Development of a Core Collection of Strawberry Cultivars Based on SSR and CAPS Marker Polymorphisms

Abstract

A strawberry core collection was established based on simple sequence repeat and cleavage amplified polymorphic sequence marker polymorphisms in 119 strawberry cultivars using the “PowerCore” program. The core collection consisted of 19 cultivars. The correlation coefficients for the diversity index were significant between the core collection cultivars and all cultivars. Allele frequencies of each marker allele were not significantly different between the core collection cultivars and all cultivars according to Fisher’s exact test. Cluster analysis indicated that the selected core collection cultivars evenly distributed throughout the multiple clusters and principle component analysis clearly showed major principle components of core collection cultivars distributed widely among those of all cultivars. Furthermore, core collection cultivars tended to harbor minor alleles. These results demonstrated that the core collection cultivars were suitably selected in terms of reflecting the genetic diversity of all strawberry cultivars.

Introduction

The concept of a core collection (or a core subset) of important agricultural crops was proposed by Frankel and Brown (Brown, 1989; Frankel, 1984; Frankel and Brown, 1984). They noted that the use of core collections allowed crop breeders to eliminate the cost of preserving germplasms, and enabled the efficient evaluation of multiple agronomic traits from a germplasm in the field. A core collection is compiled to conserve phenotypic and/or genetic variations, and is used to enhance genetic diversity and facilitate the selection of suitable candidate alleles related to important agronomic traits. Core collections have been reported for numerous crops, including rice (Ebana et al., 2008), soybean (Kaga et al., 2012), apple (Hokanson et al., 1998; Potts et al., 2012), olive (Belaj et al., 2012), cassava (Chavarriaga-Aguirre et al., 1999), peanut (Holbrook et al., 1993), plantain (Ude et al., 2003), and wheat (Balfourier et al., 2007).

As for the strawberry, Geibel et al. (2004) identified 108 core cultivars necessary to preserve the diversity of European strawberry germplasms. However, their selection procedure was based on historical and phylogenetic information, not genetic variations. Hancock et al. (2002) included 38 Fragaria genotypes in a super-core collection, based on data generated from replicated plantings at five locations in the United States. Although they indicated that the selected 38 genotypes originated from broad geographical locations, and represented a wide range of horticultural and climatic attributes, the selection was based on phylogenetic and geographical factors, not genetic variations.

A cultivated strawberry (Fragaria × ananassa) is an octoploid (2n = 8x = 56) with a heterozygous genome. Because its genome is highly complex, information regarding genome structure and genetic analyses has been limited to date. However, useful details regarding the strawberry genome, including that of woodland strawberries, have recently been reported. Lewers et al. (2005) developed polymorphic simple sequence repeat (SSR) markers from GenBank sequences of species with varied relatedness to Fragaria and Rubus species. Folta et al. (2005) proposed that expressed sequence tag-SSR (EST-SSR) markers from the cDNA library of F. × ananassa are useful for accelerating the discovery of genes related to important agronomic traits. Shulaev et al. (2011) described the genome structure of the woodland strawberry, F. vesca (2n = 2x = 14), using next generation sequencing technology. Isobe et al. (2013) developed microsatellite EST-SSR markers from F. vesca and F. × ananassa ESTs, and revealed that 45 EST-SSR markers can distinguish 129 strawberry lines from each other.

In addition to SSRs, Kunihisa et al. (2003) developed six cleavage amplified polymorphic sequence (CAPS) markers to discriminate among 14 strawberry cultivars. Furthermore, they identified another 13 CAPS markers useful for cultivar discrimination (Kunihisa et al., 2005). They also confirmed the utility of 25 CAPS markers to distinguish 125 strawberry cultivars from each other (Kunihisa et al., 2009).

Researchers have used different methods for extracting a core collection. Hu et al. (2000) proposed the stepwise clustering method. With this method, Xu et al. (2006) developed a core collection for Island cotton, and Ebana et al. (2008) developed a mini-core collection for a Japanese rice landrace. Franco et al. (2006) demonstrated that the M strategy (Schoen and Brown, 1993) was the most effective method to maximize the genetic diversity indices (Shannon and Weaver, 1949) for constructing a core subset. This method maximizes the allele richness at each marker locus. Kim et al. (2007) developed the PowerCore software, which applies an advanced M strategy with a heuristic search to construct a core collection. They determined that the heuristic search approach of PowerCore was superior to a random search. Using PowerCore, core collections for ramie (Luan et al., 2014), rice (Agrama et al., 2009), soybean (Kaga et al., 2012), and cassava (Oliveira et al., 2014) have recently been developed. However, there are no reports regarding a strawberry core collection based on DNA marker polymorphisms. Therefore, the objective of this study was to develop a core collection for cultivated strawberries using DNA polymorphic markers.

Materials and Methods

Plant materials

The 119 strawberry cultivars used in this study are listed in Table 1. These cultivars have been used in previous studies, in which CAPS markers (Kunihisa et al., 2009) and SSR markers (Isobe et al., 2013) were developed and applied separately to analyze all cultivars. Kunihisa (2010) selected these cultivars based on the fact that seedlings of the cultivars are available for research use from breeders or multiple research organizations. These cultivars are composed of the first foreign cultivar groups, which were introduced in 19^th century, following improved cultivar groups from the Meiji (20^th century) to Heisei eras (21^st century), some ornamental cultivars, and some current foreign cultivars. These facts suggested that the 119 cultivars listed in Table 1 covered most of the genetic variations of strawberry cultivars grown in modern Japan. Then, we selected these 119 cultivars as a basic population to extract a core collection and designated this ‘all cultivars’.

Table 1

Entire cultivars used in this study.

Table 1

Continued

Of the 119 all cultivars, ‘Kurume IH 1 Go’ was the only decaploid, and was derived from a cross between F. × ananassa and F. nilgerrensis. ‘Serenata’, ‘Vivarosa’, and ‘Miranche’ were not considered to be F. × ananassa cultivars because their petals were red instead of white. Most strawberry cultivars with red petals originated from a cross between F. × ananassa and Potentilla (Ellis, 1962). All other cultivars were the octoploid, F. × ananassa.

Detection of SSR genotypes and CAPS markers to extract the core collection

DNA extraction was performed with a DNeasy Plant Mini Kit (Qiagen Inc., Germany) using young leaves of plants. The SSR marker analysis of all cultivars was detailed in Isobe et al. (2013), and CAPS marker analysis was reported by Kunihisa et al. (2009). All the SSR markers and CAPS markers are listed in Table 3 and Table 4, respectively. We compared the bands in electrophoresis gels and peak sizes during fragment analyses, and signals differing in molecular weight were considered independent dominant markers in the strawberry genome. However, many SSR primer pairs produced more than two signals during a single analysis. This is because the strawberry genome consists of homoeologous chromosomes with similarities among DNA sequences. Therefore, we were unable to determine whether different signals were allelic. The presence of a signal was scored as genotype ‘1’, while absence of a signal was scored as genotype ‘0’. Heterozygous genotypes were not used.

Because CAPS markers are co-dominant, different signals may be considered allelic signals, and heterozygous signals can be detected. However, PowerCore, which was used to compile the core collection, cannot process heterozygous genotypes; it can only distinguish the presence or absence of signals. Furthermore, if we considered CAPS marker polymorphisms as co-dominant markers, there would be differences between SSR and CAPS markers regarding the number of alleles (i.e., two for SSR and three for CAPS), and in terms of weight. As a result, we classified different CAPS marker signals into different loci, similar to what was done for SSR markers. Totally, 43 and 123 dominant alleles, which were generated by 25 CAPS and 45 SSR markers, respectively, were used to extract the core collection and further characterization analysis.

Extraction of the strawberry core collection

Many previous studies (Agrama et al., 2009; Belaj et al., 2012; Kaga et al., 2012; Oliveira et al., 2014) adopted PowerCore (Kim et al., 2007) as the software for construction of a core collection or core set because PowerCore has a function that can maintain 100% diversity of the base population. Therefore, PowerCore was used to extract the strawberry core collection from the 119 strawberry cultivars in this study.

Genetic diversity between the core collection cultivars and all other cultivars

To confirm the cultivars selected for the core collection were appropriate, we calculated the genetic diversity index (DI) using two methods (Nei, 1973; Shannon and Weaver, 1949) for core collection cultivars and for all cultivars, and compared the correlation coefficients of DI from the two populations. Additionally, we compared the allele frequency of each SSR and CAPS marker allele between the core collection cultivars and all cultivars using Fisher’s exact test to determine whether each marker was significant.

Characterization of the core collection

In order to investigate the genetic differences and similarities of 119 all cultivars and 19 core collection cultivars, we performed cluster analysis and principle component analysis using genotype data as explanatory variables.

1.  Cluster analysis

Cluster analysis was conducted as follows; the raw genotype data were input into GGT2.0 (van Berloo, 2008) software in order to formulate the jaccard dissimirality matrix. A dendrogram was constructed using the matrix with an unweighted pair group method (UPGMA) of cluster analysis that was incorporated into MEGA 6 (Tamura et al., 2013).

2.  Principle component analysis

Principle component analysis was performed with statistic software R version 3.2.3 (R Core Team, 2015-12-10). A scatter plot matrix of principle component scores was generated with R package ggplot2 (Wickham, 2009).

3.  Calculation of minor allele frequency

Furthermore, we investigated minor allele frequency throughout all cultivars. A minor allele was defined as follows; if the number of cultivars which harbor either allele of one marker was less than 19 (or more than 100) among all cultivars, such markers were regarded as “markers with a minor allele”. We summed and compared the number of markers with minor alleles throughout the core collection and the other cultivars.

Results

Selection of the strawberry core collection cultivars

We included 19 strawberry cultivars in the core collection based on the SSR and CAPS marker polymorphisms for 119 cultivars (Table 2). These 19 cultivars successfully harbored 100% of alleles of every marker (Tables 3, 4). Among the core collection cultivars, ‘Serenata’ originated from the UK, ‘Elsanta’ was from the Netherlands, and the other 17 were from Japan. ‘Kurume IH 1 Go’ was a decaploid, while ‘Serenata’ and ‘Miranche’ were unique cultivars with red petals, which suggested they may not be octoploid. The other 16 cultivars were octoploid and considered to be F. × ananassa.

Table 2

Core collection cultivars.

Table 3

Information on SSR markers used in this study.

Table 3

Continued

Table 4

Information on CAPS markers used in this study.

Genetic diversity between the core collection cultivars and all other cultivars

The correlation coefficients for the DIs between the core collection cultivars and all cultivars were highly significant according to the two methods used to calculate the DIs (Fig. 1). Figure 2 presents the distribution of P values, which were calculated using Fisher’s exact test for all SSR and CAPS markers. There were no markers with significant differences in P values at the 1% level, and only seven (7/166 = 4.22%) had significantly different P values at the 5% level.

Fig. 1

Correlation coefficients for the genetic diversity index of the core collection cultivars and the other cultivars. (A) Diversity index based on the method of Nei (1973). (B) Diversity index based on the method of Shannon and Weaver (1949).

Fig. 2

Distribution of P values calculated by Fisher’s exact test based on the allele frequency of each SSR and CAPS marker allele between the core collection cultivars and all cultivars.

Characterization of the core collection cultivars

Cluster analysis

Figure 3 shows the dendrogram formulated by the cluster analysis based on the SSR and CAPS marker polymorphism. When all cultivars were divided into 4 clusters, core collection cultivars spread widely in multiple clusters; Cluster 1, 2, 3, and 4 include 5 (‘Fukuba’, ‘Kurume IH 1 Go’, ‘Fukuoka S6’, ‘Serenata’, ‘Miranche’), 2 (‘Deco Rouge’, ‘Tsuburoman’), 3 (‘Ouko’, ‘Elsanta’, ‘HS138’), and 9 (‘Summer Berry’, ‘Tochihitomi’, ‘Beniyutaka’, ‘Kurume 56 Go’, ‘Akihime’, ‘Benihoppe’, ‘Koju’, ‘Tochinomine’, ‘Sawaberry’) core collection cultivars, respectively.

Fig. 3

A dendrogram for all 119 all cultivars generated by UPGMA cluster analysis based on SSR and CAPS markers polymorphism. Black circles indicate core collection cultivars.

Principle component analysis

Table 5 shows a summary of principle component analysis based on the SSR and CAPS marker polymorphism. PC1, PC2, PC3, and PC4 showed more than a 5% proportion for total variance. Figure 4 indicates the relationship among PC1, PC2, PC3, and PC4 of the core collection and all cultivars. The distribution of PC1, PC2, PC3, and PC4 between the core collection and all cultivars was normal and similar, except for PC4. Furthermore, the 19 cultivars of the core collection distributed evenly and widely in each pairwise plot figure among PC1, PC2, PC3, and PC4.

Table 5

Summary of principle component analysis based on the SSR and CAPS marker polymorphism among all cultivars.

Fig. 4

Matrix of the pairwise plot, distribution, and correlation coeffficients of PC1, PC2, PC3, and PC4 for core collection cultivars and all cultivars derived from principle component analysis based on the SSR and CAPS marker polymorphism. Red circles and blue triangles indicate core collection cultivars and the other cultivars, respectively.

Comparison of minor allele frequency

Table 6 shows the number of markers which harbor minor alleles of the core collection and the other cultivars. Out of the core collection, ‘Miranche’, ‘Serenata’, and ‘Kurume IH 1 Go’ carry 16 to 18 minor alleles, and next were ‘Deco Rouge’, ‘Elsanta’, ‘Fukuba’, and ‘Summer berry’ (from 10 to 12 minor alleles). Among 8 cultivars, which carried 10 to 12 minor alleles, half of those cultivars belonged to the core collection. The segregation ratio (4:4) was significantly different from that of all cultivars (19:100) based on Fisher’s exact test. Furthermore, there were no other cultivars that showed more than 16 minor alleles. As a whole, the core collection tended to harbor more minor alleles than the other cultivars.

Table 6

Number of markers which harbor a minor allele of the core cultivars and other cultivars.

Discussion

The strawberry was introduced to Japan at the end of the Edo period in the middle of the 19^th century, and ‘Fukuba’ was developed as the first cultivated strawberry in Japan in 1899. Based on this cultivar, many new strawberry cultivars were developed throughout the 20^th and 21^st centuries. In this study, based on the DNA marker polymorphism of 119 cultivated strawberry cultivars, we successfully developed a strawberry core collection composed of 19 cultivars.

Firstly, we would like to discuss the conformation of the original 119 cultivars. As we mentioned, the original population included cultivars which did not belong to F. × ananassa. In rice core collection, Kojima et al. (2005) developed a small core collection of world rice. Based on their study, Ebana et al. (2008) developed a mini-core collection of Japanese landrace in order to cover its genetic diversity of Japanese landrace, because core collection selected by Kojima et al. (2005) included only two Japanese accessions. In this regard, the strawberry core collection developed in this study might not fully cover the genetic diversity of F. × ananassa. Even so, there are only one cultivar (‘Kurume H1 Go’) that is not octoploid and two cultivars (‘Serenata’ and ‘Miranche’) that are for ornamental use among the 19 collection cultivars. Furthermore, 16 residual cultivars, which are octoploid and for edible use, covered 88.5% of all alleles of SSR and CAPS markers. These facts suggest that the genetic diversity of cultivated and edible F. × ananassa was not underestimated in this study.

Although the distribution of the core collection cultivar DIs was discontinuous, the correlations between the core cultivars and all cultivars were highly significant (Fig. 1). Additionally, none of the P values were significant at the 1% level (Fig. 2). These findings indicate the selected core collection cultivars reflected the allele frequency of the SSR and CAPS marker alleles for all cultivars to cover their genetic diversity.

In order to characterize the selected core collection, we performed cluster analysis (UPGMA method) and principle component analysis by using the same SSR and CAPS marker polymorphism. Isobe et al. (2013) completed UPGMA cluster analysis to reveal the genetic similarities among 129 F. × ananassa cultivars. According to their results, cultivars with red petals such as ‘Vivarosa’, ‘Miranche’, and ‘Serenata’, were isolated from other cultivars. Cluster 1 of Figure 3 in this study included the same red petal cultivars. Similarly, Cluster 4 of Figure 3 included recently developed cultivars, which was similar to the results of Isobe et al. (2013). The results of cluster analysis in this study supported the study of Isobe et al. (2013), and also indicated that core collection cultivars could be selected almost evenly from different clusters to cover the genetic diversity of all cultivars.

Principle component analysis showed PC1, PC2, PC3, and PC4 exhibited more than 5% proportion of variance for total genetic variation (Table 5). Next, we compared principle component scores of these four principle component among the core collection and all cultivars (Fig. 4). The fact that not only all cultivars, but also the core collection showed normal frequency distribution of PC1, PC2, PC3, and PC4 showed the selected core collection fully covered the genetic diversity of all cultivars. Furthermore, pairwise plots of PC1, PC2, PC3, and PC4 exhibited that the core collection evenly distributed among all cultivars. The results of principle component analysis also supported the notion that the selected core collection covered the original diversity of all cultivars.

The strawberry core collection compiled in this study included ‘Fukuba’. Comparing minor allele frequency among the core collection (Table 6), 7 cultivars including ‘Fukuba’ harbored more DNA markers with minor alleles than other cultivars. Of those 7 cultivars, ‘Miranche’ and ‘Serenata’ are cultivars with red petals for ornamental use, ‘Kurume IH 1 Go’ is not octoploid, and ‘Deco Rouge’ and ‘Summer berry’ are ever-bearing cultivars, while ‘Elsanta’ is a foreign cultivar. Therefore, ‘Fukuba’ is the only June-bearing Japanese cultivar that showed higher minor allele frequency. These results suggested cultivars with more minor alleles were selected in the core collection. As we indicated in Table 6, ‘Fukuba’ harbored a relatively higher minor allele frequency. Although ‘Fukuba’ could have contributed as a genetic source for Japanese strawberry breeding, the relatively higher minor allele frequency of ‘Fukuba’ reminded us that only some of the minor alleles, which were linked to superior agronomic traits, were inherited by the descendent cultivars.

The recently developed ‘Fukuoka S6’, ‘Kurume 106 Go’, and ‘Kurume IH 1 Go’ belong to the same cluster in this study. Of these two cultivars, ‘Fukuoka S6’ and ‘Kurume IH 1 Go’, were selected for the core collection, which suggests both cultivars differentiated from the other recently developed cultivars, and carry unique sequences. Additionally, ‘Kurume IH 1 Go’ was estimated to carry a unique sequence which was derived from its pentaploid ancestor.

The core collection cultivars were selected based only on genotype data because there was a lack of agronomic information for all cultivars. Kaga et al. (2012) compiled a core set of soybean germplasms using genotype and agronomic data. If core collection cultivars exhibit similar agronomic traits, the collection is not ideal. This is because agronomic trait diversity is the most important factor in preparing a core collection. Although the core collection proposed in this study was selected based only on genetic diversity, the collection cultivars showed different agronomic traits (Table 2), which indicates they may reflect the practical diversity of cultivated strawberries.

In the present conditions, it is extremely difficult to clarify the SSR locus in the strawberry genome on its own because of its octoploidy and every SSR marker could be a multi-loci diagnostic (MLD) marker. When the sequence lengths between a primer pair of MLD markers are the same across homoeologous genomes, the marker cannot distinguish alleles located on different genomes. We consider the approach in this study is the best under the current conditions; however, further validation is necessary when the complete strawberry genome is available in the future. Additionally, single nucleotide polymorphism (SNP) markers, which can be polymorphic among strawberry cultivars, have recently been developed (Bassil et al., 2015; Sargent et al., 2015). Because SNPs are usually detected more frequently than SSRs in the cultivated strawberry genome, the development of an improved core collection set is expected using SNPs and the genome-wide genetic differences among strawberry cultivars.

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