Core collection construction
Because of differences in genetic and morphological diversity, core collections were sampled using both genetic and morphological diversity. Based on 34 polymorphic morphological characters and 204 polymorphic molecular markers, maximization strategy algorithm was used to construct Tunisian apricot germplasm core collection using the Mstrat software as described by Gouesnard et al. (2001).
The construction of a core collection allowed the selection of several core sizes from the global diversity (110 apricot accessions) according to the Mstrat strategy of selection. It could be selected using the visualization of optimum and random means for active and target variables (active variables are those called Markers, ‘Target’ variable means that Mstrat will compute the score realized on these variables using active variables). For that reason, Random (R) and Maximization (M) strategies were compared. Thus, three different core collections were elaborated:, the first one is based on the 34 morphological characters, the second one includes the 204 AFLP polymorphic molecular markers and the third one represents the combined morphological-AFLP data. Eight cores of the selected size were constructed and compared. The same procedure has been considered for the three cores construction.
As for the computation of the redundancy for active and target variables, the results showed that the opti mization strategy allow us to reach rapidly the optimal size of the core which corresponds to the beginning of the plateau of the curve. Similar results were reported by Gouesnard et al. (2001) which indicates that the inflection point of the M curve provides the optimal size for a core collection.
Again, the combined morphological-AFLP data core, the plotting of all optimum (Maximization method) and random values related to mean values (Fig. 2) and all points values (Fig. 3) for active and target variables showed that the plateau was reached more rapidly with the M method than with the R one for active variables (Fig. 2a). The results showed that the ideal size of the core collection obtained at the plateau of the OPT curve is around 20 individuals.
Figure 2a–b. Plotting of optimization (OPT) (Maximization method) and randomization (RAND) (random method) related to the mean values for active variables (a) and target variables (b).
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Figure 3a–b. Plotting of optimization (OPT) (Maximization method) and randomization (RAND) (random method) related to the values for all points relatively to active variables (a) and target variables (b).
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The core collection representative of the global genetic diversity of the studied sample was selected after several core collection constructions using Mstrat software. The accessions representing the core were selected in relation with their high frequency of sampling in the different core collections.
Results permitted the construction of cores allowing the choice of the 23 accessions that are highly iterated. They correspond to:
– Bargougs: B40A, B40K, B40L, B40M, B46D,
– Cultivars: ‘Chechi Khit El OuedV10A’, ‘Bouk HmedV13B’, ‘Chechi Dhraa TammarV9’, ‘Chechi HorrV29’, ‘Amor El EuchV5A’, ‘Oud AouichaV71’, ‘Oud TijaniV22B’, ‘Oud GnaaV27’, ‘BanguiV31’, ‘Bouk Hmed AkhalV32B’, ‘Khad HlimaV2A’, ‘BaccourV41C’, ‘Bedri AhmarV19A’, ‘NajjarV4A’, ‘Oud Salah Ben SalemV25B’, ‘Amor El EuchV51C’, ‘Oud HmidaV21D’, ‘Oud NakhlaV23A’.
This core collection needs to be completed with 11 other accessions representing specific molecular markers or rare modalities of morphological characters corresponding to:
– Bargougs: B44C, B44D, B46B, B46E,
– Cultivars: ‘Oud TijaniV22A’, ‘JerbaV66’, ‘Variete de MahdiaV47’, ‘Chechi BazzaV28D’, ‘ChechiV68’, ‘AranjiV17C’, ‘BayoudhiV11A’.
As a result, the apricot core collection was represented by a total of 34 accessions.
If considering the core collection based exclusively on the morphological traits, the plotting of all optimum (Maximization method) and random values related to mean values showed that the plateau was reached more rapidly with maximizing strategy method than with random one for active variables (Fig. 4a) and that the ideal size of the core collection is about 10 accessions corresponding to:
Figure 4a–b. Plotting of optimization (OPT) (Maximization method) and randomization (RAND) (random method) related to the mean values relatively to active variables related to the morphological data (a) and the AFLP data (b).
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– Bargougs: B40H, B40K, B40M, B42C,
– Cultivars: “Amor El EuchV5A”, “Oud AouichaV71”, “Chechi HorrV29”, “Oud GnaaV27”, “Bayoudhi V11A”, “Amor El Euch V51C”.
Two accessions, “JerbaV66” and “AranjiV17C”, with specific modalities for the variables LPL and LPACLS, need to be added to this core increasing the total number of the accessions to 12.
For the core collection based on the AFLP molecular markers, the plotting of all optimum (Maximization method) and random values related to mean values revealed also that the plateau was reached more rapidly with maximizing strategy method than with random one for active variables (Fig. 4b). This allowed the identification of the ideal size of the core collection corresponding to 18 individuals listed as follows:
– Bargougs: B40G, B40J, B45B,
– Cultivars: “Amor El EuchV5A’, “Chechi Khit El OuedV10A”, “BayoudhiV11A”, “Bouk HmedV13B”, “AranjiV17A”, “Bedri V1G”, “Bedri AhmarV19A”, “Oud TijaniV22B”, ‘Chechi Dhraa TammarV9”, “Khad HlimaV2A”, “BaccourV41C”, “Amor El Euch V51C”, “Bouk Hmed AkhalV32B”, “NajjarV4A”, “Bangui V31”.
The additive list of accessions representing rare markers is composed by nine accessions:
– Bargougs: B44C, B44D, B46B, B46D, B46E,
– Cultivars: “Oud TijaniV22A”, “Chechi BazzaV28D”, “AranjiV17C”, “Variete de MahdiaV47”.
Consequently, this final core size reached 27 accessions.
Core collection validation and comparison
Richness of a collection of accessions for such a qualitative variable was defined as the number of classes represented among the accessions (Gouesnard et al. 2001).
Comparison between morphological characters variability observed for the entire collection (110 accessions) and the morphological variability of each of the selected core collection is shown in Table 3.
Table 3. Comparison of the morphological characters variability between the entire collection and the different core size collections (interval of variance for quantitative variables, observed modalities for the qualitative variables).
|Morphological variables||Global collection (110 accessions)||Morphological core collection (12 accessions)||Combined data core collection (34 accessions)|
|FW||2.71 to 54.31||5.75 to 54.31 (94%)||3.45 to 54.31 (98.6%)|
|FSW||0.70 to 4.6||1.13 to 3.23 (54%)||0.75 to 3.88 (80%)|
|LBL||4.28 to 9.33||5.51 to 8.12 (52%)||5.21 to 9.01 (75%)|
|LPL||1.69 to 4.78||1.93 to 4.01 (67%)||2.19 to 4.42 (72%)|
|FLW/FVW||0.99 to 1.38||1.05 to 1.38 (85%)||0.99 to 1.38 (100%)|
|FH/FVW||0.92 to 1.73||0.92 to 1.73 (100%)||0.92 to 1.73 (100%)|
|LBL/LBW||0.86 to 1.41||0.86 to 1.21 (64%)||0.86 to 1.25 (71%)|
|LPL/LBL||0.30 to 0.63||0.32 to 0.59 (82%)||0.30 to 0.59 (88%)|
|TV||1,2,3||1,2,3 (100%)||1,2,3 (100%)|
|TDFB||1,2,3||2 (33%)||1,2,3 (100%)|
|TLN||1,2,3||1,2,3 (100%)||1,2,3 (100%)|
|LBUM||1,2,3||1,2,3 (100%)||1,2,3 (100%)|
|LPACUS||1,2,3||1,2,3 (100%)||1,2,3 (100%)|
|LPACLS||1,2,3,4,5||1,2,3,4,5 (100%)||1,2,3,4,5 (100%)|
|FDS||1,2,3||1,2,3 (100%)||1,2,3 (100%)|
|FDPC||1,2,3||1,2,3 (100%)||1,2,3 (100%)|
|FROC||1,2,3,4||1,2,3,4 (100%)||1,2,3,4 (100%)|
|FFF||1,2,3||1,2,3 (100%)||1,2,3 (100%)|
|FASF||1,2,3,4||1,2,3,4 (100%)||1,2,3,4 (100%)|
|FKB||1,2,3,4||2,3,4 (75%)||1,2,3,4 (100%)|
|LIGCLS||1,2,3||1,2,3 (100%)||1,2,3 (100%)|
|TGH||2,3,4,5||2,4,5 (75%)||2,3,4,5 (100%)|
|LBSB||1,2,3,4||1,2,3,4 (100%)||1,2,3,4 (100%)|
|LBST||1,2,3||1,2,3 (100%)||1,2,3 (100%)|
|FSLV||1,2,3,4||1,2,3,4 (100%)||1,2,3,4 (100%)|
|FSVV||1,2,3||1,2,3 (100%)||1,2,3 (100%)|
|LBAT||1,2,3||2,3 (67%)||1,2,3 (100%)|
|LBIM||1,2,3,4||1,3,4 (75%)||1,2,3,4 (100%)|
|FAS||1,2,3,4||1,2,3,4 (100%)||1,2,3,4 (100%)|
|FS||1,2||1,2 (100%)||1,2 (100%)|
|FFS||1,2||1 (50%)||1,2 (100%)|
|FGC||1,2,3,4,5||1,2,3,4,5 (100%)||1,2,3,4,5 (100%)|
|FFC||1,2,3,4,5||1,2,3,4,5 (100%)||1,2,3,4,5 (100%)|
|FSS||1,2,3||1,2,3 (100%)||1,2,3 (100%)|
The intervals of variance were compared for quantitative variables and modalities observed were compared for the qualitative variables. Results showed that for the quantitative variables FW, FSW, LBL, LPL, LBL/LBW and LPL/LBL, the variability of the combined data core collection (34 accessions) corresponds respectively to 98%, 80%, 75%, 72%, 71% and 88% of the variability of the 110 accessions. For qualitative characters, 100% of the variability of the 110 accessions was represented by the 34 accessions of the core collection (Table 3).
Among the 204 polymorphic AFLP markers, only seven markers were not represented by the combined data core collection, thus, the 34 accessions covers 97% of the genetic variability of the 110 accessions.
Differences between the morphological variability, the molecular diversity of the entire collection (110 accessions) and the subset of the core collection (34 accessions) were found to be non-significant for all the morphological and molecular markers recorded indicating that the core of 34 accessions is well representative of the global diversity.
The combination of the morphological variability and the molecular diversity shows that the core of 34 accessions represents from 70 to 100% of the existing variability (110 accessions). Accordingly, the combination of morphological and molecular markers is an efficient tool for characterizing the apricot core collection and will be valid to distinguish other accessions which can be introduced into the collection with more than 70% of the entire collection diversity.
The elaborated core collection by the morphological characters showed that all the modalities of the qualitative variables are represented by the core set of 11 accessions at a level of 100% except for the characters TDFB (33%), FKB (75%), TGH (75%), LBAT (67%), LBIM (75%), FFS (50%); while for the quantitative traits; the representativeness is about 94%, 54%, 52%, 67%, 85%, 64%, 82% for FW, FSW, LBL, LPL, FLW/FVW, LBL/LBW and LPL/LBL, respectively (Table 3). Noteworthy that non-significant difference was observed for the qualitative characters even when values are less than 70% of the global variability. The set of 12 accessions is less representative of the global variability if we refer to these percentages of the interval of variance of each trait.
When considering the AFLP core collection, we conclude that the 27 accessions enclosed 96% of the global genetic diversity and that only 9 markers were not represented by the core set. This difference is not significant showing that the core set of 27 accessions is an accurate representation of the molecular diversity of the 110 accessions.
The comparison of the three core collections showed that the core issued from the combination of the two cores from morphological and AFLP markers is the most efficient.
On the other hand, sinceWard's minimum variance hierarchical clustering dendrogram was considered by Xiurong et al. (2000) as the most suitable for core selection when constructed with the same data base, we constructed a Ward's dedrogram. This resulted in the selection of a representative collection with 39 accessions among the 110 studied. Comparison of the two cores selected on the basis of morphological and molecular markers, using the Mstrat and Ward's methods shows a strong link between the two selected cores with almost 60% of similarity (results not shown).