A germplasm stratification of taro (Colocasia esculenta) based on agro-morphological descriptors, validation by AFLP markers

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1 Euphytica 137: , C 2004 Kluwer Academic Publishers. Printed in the Netherlands. 387 A germplasm stratification of taro (Colocasia esculenta) based on agro-morphological descriptors, validation by AFLP markers J. Quero-Garcia 1, J.L. Noyer 1,,X.Perrier 1, J.L. Marchand 1 &V.Lebot 2 1 CIRAD, TA 70/16, Montpellier Cedex 5, France; 2 CIRAD, PO Box 946, Port-Vila, Vanuatu; ( author for correspondence; jean-louis.noyer@cirad.fr) Received 22 November 2002; accepted 4 May 2004 Key words: AFLP, Colocasia esculenta, core samples, genetic variation, germplasm stratification Summary This paper presents a simple and practical method for stratifying taro germplasm based on morpho-agronomical characters. More than 450 accessions of taro collected throughout Vanuatu and established in a field collection were described using 19 descriptors. A hierarchical approach was used to stratify the agro-morphological variation. Three sampling strategies were tested and the variation captured within each sample was compared for the frequencies of characters. The first sample (S1) was randomly selected; the second sample (S2) was conducted within the subgroups produced by the stratification method, and the third sample (S3) was based on UPGMA clustering within each subgroup. AFLP markers were used to compare the diversity between S3 and a fourth sample (S4) that included the parents of the Vanuatu breeding programme, and more diversity was found in S3. AFLPs were found to be useful to validate the hierarchical approach used for stratification. These studies have confirmed the narrow genetic base of the Vanuatu taro germplasm. They have been useful for detecting duplicates and fingerprinting of accessions. Abbreviations: TANSAO: Taro Network for Southeast Asia and Oceania Introduction Taro (Colocasia esculenta (L.) Schott) ranks fifth among root crops, after potato, cassava, sweet potato and yams ( It is a highly polymorphic species, cultivated from the equator to Japan (45 N), and more than 10,000 landraces exist (Ivancic & Lebot, 2000). There is no international germplasm collection and all the countries maintain for breeding purposes national field collections of a variable size. Taro is an annual crop propagated asexually. Therefore, germplasm maintenance is expensive and risky. Numerous attempts to classify cultivars have failed to provide breeders and curators with a practical approach for identifying potential parents for breeding programmes (Ivancic & Lebot, 2000). The distinction between eddoe types producing small corms, and dasheen types with a large edible main corm, is practical as it refers to two distinct marketable products. Recently, national germplasm collections have been established in Vietnam, Thailand, Malaysia, Indonesia, Philippines and Papua New Guinea, as part of the Taro Network for Southeast Asia and Oceania (TANSAO). Their collections have been fully characterized using internationally standardized morpho-agronomic descriptors and databases have been produced and exchanged. They provide an accurate picture of the extent of variation existing within this geographical region (Lebot et al., 2002). Breeders face the difficult choice of selecting the right parents in the absence of an accurate assessment of the genetic distances existing between local varieties. Because they have been propagated asexually for thousands of years, morphotypes can be quite distinct as a result of fixing somatic mutations (Kuruvilla & Singh, 1981), even when they share the same genetic background.

2 388 Using molecular markers for taro germplasm characterization are expensive and time consuming if large numbers of accessions have to be analyzed. Isozyme studies (Lebot & Aradhya, 1991) revealed the existence of two germplasm pools, one in southeast Asia and the second in Melanesia, indicating the possibility of two independent domestication processes. More powerful genetic markers have been used only recently. A subset of 44 accessions of diverse origins was analyzed with RAPD markers but gave no clear geographical or morphological structure (Irwin et al., 1998). Microsatellite and SSR markers were tested on 17 accessions from several Pacific countries (Mace & Godwin, 2002). They proved to be a valuable tool for the identification of duplicates although the geographical structure produced was not very informative, probably due to the size of the sample and the low number of primers used. Finally, AFLP markers have been used to study the diversity of a core sample including 290 accessions from seven different countries (Kreike et al., 2003). Most accessions could be clearly differentiated by using three primer pairs and few duplicates were found. Differentiation between Southeast Asian and Melanesian taros was obtained confirming the isozyme results. The aim of the present study was to evaluate a methodology that can be used by breeders to stratify their genetic resources into useful subsets. Two different approaches were compared, using morphoagronomic characterization and AFLP analyses. Similarities and variation between the two are discussed. The national taro collection of Vanuatu was used for the study. Vanuatu is an archipelago of some 80 widely dispersed islands, 800 km southeast of Solomon Islands and about the same distance west of Fiji. The total land area is 12,189 km 2 with eight largest islands contributing 87% of this. In terms of providing everyday food, taro is among the most important staple crops in Vanuatu. The total area devoted to this crop is about 4000 ha. It grows best where annual rainfall is well distributed and totals 2500 mm or more. The main islands of taro cultivation are situated in the north, Santo, Pentecost, Maewo, Ambae, Vanua Lava and in the south, Tanna and Aneityum (Figure 1). Materials and methods Agro-morphological analyses The Vanuatu taro national germplasm collection was morphologically described using a standardized TANSAO protocol during 2 years (in 1998 and 1999). Figure 1. Map of Vanuatu. Names of the largest islands are indicated. While collecting, passport data was recorded. Seventeen qualitative characters with a variable number of states (from 2 to 9) and two quantitative descriptors, corm weight and dry matter content, both transformed into several phenotypic classes, were used. Stratification of collections was based on ideotypes as defined by breeders following a hierarchical approach in which passport, characterization and evaluation data are used. Four major characters were used: stolons (presence or absence), corm shape (branched or unbranched), adaptation (rainfed or flooded) and maturity (early or late). The Vanuatu collection was used to test the stratification methodology. Data analyses were conducted on three samples assembled using distinct sampling strategies that could be applied to larger databases. The first sample (S1) was randomly selected out of the 452 accessions of the national collection. Second, within the subgroups produced by the hierarchical stratification method, two other samples were assembled: S2, in which accessions were selected randomly within each subgroup and S3, based on UPGMA clustering of the individuals belonging to each subgroup. The homogeneity of the frequency distributions between the

3 389 collection and the three samples was analyzed by Chisquare tests (cf. Ortiz et al., 1998). FAC(Factorial analyses of correspondences) were performed using the ADDAD software (Lebeaux et al., 1985) and clustering analyses using the NTSYS-PC software, version 1.8 (Rohlf, 1993). Winboot (Yap & Nelson, 1996) was used for the calculation of the bootstrap values. Bootstrap values represent a measure of the stability of the produced dendrogram. By sampling numerous sets of markers randomly selected out of the whole dataset, several trees are compared (Felsenstein, 1985). The dissimilarity index used was the Dice index (2a/(2a + b + c)) and the clustering method was the UPGMA (unweighted pair group method using average). This method first described by Sneath and Sokal (1973) uses the concept of minimal dissimilarity between two neighboring points and an ultrametric distance. AFLP analyses Plant material Two samples were analyzed. The S3 sample, produced after using UPGMA with subsequent sampling within the clusters, contained 51 accessions representing the morpho-agronomic variability existing within the Vanuatu collection. The sample S4 included the parents of the largest progenies of the Vanuatu breeding programme and contained 42 accessions. From S3, 37 accessions were selected for AFLP analysis. DNA extraction of the accessions from these two samples (S3 and S4) was carried out at different times in the same laboratory, at Port-Vila, Vanuatu. One leaf sample per accession was used. Intra-clonal variation was not tested in this study. Attention was given to the geographical origin of the accessions. DNA extraction and purification Total DNA was extracted from 1.5g young leaves using the MATAB solution as a buffer after grinding the materials in liquid nitrogen. This buffer contained 100 mm Tris-HCl ph 8, 1.4 M NaCl, 20 mm EDTA ph 8, 2% MATAB, 1% PEG 6000 and 0.5% sodium sulfite. The mix was incubated for 30 min in a water bath at 74 C. To this were added 15 ml CIAA (chloroform:isoamyl alcohol = 24:1) and the mixture was then centrifuged at 4000 rpm for 15 min. The aqueous phase was transferred to a new tube and DNA was precipitated by adding a 2.5 volume of ethanol. DNA pellets were then resuspended in 1.5 ml of a buffer containing 50 mm Tris-HCl, 0.7 M NaCl and 10 mm EDTA at ph 7. Finally, DNA was purified by passing through a QIAGEN R -Tip 20 micro-column. AFLP procedure AFLP fingerprints were performed according to the procedure provided with the AFLP Core Reagent Kit of Life Technologies R. The amplification products were analyzed in 5% urea-acrylamide gels. The gels were run at 55 W for approximately 1 h 30 min. AFLP products were revealed by exposure to X-ray films (X- OMAT) for 6 7 days. Thirteen primer pairs were used for sample S3 and 14 pairs for sample S4 (Table 1). Data analysis Banding patterns of the AFLP autoradiographs and electromorphs were scored as present (1) or absent (0). An AFLP marker can be seen as a locus with two alleles: the first one is translated by a readable band on the gel, the second one, corresponding to some mutations in sites involved in restriction or amplification, is not readable and can be considered as a null allele. For diploids, scoring in presence/absence is rather confusing because it is not possible to distinguish between homozygotes and heterozygotes. In this case it can be shown (Perrier et al., 2003) that the Simple Matching index (Sokal & Michener, 1958) ((a +d)/ P, where a represents the common presences, d the double absences and P the total number of bands) minimizes the loss of information due to confusion between homozygotes and heterozygotes. All electromorphs (polymorphs or monomorphs) were taken into account when computing distances. For the construction of the similarity matrices and trees, the softwares used were NTSYS-PC version 1.80 (Rohlf, 1993), and Darwin, a non-commercial software of CIRAD (Perrier et al., 2003). Bootstrap values were calculated by drawing 300 entries. The NJTree method was used for the calculation of the AFLP trees. First described by Saitou and Nei (1987), this method uses a concept of relative neighboring, an unweighted mean for the updating of dissimilarities and an additive distance. When using the software NTSYS, the goodness of fit of the clustering was calculated using the COPH an MXCOMP programs. To compare the estimates of genetic similarity based on AFLP analysis and phenotypic characterization, two approaches were used: correlations between dissimilarity matrices obtained from the two types of datasets were tested by a Mantel test (MX- COMP program of NTSYS) and linear regressions were computed.

4 390 Table 1. Number of polymorphic bands produced in each sample with 13 and 14 primer pairs Primer combination polymorphic monomorphic Total Sample S3 E-AAC/M-CAA E-AAC/M-CAC E-AAC/M-CAT E-AAC/M-CTA E-ACA/M-CAA E-ACA/M-CAT E-ACC/M-CAC E-ACC/M-CAT E-ACC/M-CTC E-ACC/M-CTG E-ACC/M-CTT E-ACT/M-CAT E-AGG/M-CAT Total Percentage of bands Sample S4 E-AAC/M-CAC E-AAC/M-CAT E-AAC/M-CTA E-ACA/M-CAA E-ACA/M-CAG E-ACA/M-CAT E-ACA/M-CTA E-ACC/M-CAC E-ACC/M-CAT E-ACC/M-CTC E-ACC/M-CTG E-ACC/M-CTT E-ACT/M-CTA E-AGG/M-CAT Total Percentage of bands Results Morpho-agronomical variation Variation observed Accessions bearing the same name were identified. The number of accessions with the same name varied from 2 to 9. Overall, 66 homonyms (14.6% of the collection) were thus identified. However, when their morphoagronomic characteristics recorded in the database were checked, no accessions were found to be identical. On the other hand, several accessions with different names appeared to be agro-morphologically almost identical. No real duplicates (same name and same characteristics) were identified at this stage. Therefore, it was decided to retain all the accessions. Factorial analysis and clustering techniques (results not shown) Factorial analyses of correspondences (FAC) were performed on the Vanuatu data matrix. When looking at the projection of the accessions and the phenotypic classes on the main axes, only those accessions presenting some rare modalities, such as the cup shape of the lamina, or the orange color of the corm flesh, were clearly differentiated from the others. The dendrogram produced with the UPGMA method and the Dice dissimilarity index using all the agro-morphological characters gave high numbers of clusters. The number of individuals per cluster was very variable and many accessions appeared isolated on the tree, mainly due to the presence of rare modalities. Both the FAC and the UPGMA method gave a continuous image of the agromorphological variation, in which the different groups of morphotypes were not significantly differentiated. Stratification of the collection and samples selection Four characters of agronomic importance were used hierarchically (Figure 2). The most primitive characters, stolons and corm shape (typical of wild forms), were initially used, followed by the apparently acquired agronomic characters, adaptation and maturity. The collection was subdivided into 16 subgroups of variable size. Only three subgroups (Nos. 1, 9 and 10) included a high number of accessions (over 50 accessions). Sample S1 was formed by 51 accessions randomly chosen out of the total 452 germplasm accessions. Samples S2 and S3 contained 51 accessions too, selected from the 16 subgroups into which the collection was divided after using the dichotomous key. Samples for S2 were randomly chosen and for S3, UPGMA dendrograms were made for the largest subgroups and accessions belonging to distant clusters were selected. The number of accessions per subgroup was the same for S2 and S3 (Table 2). With regard to the frequency distribution of characters (Table 3), the three samples presented, for most characters, a distribution similar to the one of the whole collection. Semi-erect and medium height plants were over-represented in the first sample (S1). For corm

5 391 Table 2. Number of accessions selected from each morphoagronomic subgroup, as defined by the use of a dichotomous key, included in samples S2 and S3 Subgroup N o acc accessions according to the number of presences was carried out (results not shown). S3 presented the most extreme values ranging from 28 to 74%. S4 had values between 38 and 60%. Figure 2. Hierarchical classification of the Vanuatu collection using four major morpho-agronomical traits. weight, much of the χ 2 value accounted for the absence of accessions with either small or very large corms (rare frequencies in the collection). Conversely, S2 and S3 had a higher frequency of high plants and heavy corms. The high χ 2 values for characters such as corm flesh color, corm shape or the position of leaf lamina were due to the presence of rare modalities such as the orange color of the corm flesh or the elongated shape of the corm. Molecular diversity AFLP analysis of samples S3 and S4 The number of polymorphic bands per primer pair ranged from 7 to 30 (Table 1). A comparison of the Diversity structures and dissimilarity matrices Accession numbers are indicated along with their geographic origins on the two dendrograms (Figures 3 and 4). Bootstrap values of the main ties are extremely low although they are higher for the first tree. Values above 50% are generally found to cluster pairs of similar accessions. A cluster of seven accessions shows a solid structure within S3 (bootstrap value of 80%). For S4, four accessions are very tightly related at a bootstrap value of 98%. The cophenetic correlation values (0.64 for S3 and for S4) were not too high. This can be related to the weak bootstrap values obtained for the main ties. When looking at the length of the lines, we observed a larger distance between accessions than between clusters. The within-cluster heterogeneity is much larger than the between-cluster heterogeneity. Vanuatu landraces appear closely related to each other with values of average dissimilarities for both samples of and The first sample, S3, presents a higher mean dissimilarity than the second, S4. The fact that the percentage of monomorphic bands was 1.5% higher in S4 might partially account for this lower global mean. When looking at the extreme values for S3, the range was and for S4, For S3, the accessions related at the lowest distances were: (0.039); (0.043) and (0.043). When looking at the dendrogram (Figure 3), these associations correspond to the higher values of bootstrap. If we compute the number of different bands between 216 and 353, there were 32 markers, out of 191, that differed. This number represents 16.75% of the total number of polymorphic bands. It is estimated that the average error rate of reading the AFLP bands would be close to 5%. It can then be concluded that no identical cultivars (or duplicates) were detected for S3. Within sample S4, the closest accessions were: (0.011); (0.016); (0.014) and (0.021). Between 222 and

6 392 Table 3. Test of frequency distribution of agro-morphological characters between the entire collection and the three samples, S1, S2 and S3 (characters used for the hierarchical stratification are in boldface) S1 S2 S3 Descriptor χ 2 P χ 2 P χ 2 P Stolon formation Corm shape Growing conditions Maturity period Growth habit Plant height Leaf shape Leaf lamina orientation Leaf lamina margin Leaf lamina color Leaf lamina variegation Sinus outline Vein junction color Basic color of leaf petiole Petiole variations Corm weight Corm flesh color Eating quality Dry matter there were 10 bands that were different and this represents 5.1% of the total number of polymorphic bands. It is therefore difficult to state whether 222 and 219 are real duplicates. Overall, S4 includes accessions genetically closer than those of sample S3, according to the polymorphism revealed by AFLP markers. Relationship between genetic diversity and morpho-agronomic variability Estimates of pair-wise dissimilarities based on AFLP and morpho-agronomic characters were compared. Four accessions widely used as parents in the breeding program were compared to 18 other accessions. For all of them, the simple regression values were low (from to 0.1) and not significant correlations between these two indices were observed. When performing a Mantel test to compare both matrices of dissimilarities, the values obtained were also low (0.027 for S3 and for S4). Geographical distribution of the genetic diversity In both the samples, S3 and S4, more than half of the accessions originated from the island of Santo. This was expected as 242 accessions out of 452 had been collected from this island. In both the cases, however, the accessions were not grouped according to the geographical origin. For S3, for example, the five accessions from Pentecost Island are scattered in the dendrogram. Some of the closest accessions had been however collected from the same island, and in some cases, from the same location (i.e., Nos. 447 & 438 in Lalavuru, Maewo island; Nos. 219, 222 & 208 in Lamap, Malekula island). However, in those coastal villages, where many cultivars are utilized and where exchanges are frequent (Pessena, Martelli), the accessions are regularly distributed within the clusters of the dendrogram. Discussion In the Vanuatu collection, accessions bearing the same name were generally different for one or more characters. Morphological descriptors such as the position of leaf lamina, growth habit or colors of petiole and vein junction may differ for the same accession. On the

7 393 Figure 3. Dendrogram (NJTree) of genetic relationship amongst 37 cultivars of taro from Vanuatu (S3) from the analysis of 191 AFLP bands. Bootstrap values are shown. Location where cultivars were collected is indicated with the name of the island in brackets (n.a.: non available). other hand, accessions with different names appeared to be almost identical for all the characters used. The meaning of the landrace name can bring some help to duplicates detection. When trying to characterize the variation within Vanuatu accessions, using agro-morphological characters, both with factorial and cluster analysis, a continuum of variation was observed, and no clearly differentiated groups of morphotypes were revealed. This may be because the characters used were too heterogeneous (passport, agronomic and morphological characters), and generally not correlated. However, when working with agronomic and morphological characters separately, then also clearly differentiated groups were not produced. The number of phenotypic classes per character was also variable, and moreover for some characters, these classes were very unequally distributed. For this reason, those accessions with rare traits (i.e., orange color of corm) appeared clearly isolated in the dendrogram, or in the projection of the FAC. When comparing the frequency distribution of characters among the three samples and the collection, very few characters produced significant differences. In most cases, these were due to the presence or absence of

8 394 Figure 4. Dendrogram (NJTree) of genetic relationship among 42 cultivars of taro from Vanuatu (S4) from the analysis of 197 AFLP bands. Bootstrap values are shown. Location where cultivars were collected is indicated with the name of the island in brackets (na.: non available). some rare modalities. The three sampling methods (S1, S2 and S3) could be equally applied to capture significant agro-morphological variability. However, stratification of the different groups (S3) appeared to be the most reliable method to avoid sampling duplicates, either accessions with the same name or accessions identical for most of the variables. Hence, in the randomly constituted sample (S1), two accessions with a similarity index of were found. It is therefore likely that if a random strategy were applied to a much larger dataset, chances of capturing duplicates could increase. In the second sample (hierarchical key plus random choice within subgroups, S2), three pairs of accessions with the same name were also found. Only in the last sample (S3), were no duplicates identified. With AFLP markers also, no duplicates were identified and the genetic diversity found for S3 was superior to the one for S4. The individuals with the most extreme percentages of presences and the highest mean dissimilarity values were also present in sample S3. On the other hand, the closest accessions were found within the sample S4. AFLP markers proved to be a valuable tool for cultivar identification, especially in collections having a narrow genetic base (Quero-Garcia, 2000). Some cultivars were genetically very close, but there was always a slight polymorphism. In practical terms, however, breeders will avoid crosses between very close genotypes such as those identified by the AFLP analysis. The genetic diversity of taro in Vanuatu did not show a strong differentiation when looking at the bootstrap values. Similarly, within cluster heterogeneity was much larger than between cluster heterogeneity. Similar results have been observed in cassava when analyzing by AFLPs a subset of African landraces (Fregene et al., 2000). Such results can be expected in a country like Vanuatu, where flowering and sexual reproduction are almost absent. The same absence of differentiation is observed at the geographical level. Although several landraces from the same location appear very tightly clustered, they are distributed all over the dendrograms. The constant movements of clonal material across Vanuatu may explain the fact that most of the morphotypes and genotypes of the country can be found in all the islands. If total isolation had existed between islands, and there was active sexual reproduction, a geographical differentiation could have been observed. The genetic clusters revealed by AFLPs do not contain any particular group of morphotypes. This situation has already been observed in other clonal crops such as plantains (Crouch et al., 2000) and yams (Malapa et al., 2003). For some important agronomic characters such as dry matter content, some correlations exist, since the most similar landraces present rather similar values. The opposite is true for the most distant ones. However, this aspect was not checked for other characters as growing conditions, stolon formation, corm weight and eating quality. The aim of this study was to develop a stratification approach for taro germplasm collections, based on agro-morphological descriptors. The stratification of large collections according to breeder s ideotypes is necessary before initiating meaningful breeding

9 395 programmes. We have shown that subsets can be easily defined and sufficient diversity can be captured with the dichotomous key, as confirmed by the AFLP analysis. These markers were able to differentiate all the analyzed accessions, even those that seemed to be agromorphologically identical. Acknowledgements This study would not have been possible without the support of the Taro Network for Southeast Asia and Oceania (TANSAO), a project funded by the INCO programme of the European Union (DG XII) (grant no. ERBIC18CT970205). The authors are thankful to the Vanuatu Government and the Vanuatu Agricultural Research and Training Centre (VARTC). Finally, they are also thankful to Dr. Philippe Feldmann for the useful discussions and scientific advises. References Crouch, H.K., J.H. Crouch, S. Madsen, D.R. Vuylsteke & R. Ortiz, Comparative analysis of phenotypic and genotypic diversity among plantain landraces (Musa spp., AAB group). Theor Appl Genet 101: Felsenstein, J., Confidence limits on phylogenies: An approach using the bootstrap. Evolution 39: Fregene, M., A. Bernal, M. Duque, A. Dixon & J. Tohme, AFLP analysis of African cassava (Manihot esculenta Crantz) germplasm resistant to the cassava mosaic disease (CMD). Theor Appl Genet 100(5): Irwin, S.V., P. Kaufuis, K. Banks, R. de la Peña & J.J. Cho, Molecular characterization of taro (Colocasia esculenta) using RAPD markers. Euphytica 99(3): Ivancic, A. & V. Lebot, The Genetics and Breeding of Taro. Séries Repères; Cirad, Montpellier, France. Kuruvilla, K.M. & A. Singh, Karyotypic and electrophoretic studies on taro and its origin. Euphytica 30: Kreike, C.M., H.J. Van Eck & V. Lebot, Genetic diversity of taro (Colocasia esculenta (L.) Schott) in South-East Asia and the Pacific. Theor Appl Genet (in press). Lebeaux, M.O., ADDAD, Manuel De Référence Version Association pour le Développement et la Diffusion de l Analyse des Données, Paris. Lebot, V. & M. Aradhya, Isozyme variation in taro (Colocasia esculenta) from Asia and Oceania. Euphytica 56: Lebot, V., S. Hartati, N.T. Hue, N.V. Viet, N.H. Nghia, T. Okpul, J. Pardales, M.S. Prana, T.K. Prana, M. Thongjiem, C.M. Krieke, H. VanEck, T.C. Yap & A. Ivancic, Genetic variation in taro (Colocasia esculenta) in South East Asia and Oceania. In: Twelfth Symposium of the ISTRC. Potential of root crops for food and industrial resources. Sept , 2000, Tsukuba, Japan, pp Mace, E. & I.D.Godwin, Development and characterization of polymorphic microsatellite markers in taro, Colocasia esculenta. Genome 45(5): Malapa, R., G. Arnau, J-L. Noyer, J-L. Marchand & V. Lebot, Genetic Relationships between Dioscorea alata L. and D. nummularia Lam. as revealed by AFLP Marker Evidence. In : Darwin s Harvest Origins, Evolution, and Conservation of Crop Plants: A Molecular Approach. Columbia University Press, (in press). Ortiz, R., E.N. Ruiz-Tapia & A. Mújica-Sánchez, Sampling strategy for a core collection of Peruvian quinoa germplasm. Theor Appl Genet 96: Perrier X., A. Flori & F. Bonnot, Methods of data analysis. In: P. Hamon, M. Seguin, X. Perrier & J.C. Glaszmann (Eds.), Genetic Diversity of Cultivated Tropical Plants, pp Cirad, Montpellier, France. Quero-Garcia, J., Etude de la Structuration de la Variabilité Génétique du Taro. MSc Report. INAPG, 31p. Rohlf, F.J., NTSYS-PC Numerical Taxonomy and Multivariate Analysis System. Version 1.8 Exeter Publ., Setauket, New York. Saitou, N. & M. Nei, The neighbor-joining method: A new method for reconstructing phylogenetic trees. Mol Biol Evol 4: Sneath, P.H.A. & R.R. Sokal, Numerical Taxonomy, Freeman, San Francisco. Sokal, R.R. & C.D.A. Michener, A statistical method for evaluating systematic relationships. Univ Kansas Sci Bull 38: Yap, I.V. & R.J. Nelson, Winboot: A Program for Performing Bootstrap Analysis of Binary Data to Determine the Confidence Limits of UPGMA-based Dendrograms. IRRI Discussion paper series No. 14.