Establishment of a core collection to optimise the conservation of
cherimoya (Annona cherimola Mill.) genetic resources using SSR
information
P. Escribano, M.A. Viruel, J.I. Hormaza
Estación Experimental La Mayora – CSIC, 29750 Algarrobo-Costa, Málaga, Spain
Keywords: ex situ conservation, germplasm management, plant genetic resources
Abstract
The management and evaluation of large germplasm collections is expensive
and inefficient due to redundancies and/or duplications and the impossibility of
analyzing with detail all the accessions conserved, particularly in fruit tree species.
Thus, collection management can be significantly improved if the regeneration,
characterization and evaluation steps are focused on a subset of individuals,
denominated ‘core collection’, that represent the diversity conserved in the whole
germplasm collection. Although molecular markers are becoming the tool of choice
for the development of core collections in plants, the examples of their use to develop
core collections in fruit species are very scarce. In this work, we used SSR marker
data to develop a core collection in an underutilized subtropical fruit tree species,
cherimoya (Annona cherimola Mill.), from an initial collection of 279 genotypes from
different countries. We compared six alternative allocation methods to construct the
core collection. The best subset was obtained with 40 accessions. In this subset, all
the SSR alleles present in the whole collection were recovered and no significant
differences in frequency distribution of alleles for any of the loci studied or in
variability parameters were recorded between the core and the whole collection.
INTRODUCTION
The management and evaluation of large germplasm collections is expensive and
inefficient due to the redundancies and/or duplications in the collections and the
impossibility of analyzing with detail all the accessions conserved (Grenier et al., 2000a).
In those cases it is interesting to select a subset of individuals that represent the whole
germplasm collection. Thus, the effort of regeneration, characterization and evaluation
can be carried out in this subset. Frankel and Brown (1984) defined a core collection as a
subset of a larger germplasm collection that maximizes the possible genetic diversity of a
crop species with minimum redundancy.
In the last years, molecular markers are becoming the tool of choice for the
development of core collections. However, although core collections in fruit species could
greatly improve germplasm management, the examples of the use of molecular markers to
develop core collections in these species are scarce.
In this work, we used SSR marker data to develop a core collection in an
underutilized subtropical fruit tree species, cherimoya (Annona cherimola Mill.,
Annonaceae), from an initial collection 279 genotypes from different countries. We
compared six alternative allocation methods to construct the core collection, four not
based in the similarity dendrogram and two based on dendrogram data.
MATERIAL AND METHODS
Data set
The data used in this study were 279 accessions of cherimoya from different
geographical origins maintained at the E.E. La Mayora–CSIC in Malaga (Spain). Those
accessions have been genetically analyzed with 16 polymorphic SSRs (Escribano et al.,
2007).
Construction of the core collection
Six alternative approaches to develop core collections were compared. For each
approach, five core subsets with different sizes, ranging from 10 to 50 at ten-individual
intervals were developed.
a. Random sampling (R). The accessions were selected from the whole collection by
random sampling without replacement.
b. Maximization strategy (M-strategy) (M) (Schoen y Brown 1995). It was carried
out using the MSTRAT software (Gouesnard et al., 2001). This strategy
maximizes the number of alleles in each locus using as second maximization
approach the Nei's diversity index (Nei, 1987).
c. Simulated annealing algorithm using the Core Set function in PowerMarker (Liu
and Muse, 2005). In this case two optimal core subsets were obtained: one
maximizing genetic diversity (SD) and the other maximizing the number of SSR
alleles (SA).
d. Logarithmic strategy (L), Brown (1989). Six main groups were defined according
to Escribano et al. (2007) (cluster A to cluster E).
e. Stepwise clustering with random sampling (S) according to Hu et al. (2000) also
based in the dendrogram obtained by Escribano et al. (2007)
Characterization of the subsets and comparison to the entire collection
The different subsets obtained were compared by molecular diversity measures,
such as number (A) and frequency (Fr) of alleles, and observed (Ho) and expected (He)
heterozygosities calculated with ARLEQUIN version 3.01 (Excoffier et al., 2005).
The frequency of alleles at loci level between the entire collection and the core
collections was analyzed by the Chi-square test. The rest of parameters related to
collection diversity (A, He and Ho) were compared by the Friedman’s Repeated Measures
Analysis of Variance on Ranks. Post-hoc Dunnett's test was used to compare the different
subsets developed with the whole collection (control group). All the comparisons at
significance level p<0.05, were carried out with SigmaStat 3.0 (SPSS Science Version,
Chicago, IL, USA).
RESULTS AND DISCUSSION
Significant differences (p<0.05) were obtained in the number of alleles compared
to the whole collection in some subset sizes of all the methods except for the M-strategy.
Observed heterozygosities were similar to those of the whole collection in all the subsets
except with the subset of 10 accessions with the M-strategy, the subset of 20 accessions
with the random strategy and with all the subsets developed following the L- strategy
where significant (p<0.05) differences were obtained. Regarding expected heterozygosity
(analogous to Nei’s genetic diversity index) significant (p<0.05) differences were only
recorded for the M-strategy with 10 individuals.
Regarding the allele frequencies, only with the subset of 50 accessions of the
random strategy, the subsets of 40 and 50 accessions of the M-strategy, the subset of 50
accessions with the SD strategy and the subsets of 47 and 76 accessions with the S
strategy no significant differences in at least 95% of the loci was obtained.
All the strategies seem to represent fairly well the overall diversity of the
collection (He). However the M-strategy is the only approach that recovers all the alleles
of the whole collection with as low as 30 accessions, reducing redundancy and capturing
most of the genetic diversity.
The whole germplasm collection shows a high redundancy since with only 30
cultivars all the alleles could be retained with the M-strategy, but with that subset 6% of
the loci showed significantly different allele frequencies compared to the whole collection.
Therefore, although some redundancy is present, the subset that showed the best
adjustment to all the validation parameters was that of 40 accessions with the M-strategy
(Table 1). In this subset, all the alleles were recovered and no significant differences were
recorded for any of the other variability parameters studied (He and Ho). This subset
represents 14% of the original collection. All the countries in the whole collection are
represented in the selected core collection, except Italy and Chile. The UPGMA
dendrogram obtained after the similarity analysis with the accessions that constitute the
core collection is shown in Fig. 1. Similarly to the results obtained with the whole
collection (Escribano et al., 2007) no clear geographic pattern was observed.
ACKNOWLEDGEMENTS
Financial support for this work was provided by the Spanish Ministry of
Education (Project Grant AGL2004-02290/AGR) and the European Union under the
INCO-DEV program (Contract 015100).
Literature Cited
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Table 1. Origin of cultivars selected to form the best subset .
Cultivars
Anderson
Andrews
CortesII
El Bumbo
Equador
Fino de Jete
Haluza
Loma
Mariella
Mexico1
Pierce
Sabor
SB109
SB124
SC10
SE11
SE14
SE29
SM29
SM32
Country
Cultivars
SP6
SP10
SP26
SP36
SP41
SP46
SP52
SP55
SP65
SP76
SP7752
SP79
SP86
SP95
SP129
SP131
SP137
SP138
SP205
Zarzero
Australia
Australia
Mexico
Australia
Australia
Spain
Australia
USA
Australia
Mexico
USA
Australia
Bolivia
Bolivia
Colombia
Ecuador
Ecuador
Ecuador
Portugal
Portugal
Country
Peru
Peru
Peru
Peru
Peru
Peru
Peru
Peru
Peru
Peru
Peru
Peru
Peru
Peru
Peru
Peru
Peru
Peru
Peru
Costa Rica
ANDERSON
ANDREWS
MARIELLA
SC10
FINO DE JETE
CORTES II
EQUADOR
SP129
MEXICO 1
PIERCE
SB109
SB124
SP7752
SP65
SP131
SM29
SP26
SE11
SM32
SP6
SP76
SP138
SP137
SP10
SP86
SP36
SP55
SP52
SE14
SE29
SP46
SP95
SP41
SP205
EL BUMBO
HALUZA
ZARZERO
SP79
LOMA
SABOR
FinodeJeteMW
0.40
0.55
0.70
0.85
1.00
Similarity
Fig. 1. Dendrogram with the 40 selected accessions of the best cherimoya core subset using
UPGMA of similarity data.