNP Analysis and Statistics

Array genotyping was performed by TraitGenetics GmbH (Gatersleben, Germany) and with a 15 K Illumina Infinium array that contains a subset of markers from the Brassica 60K array [28]. The array had previously been tested for B. oleracea and had a total of 13,714 SNPs. An initial run was carried out with 92 individuals, and a second with 178 individuals. Ten individuals were analyzed from each accession in order to capture the within-accession variation. Cabbage accessions are expected to harbour substantial within-accession variation, and therefore more individuals per accession are needed. By analyzing 10 individuals per accession, we gained an adequate picture of the within-accession variation and at the same time we were able to include many accessions in the study. The first run with 92 individuals was a test run, and since that was successful the second run with 178 individuals was performed in the same way to increase the number of individuals analyzed. After the merging of the two runs, failed and invariant markers were removed, as were markers failing in more than 50% of the individuals. The remaining 5965 markers were used for further analysis. Of these, 68% (4090 markers) had less than 10% missing data. After the removal of the above markers, individuals with more than 40% failed markers were removed (2 individuals + 2 controls). Of the remaining individuals, all had less than 10% missing data.
Two individuals of the accession K2248 were genotyped in both runs. The second genotyping of both individuals had a lower success rate and was removed from the downstream analysis. In total, 266 individuals were kept for further analyses and were analyzed with 5965 markers.
Deviations from the HWE (Hardy-Weinberg Equilibrium) were tested using a χ2 test with and without Bonferroni correction. All the accessions had less than 10% of the markers deviating from the HWE before the Bonferroni correction. No marker deviated significantly from the HWE in any accession after the Bonferroni correction, and hence no marker was removed for this reason.
Wright’s FST [50] and Nei’s h were estimated according to Nei [51] using purpose-written perl scripts. For the FST values, significance was determined by permutation tests (1000 permutations). Subsets of the markers were analyzed to evaluate if the FST values between pairs of accessions from the same group could have been estimated with similar accuracy with a more limited number of markers. Subsets of 500, 100, 50, and 10 markers were randomly drawn from the dataset and used for calculating the FST values. This was repeated 1000 times for each number of markers, and the average FST values and standard error for the 1000 replicates were calculated.
PCA of the genetic data was carried out using R v 3.2.4 [52] and the prcomp command. For an accession-level PCA, the allele frequencies for each allele at each locus were treated as independent variables, while in the individual-level PCA the number of copies of each allele at each locus was used.
The software STRUCTURE (v 2.3.4) [53,54] was used to explore the data for genetic structuring. The software was run with a burn-in length of 20,000 iterations, followed by 50,000 iterations for estimating the parameters, with non-amplifying markers treated as missing data. Each analysis using the admixture model was repeated 10 times for each number of clusters (K = 1 to 10), until the likelihood values for the runs no longer improved. The number of clusters observed in the dataset was evaluated by calculating the ΔK according to Evanno et al. [55]. CLUMPP v 1.1.1 [56] was used to compare the results of individual runs and to calculate the similarity coefficients, H, and the average matrix of ancestry. In CLUMPP, the Full- Search, Greedy, and LargeKGreedy algorithms were used for comparing runs with K < 4, 4 ≤ K ≤ 6, and K > 6, respectively. The graphical presentation of the results was obtained using DISTRUCT v 1.1 [57]. STRUCTURE was also used to analyze the subsets of randomly chosen markers (10, 50, 100, and 500 markers, respectively), and the repeatability of the STRUCTURE analysis of 50 randomly chosen markers was analyzed using CLUMPP.

5. Conclusions

Our study is a contribution to AEGIS and the work to avoid unwanted duplication holdings. We tested the SNP markers developed for B. napus and found that many of these genetic markers (nearly 6000) were suitable for an analysis of the genetic structure and duplicate identification of B. oleracea. Of these, a subset of 500 markers are recommended for a future large-scale analysis of B. oleracea var. capitata. Both the genetic SNP data and the morphological data demonstrate the complex relationships among old cabbage cultivars and show that similar accession names do not necessarily mean that accessions are genetically or morphologically similar. This emphasizes that in the case of old cultivars of cross-pollinating species such as cabbage, extra care should be taken when identifying duplicates.

Supplementary Materials

The following are available online at https://www.mdpi.com/2223-7747/9/8/925/s1: Figure S1: Data for determining optimal number of clusters (K) in the STRUCTURE analysis of the full SNP dataset. Figure S2: Results of the individual level principal component analysis (PCA) based on SNP markers. Figure S3: FST values between different accessions based on 1000 subsamples of each 10, 50, 100 and 500 SNP markers respectively. Figure S4: Results from PCA when subsampling 20 SNP markers: A) individual level analysis and B) accession-level analysis. Figure S5: Subsampling of SNP markers for STUCTURE analysis assuming two genetic clusters (K=2). Figure S6: Comparison of STRUCTURE analyses of 10 subsets of 50 randomly chosen SNP markers. Table S1: Morphological descriptors with means and standard deviations. Table S2: Pairwise FST values among all investigated accessions. Table S3: Average number of pairwise shared SNP alleles among all investigated accessions. Table S4: Marker subset suggested for the identification of duplicate accessions (Excel file).

Author Contributions

Conceptualization, A.A. and S.Ø.S.; methodology, J.H., K.A., and A.E.P.; formal analysis, K.A., J.H., and A.E.P.; investigation, A.E.P.; writing—original draft preparation, A.P., J.H., and S.Ø.S.; writing—review and editing, all; visualization, J.H., S.Ø.S., and A.E.P.; project administration, S.Ø.S. and A.A.; funding acquisition, S.Ø.S. and A.A. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the Nordic countries (Norway, Sweden, Denmark, Finland, and Iceland) through the Nordic Counsel of Ministers.

Acknowledgments

We like to thank Flemming Yndgaard and Jerker Niss at Nordic Genetic Resource Center for assistance in the visualization of morphological data and for field cultivation, respectively.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analysis, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

References

  1. FAO. The Second Report on the State of the World’s Plant Genetic Resources for Food and Agriculture; Food and Agriculture Organization of the United Nations: Rome, Italy, 2010. [Google Scholar]
  2. Fu, Y.B. The Vulnerability of Plant Genetic Resources Conserved Ex Situ. Crop Sci. 2017, 57, 2314–2328. [Google Scholar] [CrossRef] [Green Version]
  3. ECPGR. A European Genebank Integrated System AEGIS. Available online: http://www.ecpgr.cgiar.org/aegis/about-aegis/ (accessed on 11 June 2020).
  4. Engels, J.M.M.; Maggioni, L. Benefits of Establishing and Operating a European Collection of Unique and Important Germplasm; ECPGR Secretariat, Bioversity International: Maccarese, Rome, Italy, 2015. [Google Scholar]
  5. Germeier, C.U.; Frese, L.; Bücken, S. Concepts and data models for treatment of duplicate groups and sharing of responsibilities in genetic resources information systems. Genet. Resour. Crop Evol. 2003, 50, 693–705. [Google Scholar] [CrossRef]
  6. Vilmorin-Andrieux, M.M. The Vegetable Garden; Ten Speed Press: Berkeley, CA, USA, 1885. [Google Scholar]
  7. Börjesson, A. Sorter Av Köksväxter: Svenske Priskuranter Fra 1800-Talet till Ca 1930; NordGen Publication Series 2015:01; Nordic Genetic Resource Centre: Alnarp, Sweden, 2015. [Google Scholar]
  8. Lamm, R.; Tometorp, G.; Åvall, H. Klassificerande försök med köksväxter. Medd. Statens Trädgårdsförsök 1945, 26, 165–202. (In Swedish) [Google Scholar]
  9. Mackay, I.; Horwell, A.; Garner, J.; White, J.; McKee, J.; Philpott, H. Reanalyses of the historical series of UK variety trials to quantify the contributions of genetic and environmental factors to trends and variability in yield over time. Theor. Appl. Genet. 2011, 122, 225–238. [Google Scholar] [CrossRef]
  10. UPOV. The International Union for the Protection of New Varieties of Plants. Available online: http://www.upov.int (accessed on 1 April 2020).
  11. Solberg, S.Ø.; Breian, L. Commercial cultivars and farmers’ access to crop diversity: A case study from the Nordic region. Agric. Food Sci. 2015, 24, 150–163. [Google Scholar] [CrossRef]
  12. Alercia, A.; López, F.M.; Sackville Hamilton, N.R.; Marsella, M. Digital Object Identifiers for Food Crops-Descriptors and Guidelines of the Global Information System; Food and Agriculture Organization of the United Nations: Rome, Italy, 2018. [Google Scholar]
  13. Zamir, D. Where Have All the Crop Phenotypes Gone? PLoS Biol. 2013, 11, e1001595. [Google Scholar] [CrossRef] [Green Version]
  14. McCouch, S.R.; McNally, K.L.; Wang, W.; Sackville Hamilton, R. Genomics of gene banks: A case study in rice. Am. J. Bot. 2012, 99, 407–423. [Google Scholar] [CrossRef] [Green Version]
  15. Poland, J.A.; Rife, T.W. Genotyping-by-Sequencing for Plant Breeding and Genetics. Plant Genome 2012, 5, 92–102. [Google Scholar] [CrossRef] [Green Version]
  16. Huang, Y.F.; Poland, J.A.; Wight, C.P.; Jackson, E.W.; Tinker, N.A. Using genotyping-by-sequencing (GBS) for genomic discovery in cultivated oat. PLoS ONE 2014, 9, e102448. [Google Scholar] [CrossRef] [Green Version]
  17. Anglin, N.L.; Amri, A.; Kehel, Z.; Ellis, D. A case of need: Linking traits to Genebank accessions. Biopreservation Biobanking 2018, 16, 337–349. [Google Scholar] [CrossRef] [Green Version]
  18. Singh, N.; Wu, S.; Raupp, W.J.; Sehgal, S.; Arora, S.; Tiwari, V.; Vikram, P.; Singh, S.; Chhuneja, P.; Gill, B.S.; et al. Efficient curation of genebanks using next generation sequencing reveals substantial duplication of germplasm accessions. Sci. Rep. 2019, 9, 650. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  19. McCouch, S.; Baute, G.J.; Bradeen, J.; Bramel, P.; Bretting, P.K.; Buckler, E.; Burke, J.M.; Charest, D.; Cloutier, S.; Cole, G.; et al. Agriculture: Feeding the future. Nature 2013, 499, 23–24. [Google Scholar] [CrossRef] [PubMed]
  20. Van Treuren, R.; van Hintum, T.J.L. Next-generation genebanking: Plant genetic resources management and utilization in the sequencing era. Plant Genet. Resour. 2014, 12, 298–307. [Google Scholar] [CrossRef]
  21. Bothmer, R.V.; Gustafsson, M.; Snogerup, S. Brassica sect. Brassica (Brassicaceae) II. Inter- and intraspecific crosses with cultivars of B. oleracea. Genet. Resour. Crop Evol. 1995, 42, 165–178. [Google Scholar] [CrossRef]
  22. Nagaharu, U. Genome analysis in Brassica with special reference to the experimental formation of B. napus and peculiar mode of fertilization. Japan J. Bot. 1935, 7, 389–452. [Google Scholar]
  23. Hintum, T.J.L.; van Sackville Hamilton, N.R.S.; Engels, J.M.M.; Treuren, R.V. Accession Management Strategies: Splitting and Lumping. In Managing Plant Genetic Diversity; Engels, J.M.M., Ramanatha, R.V., Brown, A.H.D., Jackson, T., Eds.; CABI Publishing: Wallingford, UK, 2002; pp. 113–120. [Google Scholar]
  24. Van Hintum, T.J.; van De Wiel, C.C.M.; Visser, D.L.; Van Treuren, R.; Vosman, B. The distribution of genetic diversity in a Brassica oleracea genebank collection related to the effects on diversity of regeneration, as measured with AFLPs. Theor. Appl. Genet. 2007, 114, 777–786. [Google Scholar] [CrossRef] [Green Version]
  25. Christensen, S.; von Bothmer, R.; Poulsen, G.; Maggioni, L.; Phillip, M.; Andersen, B.A.; Jørgensen, R.B. AFLP analysis of genetic diversity in leafy kale (Brassica oleracea L. convar. acephala (DC.) Alef.) landraces, cultivars and wild populations in Europe. Genet. Resour. Crop Evol. 2011, 58, 657–666. [Google Scholar]
  26. Faltusova, Z.; Kucera, L.; Ovesna, J. Genetic diversity of Brassica oleracea var. capitata gene bank accessions assessed by AFLP. Elect. J. Biotech. 2011, 14. [Google Scholar] [CrossRef]
  27. Izzah, N.K.; Lee, J.; Perumal, S.; Park, J.Y.; Ahn, K.; Fu, D.; Kim, G.B.; Nam, Y.W.; Yang, T.J. Microsatellite-based analysis of genetic diversity in 91 commercial Brassica oleracea L. cultivars belonging to six varietal groups. Genet. Resour. Crop Evol. 2013, 60, 1967–1986. [Google Scholar] [CrossRef]
  28. Clarke, W.E.; Higgins, E.E.; Plieske, J.; Wieseke, R.; Sidebottom, C.; Khedikar, Y.; Batley, J.; Edwards, D.; Meng, J.; Li, R.; et al. A high-density SNP genotyping array for Brassica napus and its ancestral diploid species based on optimised selection of single-locus markers in the allotetraploid genome. Theor. Appl. Genet. 2016, 129, 1887–1899. [Google Scholar] [CrossRef] [Green Version]
  29. Willing, E.M.; Dreyer, C.; Van Oosterhout, C. Estimates of genetic differentiation measured by FST do not necessarily require large sample sizes when using many SNP markers. PLoS ONE 2012, 7, e42649. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  30. Nelson, M.F.; Anderson, N.O. How many marker loci are necessary? Analysis of dominant marker data sets using two popular population genetic algorithms. Ecol. Evol. 2013, 3, 3455–3470. [Google Scholar] [CrossRef] [PubMed]
  31. Menting, F.; Bas, N. The ECPGR Brassica Database. Available online: http://ecpgr.cgn.wur.nl/Brasedb/ (accessed on 15 November 2017).
  32. Nordic Genetic Resource Center. Seed Information Database. Available online: https://nordgen.org/ (accessed on 15 November 2019).
  33. Soleri, D.; Smith, S.E. Morphological and phenological comparisons of two hopi maize varieties conserved in situ and ex situ. Econ. Bot. 1995, 49, 56–77. [Google Scholar] [CrossRef]
  34. Gomez, O.J.; Blair, M.W.; Frankow-Lindberg, B.E.; Gullberg, U. Comparative study of common bean (Phaseolus vulgaris L.) landraces conserved ex situ in genebanks and in situ by farmers. Genet. Res. Crop Evol. 2005, 52, 371–380. [Google Scholar] [CrossRef]
  35. Negri, V.; Tiranti, B. Effectiveness of in situ and ex situ conservation of crop diversity. What a Phaseolus vulgaris L. landrace case study can tell us. Genetica 2010, 138, 985–998. [Google Scholar] [CrossRef]
  36. Hagenblad, J.; Zie, J.; Leino, M.W. Exploring the population genetics of genebank and historical landrace varieties. Genet. Resour. Crop Evol. 2012, 59, 1185–1199. [Google Scholar] [CrossRef]
  37. Solberg, S.Ø.; Yndgaard, F.; Palmé, A. Morphological and phenological consequences of ex situ conservation of natural populations of red clover (Trifolium pratense L.). Plant Genet Resour. 2015, 15, 97–108. [Google Scholar] [CrossRef]
  38. Ellstrand, N.C.; Elam, D.R. Population genetic consequences of small population size: Implications for plant conservation. Annu. Rev. Ecol. Syst. 1993, 24, 217–242. [Google Scholar] [CrossRef]
  39. FAO. Genebank Standards for Plant Genetic Resources for Food and Agriculture; Food and Agriculture Organization of the United Nations: Rome, Italy, 2014. [Google Scholar]
  40. Wright, S. Evolution in Mendelian Populations. Genetics 1931, 16, 97–159. [Google Scholar]
  41. Bohart, G.E.; Todd, F.E. Pollination of Seed Crops by Insects. In Seeds. The Yearbook of Agriculture; US Government Printing Office: Washington, DC, USA, 1961; pp. 240–246. [Google Scholar]
  42. Cruz, V.; Nason, J.; Luhman, R.; Marek, L.; Shoemaker, R.; Brummer, E.; Gardner, C. Analysis of bulked and redundant accessions of Brassica germplasm using assignment tests of microsatellite markers. Euphytica 2006, 152, 339–349. [Google Scholar] [CrossRef]
  43. Diederichsen, A. Duplication assessments in Nordic Avena sativa accessions at the Canadian national genebank. Genet. Resour. Crop Evol. 2009, 56, 587–597. [Google Scholar] [CrossRef]
  44. Lund, B.; Ortiz, R.; Skovgaard, I.M.; Waugh, R.; Andersen, S.B. Analysis of potential duplicates in barley gene bank collections using re-sampling of microsatellite data. Theor. Appl. Genet. 2003, 106, 1129–1138. [Google Scholar] [CrossRef] [PubMed]
  45. Branca, F.; Bas, N.; Artemyeva, A.; De Haro, A.; Maggioni, L. Activities of the Brassica Working Group of the European Cooperative Programme for Plant Genetic Resources (ECPGR). Acta Hort. 2013, 1005, 149–155. [Google Scholar] [CrossRef] [Green Version]
  46. Solberg, S.Ø.; Artemyeva, A.; Yndgaard, F.; Dorre, M.; Niss, J.; Burleigh, S. Duplication assessments in Brassica vegetable accessions. Plant Genet. Resour. 2017, 16, 201–208. [Google Scholar] [CrossRef]
  47. UPOV. UPOV Guidelines for the Conduct of Tests for Distinctness, Uniformity and Stability TG/48/7; International Union for the Protection of New Varieties of Plants: Geneva, Switzerland, 2004. [Google Scholar]
  48. Crawley, M.J. The R Book; John Wiley & Sons Ltd.: Chichester, UK, 2009. [Google Scholar]
  49. Doyle, J.J.; Doyle, J.L. Isolation of plant DNA from fresh tissue. Focus 1990, 12, 13–15. [Google Scholar]
  50. Wright, S. The genetical structure of populations. Ann. Eugen. 1951, 15, 323–354. [Google Scholar] [CrossRef]
  51. Nei, M. Analysis of gene diversity in subdivided populations. Proc. Natl. Acad. Sci. USA 1973, 70, 3321–3323. [Google Scholar] [CrossRef] [Green Version]
  52. R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria; Available online: https://www.R-project.org/ (accessed on 11 June 2020).
  53. Pritchard, J.K.; Stephens, M.; Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 2000, 155, 945–959. [Google Scholar]
  54. Falush, D.; Stephens, M.; Pritchard, J.K. Inference of population structure using multilocus genotype data: Linked loci and correlated allele frequencies. Genetics 2003, 164, 1567–1587. [Google Scholar]
  55. 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]
  56. Jakobsson, M.; Rosenberg, N.A. CLUMPP: A cluster matching and permutation program for dealing with label switching and multimodality in analysis of population structure. Bioinformatics 2007, 23, 1801–1806. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  57. Rosenberg, N.A. DISTRUCT: A program for the graphical display of population structure. Mol. Ecol. Notes 2004, 4, 137–138. [Google Scholar] [CrossRef]
Plants 09 00925 g001 550
Figure 1. Morphological data: Principal component analysis (PCA) biplot where the descriptors of importance are given as arrows and where the length of an arrow is a measure of the descriptor’s variance and the angle between arrows is a measure of the correlation between descriptors, with a small angle expressing a high correlation. PC1 and PC2 explain 42% and 23% of the total variation. Accession names are abbreviated as codes; see Table 1.
Figure 1. Morphological data: Principal component analysis (PCA) biplot where the descriptors of importance are given as arrows and where the length of an arrow is a measure of the descriptor’s variance and the angle between arrows is a measure of the correlation between descriptors, with a small angle expressing a high correlation. PC1 and PC2 explain 42% and 23% of the total variation. Accession names are abbreviated as codes; see Table 1.
Plants 09 00925 g001
Plants 09 00925 g002 550
Figure 2. Boxplots describing the variation in the continuous morphological descriptors. Accession names are abbreviated as codes; see Table 1.
Figure 2. Boxplots describing the variation in the continuous morphological descriptors. Accession names are abbreviated as codes; see Table 1.
Plants 09 00925 g002
Plants 09 00925 g003 550
Figure 3. STRUCTURE analysis based on the full SNP dataset, assuming (A) two genetic clusters (K = 2); (B) three genetic clusters (K = 3). Different colours symbolize the different genetic clusters identified in each individual.
Figure 3. STRUCTURE analysis based on the full SNP dataset, assuming (A) two genetic clusters (K = 2); (B) three genetic clusters (K = 3). Different colours symbolize the different genetic clusters identified in each individual.
Plants 09 00925 g003
Plants 09 00925 g004 550
Figure 4. SNPs markers data: Accession-level principal component analysis (PCA) of the SNP markers, where the use of color refers to the leaf color of the cabbage accessions (purple circles = purple leaves, green squares = green leaves, reddish-brown triangles = purple/green leaves). PC1 and PC2 explain 15% and 10% of the total variation, respectively.
Figure 4. SNPs markers data: Accession-level principal component analysis (PCA) of the SNP markers, where the use of color refers to the leaf color of the cabbage accessions (purple circles = purple leaves, green squares = green leaves, reddish-brown triangles = purple/green leaves). PC1 and PC2 explain 15% and 10% of the total variation, respectively.
Plants 09 00925 g004
Table
Table 1. Overview of the accessions included in the study, sorted into groups based on accession name. For each accession, a code is given. Information is provided on the accession number, genebank holding institute, acquisition year, and donor institute. Acquisition year indicates the year when the accession entered the genebank holding institute, and the donor institute is the organization from which the material was sent.
Table 1. Overview of the accessions included in the study, sorted into groups based on accession name. For each accession, a code is given. Information is provided on the accession number, genebank holding institute, acquisition year, and donor institute. Acquisition year indicates the year when the accession entered the genebank holding institute, and the donor institute is the organization from which the material was sent.
CodeAccession NameAccession NumberGene Bank Acquisition Year, Donor Institute
Amager Tall group
A1Amager HögNGB11705NGB1995, Olson & Sons AB, Sweden
A3Amager Høj Grøn GramiNGB1875NGB1980, Dæhnfeldt A/S, Denmark
A4GramiK2537VIR1988, Unknown, Denmark
A6Amager Høj, Grøn, Toftø 67NGB1873NGB1980, FDB Frø, Denmark
A7Amager Tall ResistentK2475VIR1980, Unknown, Denmark
Amager Short pair
A8Amager Kurzstunkiger OrginalK1485VIR1935, Ohlsens Enke, Denmark
A9Amager L NF OrginalK2248VIR1967, Unknown, Norway
Amager Winter pair
A8Amager Kurzstunkiger OrginalK1485VIR1935, Ohlsens Enke, Denmark
A9Amager L NF OrginalK2248VIR1967, Unknown, Norway
Blåtopp group
B1Amager Faales Blatopp K1181VIR1930, Norsk Frø, Norway
B2BlatoppK2250VIR1967, Unknown, Norway
B3Blatopp Kvithamar K2243VIR1967, Unknown, Norway
B6Blåtopp KvithamarNGB4555NGB1984, Unknown, Norway
Kissendrup pair
K1KissendrupK111VIR1935, Unknown, Denmark
K2Kissendrup TagenshusNGB1996NGB1980, Hansens Amagerfrø, Denamrk
Langendijker group
L1Langendijk SummerK181VIR1959, Unknown, Denmark
L2Langendijker Sommer DebutNGB1997NGB1980, Ohlsens Enke, Denmark
L3Langendijker Sommer LansoNGB1998NGB1980, Hansens Amagerfrø, Denmark
Ruhm v Enkhizen group
R1Ruhm von EnkhizenNGB11718NGB1996, Hansens Amagerfrø, Denamrk
R2Ruhm von Enkhizen HabaNGB1888NGB1980, Hansens Amagerfrø, Denmark
R3Ruhm von Enkhizen, B HunderupNGB2431NGB1982, Unknown, Denmark
Jåtunsalgets pair
J1Jatunsalgets Vinterkål BerbesK2139VIR1959, Unknown, Norway
J2Jåtunsalgets VinterkålNGB5007NGB1983, Unknown, Norway
Loke pair
LO1Loke K2489VIR1982, Unknown, Sweden
LO2LokeNGB12050NGB1997, Unknown, Denmark
Stavanger Torv pair
ST1Stavanger Torv K2175VIR1961, Unknown, Norway
ST2Stavanger TorgNGB8515NGB1990, NLH, Norway
Table
Table 2. Genetic diversity in individual accessions as measured by SNP markers.
Table 2. Genetic diversity in individual accessions as measured by SNP markers.
CodeAverage No AllelesNei′s hObserved Heterozygosity
A11.10.060.06
A31.40.140.15
A41.80.270.30
A61.50.160.17
A71.30.140.12
A81.50.210.22
A91.30.130.14
A111.60.170.15
A121.40.140.15
B11.40.130.15
B21.40.140.18
B31.30.120.13
B61.40.130.14
K11.20.060.06
K21.40.140.15
L11.40.160.15
L21.40.130.10
L31.50.180.17
R11.50.170.19
R21.50.170.16
R31.40.140.13
J11.30.100.10
J21.40.140.15
LO11.60.190.22
LO21.60.190.18
ST11.30.110.14
ST21.30.120.13
Table
Table 3. Pairwise FST values within groups based on all markers (full dataset) and from subsets of 10 markers (subsample average). Subsample values that do not include the pairwise FST value for the full dataset are highlighted in bold.
Table 3. Pairwise FST values within groups based on all markers (full dataset) and from subsets of 10 markers (subsample average). Subsample values that do not include the pairwise FST value for the full dataset are highlighted in bold.
AccessionAccessionFull DatasetSubsample Average
12FSTFSTFST − 1 SEFST + 1 SE
A1A30.2160.2180.2150.222
A1A40.1840.1840.1820.186
A1A60.2520.2430.2400.247
A1A70.3040.3090.3050.314
A3A40.1030.1040.1030.105
A3A60.1490.1490.1470.151
A3A70.1750.1730.1710.176
A4A60.1100.1110.1090.112
A4A70.1320.1320.1310.134
A6A70.1660.1690.1660.171
A8A90.3260.3270.3240.331
A11A120.1440.1430.1410.145
B1B20.2910.2940.2900.298
B1B30.2280.2300.2260.234
B1B60.2180.2160.2130.220
B2B30.3210.3240.3200.328
B2B60.3040.3090.3050.313
B3B60.0870.0850.0840.087
K1K20.2530.2490.2450.253
L1L20.1880.1880.1850.190
L1L30.1600.1620.1600.164
L3L20.1170.1170.1150.119
R1R20.1110.1100.1080.111
R1R30.1440.1490.1470.151
R2R30.1290.1290.1270.131
J1J20.2020.2010.1980.205
LO1LO20.0950.0950.0940.097
ST1ST20.3000.2940.2900.298

Share and Cite

MDPI and ACS Style

Palmé, A.E.; Hagenblad, J.; Solberg, S.Ø.; Aloisi, K.; Artemyeva, A. SNP Markers and Evaluation of Duplicate Holdings of Brassica oleracea in Two European Genebanks. Plants 2020, 9, 925. https://doi.org/10.3390/plants9080925

AMA Style

Palmé AE, Hagenblad J, Solberg SØ, Aloisi K, Artemyeva A. SNP Markers and Evaluation of Duplicate Holdings of Brassica oleracea in Two European Genebanks. Plants. 2020; 9(8):925. https://doi.org/10.3390/plants9080925

Chicago/Turabian Style

Palmé, Anna E., Jenny Hagenblad, Svein Øivind Solberg, Karolina Aloisi, and Anna Artemyeva. 2020. "SNP Markers and Evaluation of Duplicate Holdings of Brassica oleracea in Two European Genebanks" Plants 9, no. 8: 925. https://doi.org/10.3390/plants9080925

APA Style

Palmé, A. E., Hagenblad, J., Solberg, S. Ø., Aloisi, K., & Artemyeva, A. (2020). SNP Markers and Evaluation of Duplicate Holdings of Brassica oleracea in Two European Genebanks. Plants, 9(8), 925. https://doi.org/10.3390/plants9080925

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