Journal Identification = HORTI Article Identification = 3846 Date: March 5, 2011 Time: 1:44 pm

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Scientia Horticulturae 128 (2011) 311–319

Contents lists available at ScienceDirect

Scientia Horticulturae

journa l homepage: www.e lsev ier .com/ locate /sc ihor t i

onstruction and evaluation of a primary core collection of apricot germplasmn China

uzhu Wang ∗,1, Junhuan Zhang1, Haoyuan Sun, Ning Ning, Li Yangnstitute of Forestry and Pomology, Beijing Academy of Agriculture and Forestry Science, Beijing 100093, China

r t i c l e i n f o

rticle history:eceived 10 June 2010eceived in revised form6 November 2010ccepted 26 January 2011

eywords:pricotenetic diversityrimary core collectionSR molecular marker

a b s t r a c t

As the origin center of apricots, China has the richest apricot germplasm resources. The establishmentof core collections is very important for a better evaluation and utilization of apricot germplasm. In thisstudy, 1501 apricot accessions from the initial collections in China were used to sample and establisha primary core collection. Data of 18 traits including both quantitative and qualitative were collectedand used to design sampling strategy. The overall sampling strategy includes principles for grouping,sampling proportion within group and sampling method from each group. Three sampling proportions5%, 10% and 15% were used in the study. Our results suggest that 10% was the best entire sampling ratiofor primary core collection of apricots. With 10% entire sampling ratio, the optimal sampling strategy wasto group samples based on their growing regions, in combination with logarithmic sampling proportionwithin each group. Using this sampling strategy, we have established a primary core collection with

ampling strategy 150 accessions, and the primary core collection can well represent the genetic diversities of the entirecollection. The genetic diversity of the apricot primary core collection was further analyzed using SimpleSequence Repeats (SSRs) molecular marker technique. A total of 22 polymorphic specific primers weredeveloped and 196 alleles were detected. The average number of alleles in each locus was 8.9, suggestingthat there were very rich genetic variances among the prime core collection. This also confirms that ourprimary core collection is a good representation of the genetic diversities of the whole collections at DNA

level.

. Introduction

Genetic diversity is the core of biodiversity. Abundant geneticiversity in plants can provide a broad genetic background for cropreeding and genetic research. As the origin center of apricots,hina has the richest resources of both wild and cultivated vari-ties (Zhang and Zhang, 2003). There had been about 3000 apricotarieties in the world, and 2000 varieties or types were preservedn China, including 7 species and 11 variations (Wang, 1998). How-ver, with continuous collecting of germplasm resources, the sizesf collections have become larger and larger, which created manyimitations to the preservation, the evaluation, the research and these of germplasm resources. To solve this problem, Frankel (1984)roposed the concept of the core collection. A core collection is

efined as a representative sample of the whole collection withinimum repetitiveness and maximum genetic diversity of a crop

pecies and its relatives (Frankel, 1984; Frankel and Brown, 1984).hus, the establishment of core collections is a helpful means to bet-

∗ Corresponding author. Tel.: +86 10 82592521; fax: +86 10 62598744.E-mail address: chinabjwyz@sohu.com (Y. Wang).

1 These authors contributed equally to this work.

304-4238/$ – see front matter © 2011 Elsevier B.V. All rights reserved.oi:10.1016/j.scienta.2011.01.025

© 2011 Elsevier B.V. All rights reserved.

ter evaluate and use plant germplasm. According to its definition, acore collection should avoid identical or near-identical accessions,and contains as much diversity as possible. Therefore, a good andefficient sampling strategy is important in the construction of corecollections. Many methods have been introduced for germplasmsampling in order to form a core collection, but most samplingstrategies were proposed and developed according to phenotypicdata from annual crops (Brown et al., 1987; Diwan et al., 1995; Liet al., 2002; Bhattacharjee et al., 2007; Yao et al., 2008). Now stud-ies on core collections of more than 30 crops have been carriedout worldwide. However, among these 30 crops, only a few areperennial plants. This is especially true for fruit plants, as core col-lections have only been established for species such as Japaneseapricot, peach, apple and cherimoya (Gao et al., 2005; Li et al.,2007; Escribano et al., 2009; Zhang et al., 2009a, 2010). In apricot,only one study reported the establishment of a primary core col-lection, which was based on a partial collection of apricot (Prunusaemeniaca) resources in China (Zhang et al., 2009b).

In this study, we have developed the sampling strategy for a pri-mary core collection from the whole collection of China apricots.Furthermore, we have also evaluated the genetic diversity distribu-tion and the genetic variances of the primary core collection usingSSR technique. These data have laid the foundation for construct-

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312 Y. Wang et al. / Scientia Horticult

I II III Grouping principles Sampling proportion within group Sampling method

Variety origin

Geographical regions

Variety utility

Random

sampling

proportion of genetic diversity

fixed proportion

proportion of square root

proportion of logarithm

iacoC

2

2

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Non-grouping fixed proportion

Fig. 1. Sampling scheme of primary core collection for apricot.

ng a core collection of perennial wood plants, especially fruit crops,nd offered a database for abundant theory and technology of plantore collections. These results will also facilitate the identificationf useful parents for future improvement in apricot breeding inhina and elsewhere.

. Materials and methods

.1. Data set

The 1501 apricot accessions used in this study were previouslyescribed and listed in two books ‘China Fruit Flora: Apricot’ (Zhangnd Zhang, 2003) and ‘The Catalogue of Germplasm Resources ofruit Trees’ [The Institute of Fruit Tree and Chinese Academy ofgricultural Sciences (Fruit Research Institute and C.A.A.S., 1998)].he database consists of 18 morphologic and agronomic traits: (1)ruit weight, (2) fruit shape, (3) fruit height, (4) lateral width, (5)entral width, (6) symmetry in ventral view, (7) suture, (8) depthf stalk cavity, (9) shape of apex, (10) ground color, (11) hue of overolor, (12) pulp color, (13) pulp texture, (14) juice content, (15) sol-ble solid content, (16) adherence of stone to flesh, (17) ripe stagend (18) flesh fiber content. Most of the quality characters weredjusted or standardized according to the terms described in theook ‘China Fruit Flora: Apricot’ (Zhang and Zhang, 2003). Accord-

ng to the method of Li et al. (2002), the quantitative charactersere quantified into 10 categories, where the distance between

wo neighboring categories was every 0.5 standard deviation.

.2. Sampling scheme of primary core collection

A flowchart of the methodology used in the establishment ofore collection is presented in Fig. 1. Sampling schemes were devel-ped at three levels, that is, the grouping principle, the samplingroportion within each group and the sampling method withinach group. Grouping principles included variety origin, geograph-cal distributions, variety utility, and non-grouping. The varietyrigin can be further classified according to the breeding types; theeographical distributions included northeast china frigid areas,orth china warm areas, northwest china arid areas, tropicalnd subtropical areas, southwest china plateau region; for theariety utility, there were cultivars for fresh market and process-ng, cultivars for kernel consuming, ornamental cultivars, newlyreed cultivars (or lines), introduced germplasm and inter-specificybrids. Sampling proportion within each group was determinedased on square root (S), logarithm (L), index of genetic diversity (G)nd fixed proportion (P). Random sampling was carried out in eachroup. In order to reduce errors of random sampling, random selec-

ion was repeated for three times and resulted in three core subsetsn each method. All the together, combining the grouping principle

ith the sampling proportion within each group and the samplingethod within each group, 13 sampling methods were used to

evelop core collection, and 39 core subsets were generated.

urae 128 (2011) 311–319

For the size of core subset under each sampling approach, sam-pling proportion from the whole collection was designed as 5%, 10%and 15%.

In order to avoid losing some important biological types, at leastone accession from each group was included in the core collection.

In addition, the sample selecting of the primary core collectionwere conducted according to the determined sampling strategies incombination with many other germplasm information at the sametime. Some accessions with distinct traits will be deliberately addedto the list of the primary core collection if not being selected.

2.3. Evaluating parameters for sampling methods

Six evaluating parameters were selected according to Li et al.(2002) and Wang et al. (2007) with some modification, includingcoefficient of variation (CV), index of genetic diversity (I), varianceof phenotype value (VPV), ratio of phenotype retained (RPR), differ-ence of phenotype minimum (Dmin) and difference of phenotypemaximum (Dmax). Formulas for each parameter are as following:

I =−∑

i

∑jPij ln Pij

N

where Pij is the frequency of the jth phenotype in the ith trait; N isthe total number of traits.

RPR =∑

iMi∑iMi0

where, Mi0 is the number of the ith phenotype of the initialgermplasm group; Mi is the number of the ith phenotype of coresubset.

CV =

∑((√∑(Xij − Xi)

2/(n − 1)

)/Xi

)

N

where Xij is the phenotypic value of the jth accession in the ith trait;Xi is the mean of phenotypic values of all accessions in the ith trait.n is the number of accessions; N is the total number of traits.

VPV =∑[

STDi

(∑j(Xij − Xi)

2/(Mi − 1)

)]N

where STDi is the standardization for the ith trait; Xij is the phe-notypic value of the jth accession in the ith trait; Xi is the mean ofphenotypic values of all accessions in the ith trait, Mi is the num-ber of the ith phenotype in core collection; N is the total number oftraits.

Dmax =∑

[STDi (Maxi − Maxi0)]N

where STDi is the standardization for the ith trait; Maxi0 is the max-imum value of the ith trait of the initial germplasm group; Maxi isthe maximum value of the ith trait of core subset; N is the totalnumber of traits.

Dmin =∑

[STDi (Mini − Mini0)]N

where STDi is the standardization for the ith trait; Mini0 is the min-imum value of the ith trait of the initial germplasm group; Miniis the minimum value of the ith trait of core subset; N is the totalnumber of traits.

2.4. Data processing

Analysis of the significance of differences was carried outusing SAS. Duncan’s multiple range test (DMRT) was employedto compare the differences of the above-mentioned parameters

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Y. Wang et al. / Scientia Horticulturae 128 (2011) 311–319 313

Table 1Comparison of core subsets in different size by test parameters.

Sampling percentage (%) Rank-num Average rank RPR (%)

I VPV Dmax Dmin CV

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5 2.0 2.0 1.010 1.0 1.0 1.015 2.0 2.0 1.0

mong grouping principles and sampling proportions. Then eachf parameters was allotted a rank based on the result of multipleomparisons. The final results were presented by the average of theanks of the different parameters. A smaller rank number indicatesbetter sampling method and a more valid core subset.

.5. Evaluation of the representation of the primary coreollection based on the validated sampling methods

To determine the representation of the primary core collection,ix quantitative traits and six evaluating parameters were selectedo compare the entire and core collection. The six traits include fruiteight, fruit height, lateral width, ventral width, soluble solid con-

ent and ripe stage. The six evaluating parameters are Maximum,inimum, Range (R), Coefficient of variation (CV), Variance of pheno-

ype value (VPV) and Ratio of trait retained (RTR). The values of R andTR were calculated using the following formulas, respectively:

= the maximum value of one trait − the minimum value of one tra

TR(%) = the R value of one trait in the core subsetthe R value of one trait in the initial collection

× 100

The values of I, CV, VPV were calculated according to the formulashown in Section 2.3.

.6. DNA isolation and SSR analysis

0.5 g of leaf tissues were ground into a fine powder in liquiditrogen using mortars and pestles. Genomic DNA was iso-

ated using cetyltrimethylammonium bromide (CTAB) method asescribed (Doyle and Doyle, 1987) with only minor modifications.

Polymerase chain reaction (PCR) amplifications were performedn a MyCyclerTM thermal cycler (Bio-Rad Laboratories, Inc., USA).pricot SSR reactions were conducted following the proceduresescribed by Ning et al. (2009). The SSR primers in Table 2 wereesigned based on the nucleotide sequences reported by Testolint al. (2000), Sosinski et al. (2000), Dirlewanger et al. (2002), Lopest al. (2002), and Hagen et al. (2004). These primers were firstcreened for amplification of polymorphic and unambiguous bandsn the 150 accessions. The selected high polymorphic and specificSR primers were used for further analysis of genetic diversity.he PCR products were separated on 8% denatured polyacrylamideels and the electrophoresis was conducted at 1000 V for 1.5 h. TheNA bands were visualized by silver staining of gels according toanufacturer’s protocol (Promega, Madison, USA).

.7. Statistical analysis of SSR data

The bands were scored and converted into digital (0/1) format,here 1 indicated the presence of a band and 0, the absence ofband. The dataset was converted into a mathematical matrix,

hich was used to perform statistical analysis and calculate theumber of alleles, Nei’s gene diversity, also named polymorphic

ndex content, Shannon’s information index and genetic distance.he calculation was programmed using Cervus version 2.0 andopGene version 1.32 software. In addition, the data analysis was

1.0 2.0 1.6 96.861.0 1.0 1.0 98.841.0 3.0 1.8 99.60

performed using NTSYSpc-2.11F to obtain the Dice similarity coeffi-cient matrix. Cluster analysis based on similarity coefficient matrixwas performed with Un-weighted Pair Group Method of ArithmeticAverage (UPGMA).

3. Results

3.1. Determination of the size of core subset

To determine the size of the core collection, three sampling per-centages from the whole collection were used: 5%, 10% and 15%.Thus, combined with 13 sampling strategies, in total 39 core subsetswere established. As shown in Table 1, when sampling percentage10% was used, the rank-nums of evaluating parameters such as I,VPV and CV were all 1.0, indicating the best results. Among thethree sampling percentages 5%, 10% and 15%, the percentages ofphenotype retained (RPR%) were all very high, and reached 96.86%,98.84% and 99.60%, respectively. With the smallest average rank ofall the parameters and a high RPR%, which indicates an optimal sizeof core subset and a large genetic variation of the initial collectionstill remaining in the core subset, 10% was selected as the best sam-pling percentage from the whole collection when constructing thecore collection.

3.2. Grouping principles and sampling proportion within eachgroup

Using sampling percentage 10% and randomly samplingmethod, genetic diversities were conducted and compared amongthe core subsets established according to different grouping prin-ciples or sampling proportion within each group. The rank-numanalysis of six test parameters indicated that grouping principlebased on the growing regions was more suitable than the other twogrouping principles (Table 2). Among the four sampling proportionswithin each group, the L ranked the first (the average rank of all thesix parameters is 1.0), the seconds are the G (1.25) and S (1.25), andthe P (1.33) ranked the last. Based on the method of L, the samplingproportion would be decreased within the large groups with moreaccessions, and increased within the small groups with fewer indi-viduals. Consequently, the genetic repeats were effectively reducedand acquired the suitable core subset.

Furthermore, the interaction between grouping principle andsampling proportion within each group was also determined. Whenthe sampling percentage (10%) and randomly sampling methodwas used, genetic diversities were analyzed and compared amongthe 13 core subsets established according to different groupingprinciples and sampling proportions within each group. Differenceswere identified among different sampling strategies in six testparameters, such as coefficient of variation (CV), index of geneticdiversity (I), variance of phenotype value (VPV), ratio of phenotyperetained (RPR), difference of phenotype minimum (Dmin) and dif-

ference of phenotype maximum (Dmax) (Table 3). The best samplingstrategy was to group samples based on growing regions, combinedwith the logarithmic sampling proportion within each group. Whenusing this strategy, the average rank for all the parameter was 1.0,much lower than other strategies (1.50–1.92). Moreover, the values

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314 Y. Wang et al. / Scientia Horticulturae 128 (2011) 311–319

Table 2Rank-num analyses by 6 test parameters of 13 core subsets constructed by different grouping principles and sampling proportions within group.

Rank-num Average rank

I RPR Dmax Dmin VPV CV

Grouping principles Variety original 2.0 3.0 1.0 2.0 1.0 1.0 1.67Variety utility 1.0 2.0 1.0 1.5 1.0 1.0 1.25Growing region 1.0 1.0 1.0 1.0 1.0 1.0 1.00

Sampling proportionswithin group

P 2.0 1.0 1.0 2.0 1.0 1.0 1.33S 2.0 1.0 1.0 1.5 1.0 1.0 1.25L 1.0 1.0 1.0 1.0 1.0 1.0 1.00G 2.0 1.0 1.0 1.5 1.0 1.0 1.25

Table 3Rank analyses of the test parameters of 13 different sampling strategies.

Sampling schemes Ranks of test parameters

Grouping principle Sampling proportionswithin group

I RPR Dmax Dmin VPV CV Average rank

Variety origin P 2.5 1.0 1.0 1.5 1.5 3.0 1.75S 2.5 1.0 1.0 1.0 1.5 2.0 1.50L 1.5 1.0 1.0 1.0 2.0 2.5 1.50G 2.5 1.0 1.0 1.5 1.5 3.0 1.75

Variety utility P 3.5 1.0 1.0 1.5 1.5 3.0 1.92S 2.5 1.0 1.0 1.0 1.5 2.5 1.58L 2.5 1.0 1.0 1.5 1.5 3.0 1.75G 4.0 1.0 1.0 1.5 1.5 2.5 1.92

Growing region P 2.5 1.0 1.0 2.0 1.5 2.0 1.671.01.01.01.0

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3

3

apwtl2Tag

3

ci

TI

S 3.0L 1.0G 2.5

Non-grouping P 2.0

f I, VPV and CV were all lower in this core subset than those in other2 core subsets, which indicated the presence of a large geneticariance in this core subset. The variance uniformly distributed inach trait, with the least genetic redundancy, compared with otherore subsets established using 12 combining methods. Thus, theampling strategies we selected to establish the apricot primaryore collection were as following: (1) group samples based on theirrowing regions; (2) logarithmic sampling proportion was usedithin each group; and (3) use random sampling method within

ach group.

.3. Estimation of the primary core collection

.3.1. Representation of the primary core collectionTable 4 lists the index of genetic diversity (I), coefficients of vari-

nce (CV) and ratio of phenotype retained (RPR) of 18 traits for therimary core and initial collections. The primary core collectionas able to preserve 100% phenotype of the initial collection, and

he index of genetic diversity was 1.25, the same as the initial col-ection. It is interesting that CV of the primary core collection was3.80, which is remarkably higher than that of the initial collection.hese results indicated that the primary core collection retainedll genetic information of the initial collection and can representenetic diversity of the initial collection.

.3.2. Validation of the primary core collectionFive parameters of six quantitative traits from the primary core

ollection were compared with those same parameters from thenitial collection (Table 5). Overall, a large variation exists among

able 4ndex of I, VPV and RPR for the initial and primary core collections.

Parameters I CV RPR

Initial collection 1.25 21.33 100%Primary core collection 1.25 23.80 100%

1.0 2.0 1.5 2.0 1.751.0 1.0 1.0 1.0 1.001.0 1.5 1.5 1.5 1.501.0 1.5 1.5 3.0 1.67

the 150 apricot accessions at phenotypic level. For the initial col-lection, the range varied from 4.9 to 130.3, and the variance (VPV)from 0.35 to 318.18, and the coefficient of variation (CV) rangedfrom 15.12 to 44.48.

The CV and VPV values of soluble solid content were lower inthe primary core collection than those in initial collection, whereasthe same two parameters of the other five traits in the primarycore collection were all similar or higher than those in the initialcollection, indicating that the primary core collection has retaineda large variation in the initial collection, and can represent thegenetic diversity of the initial apricot germplasm resources verywell.

For the retained ratios, there existed some differences amongthe six traits. The retained ratio of fruit weight was the highest,with a value of 99.69%, fruit height was in the second place with avalue of 97.96%, followed by lateral width (80%) and ripe stage offruit (78.57%), and values of the soluble solid content and ventralwidth was comparably lower, however, both values exceeded 70%.

3.3.3. Practicality of the primary core collectionSix phenotypic traits based on the current breeding objectives

were investigated between initial and the primary core collection.The results indicated that the valuable germplasms including dif-ferent economic traits (e.g. fruit size, fruit quality, earliness, selffertility, disease tolerance, low temperature tolerance, etc.) wereall remained in the primary core collection, that is, the primarycore collection has high practicality. The name of germplasm andtheir characteristics are shown in Table 6.

In summary, these results confirmed that the sampling strategywe selected was well suitable at phenotype level.

3.4. Estimation of genetic diversity for the primary core collectionbased on SSR data

The genetic diversity of primary core collection of apricots wasconducted by using SSR molecular markers. A total of 22 pairs of

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Y. Wang et al. / Scientia Horticulturae 128 (2011) 311–319 315

Table 5Evaluation of primary core collection in apricot.

Characteristics Original germplasm Primary core collection Retained ratio(%)

Max Min Range CV VPV Max Min Range CV VPV

Fruit weight (g) 133.0 2.7 130.3 44.48 318.18 133.0 3.1 129.9 51.82 457.54 99.69Soluble solid content (%) 29.0 6.0 23 20.91 7.50 23.5 7.3 16.2 17.02 4.56 70.44

.01

.40

.35

.42

Sailn8mnl

aasthtvie

TT

Ripe stage(d) 190 50.0 140 15.12 139Fruit height (cm) 7.2 1.7 5.5 15.72 0Lateral width (cm) 6.7 1.0 5.7 15.62 0Ventral width (cm) 6.6 1.7 4.9 16.11 0

SR primers were selected for amplification of high polymorphicnd unambiguous bands. The number of alleles at each SSR locuss listed in Table 7. A total of 196 alleles were identified at 22 SSRoci in 150 accessions from the apricot primary core collection. Theumber of alleles per locus ranged from 5 to 15, with an average of.91 alleles per locus. All of the selected primers were highly poly-orphic and specific. Primer pair ssrPaCITA19 detected the highest

umber of alleles, is the most polymorphic locus among all the SSRoci assayed in this study.

The number of effective alleles (Ne) represents the importance oflleles in the species population. A higher Ne value indicates that thevailability of alleles is more important to the species population. Ashown in Table 7, the Ne values of 150 apricot accessions revealed byhe SSR analysis ranged from 1.88 to 7.86, average 4.43. Observed

eterozygosity (Ho) and expected heterozygosity (He) were usedo estimate the degree of genetic variance of population. The Ho

alue represents the degree of heterozygosity. The high Ho valuendicates the degree of heterozygosity is high. In the 22 loci, thexpected and observed heterozygosity values varied greatly, with

able 6he valuable apricot germplasms and their characteristics.

Major traits Criterion

Fruit Good postharvest characters Postharvest fruit remaithan 7–10 d when leavroom temperature;Or: the firmness withskin > 19 kg/cm2, the firwithout skin14kg/cm2

Large fruit size Average fruit weight ≥

High soluble solids Soluble solids content(SSC) > 16%

High sugar Sugar content > 9.0%

High acid Acid content > 3.0%High Vc Vc content > 19 mg/l00High pectin Pectin content > 0.8%Specific fruit shapeDegenerate stoneVery large kernel Average weight of kern

Fruit developmentperiod (PDF)

Very early ripening PDF ≤ 60 days

Very late ripening PDF > 100 daysSelf fertility High ratio of self-fertility The ratio of self-fertilit

Stresses resistance Very cold resistance Critical lethal low tempis lower than −35.4 ◦C,low to −45.1 ◦C

Very salt resistance Normally growing in thwith 0.3% NaCl

Fruit scab disease resistance High resistance

Absolute immunityBlossom Multi-ply petal The petal is multiple

Green sepal The sepal is greenShoot Drooping

160 50 110 15.02 138.87 78.576.3 1.9 4.4 17.71 0.51 80.005.5 1.5 4.0 20.07 0.57 70.186.6 1.8 4.8 19.32 0.76 97.96

an average of 0.731 and 0.427, respectively. In addition, the averagevalue of polymorphic information content (PIC) was 0.695, rangingfrom 0.44 to 0.86. The average Shannon’s information index was1.627 for all the loci of 150 accessions in the primary core collection.

These results suggest that the 22 SSR primer pairs used in thisstudy were highly polymorphic and specific, and the genetic back-ground of the 150 accessions was more various and complex.

Cluster analysis based on SSR data from 22 pair primers wasalso performed with UPGMA. As shown in Fig. 2, the similaritycoefficients between 150 apricot accessions in the prime core col-lection ranged from 0.25 to 1.0. Moreover, the similarity coefficientsamong almost more than 2/3 accessions was lower than 0.62. Thisindicated that the primary core collection retained a high levelof genetic diversity in DNA level. In addition, the 150 accessions

could be divided into 19 groups when the similarity coefficientwas equal to 0.383. There only one accession in groups XIV, XVI,XVII and XIX, respectively, which implied the corresponding acces-sion was special germplasm. It should be preferentially consideredthese germplasms when to select the core sample in the next step.

Name of germplasm Characteristics

ns moree at

mness

Chuanzhihong, Longken5hao,Shuangrenxing, Xinshuixing,Wanshuxing

110 g Erzhuanzi, Kaitexing,Shuangrenxing631xing, Kezierkumaiti,Keziximixi

16.3%, 22.5%, 21.0%

Anjiana, Keziximixi,Zhongbaixing, Zhoujiaxing,Heiyexing

9.9%, 9.0%, 9.6%, 9.6%, 16.0%

Kangding2hao, Youyixingmei 3.0%, 3.3%g FW Keziximixi 22.1

Yinghong1hao 0.9%Lajiaoxing Similar to capsicum in shapeRuanhexing

el > 0.8 g 80A03xing, Fengren,Longwangmao

0.98 g, 0.89 g, 0.84 g

Luotuohuangxing,Hongfengxing, Maihuangxing,Jintaiyangxing, Xinzhoudashi

55 d, 57 d, 60 d, 60 d, 60 d

Wanshuxing 160 dy > 5% Kaitexing, Manaoxing,

Jintaiyang70%, 67%, 26.8%

eratureevenly

Chuanzhihong,Luotuohuanxing, Longken1hao,Liaomeixing, Ruanhexing

e soil Chuanzhihong, Youyixingmei

80A03xing, Dapiantouxing,Shajinhong1haoHuiyangbaixingLiaomeixing, ShanmeixingLüeshanxingChuizhixing

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316 Y. Wang et al. / Scientia Horticulturae 128 (2011) 311–319

Fig. 2. Dendrogram of 150 apricot accessions based on SSR analysis with 22 pair primers. Abbreviations: LYHX, Longyuanhuangxing; LQWXB, Longquanwuxiangbai;PXDSX, Panxiandashaxing; KRHKW, Kurenhuangkouwai; YSHX, Yangshaohuangxing; BAHDHX, Beianhedahuangxing; BTYCL, Baotianyichuanling; CLBGX, Changlibaiguoxing;SGHG1, Shiguanhongguang1hao; SGZH1, Shiguanzaohong1hao; CBSX, Chongbanshanxing; TRHKW, Tianrenhuangkouwai.

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Y. Wang et al. / Scientia Horticulturae 128 (2011) 311–319 317

Table 7Summary of genetic variations for 22 SSR loci in 150 apricot accessions.

SSR locus Na Ne Ho He PIC I

Pchgms4 7 2.64 0.533 0.624 0.549 1.1815UDP97-402 7 3.82 0.260 0.741 0.700 1.5452UDP98-406 11 7.31 0.200 0.866 0.848 2.1317UDP98-409 9 4.93 0.433 0.800 0.768 1.7676UDP98-411 11 5.94 0.600 0.834 0.810 1.9474UDP98-412 9 6.51 0.473 0.849 0.829 2.0068Pchcms4 6 1.88 0.447 0.470 0.440 0.9626Pchgms10 6 2.27 0.560 0.562 0.480 0.9980BPPCT002 8 5.63 0.587 0.825 0.797 1.7904BPPCT001 6 2.58 0.503 0.612 0.567 1.2114BPPCT028 5 2.53 0.433 0.606 0.525 1.0946BPPCT029 10 4.94 0.399 0.800 0.776 1.8970BPPCT030 5 2.02 0.493 0.512 0.476 1.0058UDP96-005 9 3.23 0.593 0.694 0.648 1.4387UDP97-401 10 3.11 0.300 0.680 0.638 1.4257P04 11 3.34 0.260 0.706 0.684 1.7031ssrPaCITA15 10 5.30 0.267 0.814 0.787 1.8892ssrPaCITA19 15 7.86 0.333 0.876 0.860 2.3043AMPA095 13 3.24 0.320 0.694 0.674 1.7112

0.4870.3600.5440.427

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AMPA105 11 7.09AMPA109 8 5.16AMPA112 9 6.05Average 8.91 4.43

. Discussion

.1. The size of core collection

The core collection should represent maximum genetic diversityf the whole resource with minimum repetitiveness; therefore, aeasonable sampling percentage was vital for achieving this goal.rown (1989) suggested that about 5–10% sample size of thentire collection with an upper limit of 3000 per species wouldffectively retain about 70% of the alleles of the entire collection.owever, Yoneazwa et al. (1995) suggested the optimal proportionas 20–30%. Li et al. (2003) reported that the sampling propor-

ion should vary according to the size of the initial accessions. Theroportion should be increased when the number of initial collec-ion is small, and reduced when the initial one is big. The samplingercentage of core collections generally accounted for 10–30% ofhe entire resource in several crop species, and the representationvaluation indicated that the genetic diversity of the core collectionepresented the entire germplasm (Gao et al., 2005; Bhattacharjeet al., 2007; Yao et al., 2008; Escribano et al., 2009). In this study,hree sampling percentages from the whole collection were used:%, 10% and 15%. The ratios of phenotype retained (RPR%) for thehree percentages were all higher than 96%, highly exceeding the0% predicted by Brown (1989). Especially, 10% sample size of thentire apricot collection retained 98.84% of the phenotypes in theamples. Moreover, the index of genetic diversity and variancef phenotype value were also much higher than other two sizes5% and 15%) (Table 1), suggesting that the primary core collec-ion preserved a much higher level of genetic variation, with lessenetic redundancy. Therefore, 10% was selected as the best sam-ling percentage from the whole collection when constructing theore collection.

.2. Sampling scheme

For whole collections of small sizes, the effective methods foronstructing core collections are stepwise clustering according to

enetic distance (Hu et al., 2000; Zewdie et al., 2004; Jansen and vanintum, 2007; Zhang et al., 2009a). Step cluster sampling methodsould not be used in this study due to the huge size of the col-ection (1501 accessions). The three-grade sampling method wasmployed (Fig. 1) as described in several previous studies (van

0.862 0.843 2.09150.810 0.780 1.76020.838 0.814 1.93060.731 0.695 1.6270

Hintum et al., 1995; Li et al., 2002; Li et al., 2007). The optimiza-tion of sampling scheme was executed on the decision of groupingprinciple and sampling proportion within each group. The optimalgrouping principle could be determined through general com-paring six test parameters, however, it could not distinguish thedifferent sampling proportions within each group (Table 3). Differ-ent grouping principles had different sampling effects among thefour methods of sampling proportion within each group. For exam-ple, the preferred scheme of sampling proportion within each groupwas based on square root (S) when the grouping principle based oncultivar utility was used, whereas the better scheme is based onlogarithm (L) when the grouping principle was based on growingregions. Hence, it is necessary to select the optimal grouping prin-ciple when determining the best scheme of sampling proportionwithin each group. This strategy in developing core collection hasbeen successfully used in other crops such as rice (Li et al., 2002)and peach (Li et al., 2007).

At present, there are no uniform classification criteria in apri-cot accessions of China. Three classification methods have beenproposed, based on growing region, cultivar origin and utility,respectively (Zhang and Zhang, 2003). When growing region wasuses as a classification criterion, there are five different geo-graphical regions with various nature conditions, various cultivarresources and difference utilization: (1) Northeast China frigid area,(2) North China warm area, (3) Northwest China arid areas, (4) trop-ical and subtropical area, and (5) Southwest China plateau region.In addition, some apricot cultivars were separately classified intoa special group, which has a higher level of adaptation and widergrowing regions. Subsequently, the screening of the best group-ing principles was performed by analyzing the genetic diversity atthe level of phenotype based on the data of fruit characters. Ourresults from this study have shown that the best sampling schemewas based on growing region, compared with other grouping prin-ciples (Tables 2 and 3). When the grouping principle was basedon the growing region, there were a lot of differences in the totalnumber of accessions among different groups. Whereas, the loga-rithm method tends to balance the number of accessions selected

within groups, and species with a small number of accessions werealso represented by a relatively large number of accessions. Con-sequently, the genetic redundancy was effectively reduced andthe suitable core subsets were generated. Moreover, the data ofmorphological information were relative more adequate. Based on

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18 Y. Wang et al. / Scientia Ho

hese observations, the optimal sampling strategies to develop thepricot primary core collection was selected as following: group-ng principle based on growing region, sampling proportion withinach group based on logarithm, and sampling method within eachroup based on random. The primary core collection, establishedccording to this approach and with the 10% sample size of thentire collection, had perfect representative, capturing high levelf diversity and variance of the initial germplasm resources.

.3. Assessment of the apricot primary core collection

The representation evaluation is a significant step in the pro-edure of core collection construction. The evaluation of geneticiversity, that is, whether the primary core collection could rep-esent the genetic diversity of the initial germplasm resources,s critical for the assessment. Genetic diversity represents all theenetic variance from different individuals within one species orne group. The evaluation of genetic diversity included the rich-ess and evenness of genetic variance. The parameters for geneticiversity evaluation of the core collection were previously estab-

ished (Brown, 1989; Li et al., 2002; Wang et al., 2007), includingPV, CV, range of variance, mean and standard deviation (Li et al.,002; Holbrook and Dong, 2005; Li et al., 2007). Diwan et al. (1995)roposed that the core collections were considered to be represen-ative of the initial subsets based on the following principles: (1)0% or fewer of means and variance ranges of the core collectionere significantly different (˛ = 0.05) from the initial subset; and (2)

he percentage of the variance range retained by the core collectionrange ratio) was at least 70% of the range of the initial subset. Inhis study, we selected I, RPR, Range, VPV, CV as the test parameters.or the primary core collection with 150 apricot accessions, theetained ratios of all six traits were ranged from 70.18% to 99.69%Table 5), preserving most of the variance in the initial collection.he coefficient of variation was significantly higher than that in ini-ial collection, indicating that the primary core collection retainedhigh level of genetic variance both in richness and evenness.

.4. Genetic diversity of the apricot primary core collection

Analyzing the genetic composition at molecular level usingolecular marker techniques has become one of the best effective

ools to test the genetic diversity of core collection. Among vari-us DNA markers, the simple sequence repeats (SSRs; also referredo as microsatellites) markers are well known for their potentiallyich information content and versatility as molecular tools. They arelso amenable to high-throughput genotyping and have proven toe highly versatile and useful markers for genetic analysis includ-

ng resource evaluation, identification of cultivar, gene anchor inhe chromosome, and genome mapping in many fruit crop speciesZhebentyayeva et al., 2003; Sánchez-Pérez et al., 2005; Gökirmakt al., 2009). However, most of the previous studies on evaluationf the representative of core collections only used morphologicalescriptors and few agronomic traits, and very few had molecu-

ar data. In this study, the analysis of the genetic diversity of 150ccessions in the primary core collection of apricots was evaluatedsing SSR technique, and the results revealed rich genetic diver-ity in the apricot primary core collection (Table 7). The numberf alleles per locus ranged from 5 to 15, with an average of 8.91lleles per locus, which was higher than previously reported (2–7)Vilanova et al., 2006) or in China apricot cultivars (3–11) (Pedryct al., 2009). In the 22 SSR loci, the expected heterozygosity values

aried greatly, ranged from 0.470 to 0.876 (average 0.731), whichas wider than the range 0.5949–0.8487 reported by Maghuly et al.

2005), also wider than the range 0.4607 to 0.8339 (Pedryc et al.,009) in apricot. In addition, the average value of polymorphic

nformation content (PIC) was 0.695, ranged from 0.44 to 0.86. The

urae 128 (2011) 311–319

average Shannon’s information index was 1.627 for all the loci of150 accessions in the primary core collection. This level of poly-morphism observed in apricot using SSR markers in this study washigher than that both in wild apricot resources of West China (Heet al., 2007) and in apricot germplasms in southern Xinjiang (Yuanet al., 2007). These results further offered the molecular proof thatthe sampling strategy was suitable and the primary core collectionwas well representative of the initial genetic resources of apricots.

Acknowledgements

This research was financially supported by Municipal NaturalScience Foundation of Beijing (No. 6081001), Special Fund for Agro-scientific Research in the Public Interest (No. 201003058) and theSpecial Project from National Forestry Bureau (No. 200904032). Weare grateful to Prof. Jia Ji-zeng (Chinese Academy of AgriculturalSciences, China) and assistant Prof. Zhang Hong-liang (China Agri-cultural University, China) for their valuable advice to this study,and to Dr. Li Cheng-xia (Department of Plant Sciences, Universityof California, Davis, CA 95616, USA) for revising the English.

Appendix A. Supplementary data

Supplementary data associated with this article can be found, inthe online version, at doi:10.1016/j.scienta.2011.01.025.

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