AFLP analysis of enset clonal diversity in south and southwestern Ethiopia for conservation. (Crop Breeding, Genetics & Cytology).
Characterization of clones by morphological markers is rudimental (Endale, 1997), and a well-established taxonomic classification and descriptor list are lacking. In addition, few attempts have been made to document and analyze clonal identity using farmers' classification. In these cases, clonal names reported in the literature are associated with only limited phenotypic data provided by farmers (Shigeta, 1991). Local knowledge is the main source of passport and agronomic data for the genetic resource collections maintained by the Institute of Biodiversity Conservation and Research in Addis Ababa, the Institute of Agricultural Research at Areka, and the Debub University in Awassa.
Most of the genetic diversity of enset is traditionally maintained in situ by farmers. Unfortunately, many valuable clones have been lost due to various human and environmental factors (Gebremariam, 1996), which may have reduced the total available genetic diversity of the crop. Lack of knowledge about the genetic diversity of this crop species complicates the conservation, improvement, and utilization of enset by farmers, conservationists, and breeders.
Although assessment of morphological variation present in enset is feasible, its use is rather limited due to the small number of phenotypic markers and the fact that they are influenced by environmental conditions. Therefore, in this study, molecular methods have been applied to characterize germplasm diversity in enset as a complementary approach.
Molecular genetic markers are usually unaffected by the environment and can often be generated in large numbers (Vosman et al., 1999). The AFLP technique applied in this study combines the use of restriction enzymes and polymerase chain reactions (PCRs) (Vos et al., 1995). Amplified fragment length polymorphism fingerprinting profiles can be generated without prior knowledge of genome sequences, and therefore be applied to DNAs of any origin. Amplified fragment length polymorphism fingerprinting usually results in informative profiles due to the high number of genomic fragments that can be analyzed in a single assay (Lin et al., 1996; Powell et al., 1996). Amplified fragment length polymorphisms have been applied successfully to characterize germplasm of various crops, including Musa spp., which are close relatives of enset (Engelborghs et al., 1998).
In our study, AFLPs have been employed to characterize 146 enset clones from southern and southwestern Ethiopia. Specifically, we aimed to assess genetic relationships among the clones, identify duplicated accessions, and investigate regional variation. Implications of our results for enset conservation efforts in Ethiopia are discussed.
MATERIALS AND METHODS
Leaf samples from 146 enset clones were collected on farm from southern and southwestern Ethiopia in 1999. A representative set of clones were collected from the available diversity maintained in farmers' fields. Vernacular names were obtained from the enset farmers who provided the germplasm. Samples were collected from the Chena (n = 36) and Decha (n = 29) districts of the Kaffa-Shaka zone in southwestern Ethiopia (located 70 km apart) and from the Sidama (n = 30), Hadiya (n = 45), and Wolaita (n = 6) zones in southern Ethiopia (Fig. 1). The Kaffa-Shaka zone is located [approximately equal to] 450 km from the closest other zone where samples were collected. A complete list of the enset clones analyzed is available upon request from Almaz Negash (Chena and Decha districts) and Admasu Tsegaye (Sidamo, Hadiya, and Wolaita zones).
[FIGURE 1 OMITTED]
Pieces of young leaf tissue were harvested for each clone and stored in 50-mL tubes containing a saturated NaCl-CTAB (N-hexadecyltrimethylammonium bromide) preservation buffer, following the procedures of Rogstad (1992). Upon return to the laboratory 2 wk later, the CTAB was washed off thoroughly with distilled water. About 50 to 100 mg of leaf tissue was then transferred to 2-mL Eppendorf tubes (Eppendorf A.G., Hamburg, Germany) and stored at -80[degrees]C, awaiting further analysis.
Tissue samples were vacuum dried overnight and mechanically ground using a retch shaking mill and about five glowed glass beads per Eppendorf tube. Since DNA analyses in enset had not been described before, three different DNA extraction procedures as described by Fulton et al. (1995), Rogstad (1992), and the Qiagen spin column extraction method (Dneasy Plant Mini Kit, Qiagen, Westburg b.v., Leusden, The Netherlands) were tested in order to determine the optimal protocol. No amplification using PCR could be accomplished with DNA obtained by the microprep protocol of Fulton et al. (1995). The other two protocols both resulted in DNA that enabled proper amplification, but slightly better results were obtained from DNA isolated with the Qiagen spin column method. This method was therefore adopted to isolate genomic DNA from all samples. Extracted DNA samples were stored at 4[degrees]C. DNA concentrations were estimated by comparing 2 [micro]L of each sample with 20, 40, 60, 80, and 100 ng of phage lambda DNA on a 0.8% (w/v) agarose gel.
Amplified Fragment Length Polymorphism Protocol
In general, the AFLP protocol followed the procedures described by Vos et al. (1995). Briefly, [approximately equal to] 300 ng of total genomic DNA was digested to completion using five units of EcoRI and five units of MseI. Amplified Fragment Length Polymorphism adapters for both restriction enzymes were then ligated to the fragments. Selection of EcoRI-EcoRI and EcoRI-MseI fragments from the total fragment pool, by the use of biotinylated EcoRI adapter and magnetic beads, was not applied. Subsequently, template DNA was preamplified using primer pairs based on the sequence of the adapters, and 3' extended with one selective nucleotide ("A" for the EcoRI primer and "C" for the MseI primer). Successful amplification was verified by electrophoresis of a portion of the PCR products on a 2% (w/v) agarose gel. Diluted preamplification product was then used as template in a second amplification reaction, using primer pairs variably extended with a number of selective nucleotides at the 3' end. The EcoRI primer was radiolabeled with [sup.33]P prior to PCR. Labeled PCR products were separated on 6% (w/v) denaturing polyacrylamide gels (Biozym, Sequagel-6) and exposed to x-ray film (XOMAT AR, Eastman Kodak, Rochester, NY) for several days. Goldstar Taq DNA polymerase (Eurogentec) was used for PCR, and all amplification reactions were performed on a Perkin Elmer (Norwalk, CT) 9600 thermocycler. Cycling conditions were as follows: 1 cycle of 30 s at 94[degrees]C, 30 s at 65[degrees]C, and 60 s at 72[degrees]C; 12 cycles in which the initial annealing temperature of 65[degrees]C was lowered by 0.7[degrees]C each cycle; 23 cycles in which the annealing temperature was held constant at 56[degrees]C. More details about the experimental procedures are given by Arens et al. (1998).
Following preamplification of the samples, 12 different primer combinations (E + AA, E + AC, and E + AG in combination with each of M + CCA, M + CCT, M + CGG, and M + CTC) were tested on six clones (vernacular names: Chele bocho, Choro, Neche nobo, Gesh ariko, Neche epo and Ketano) in order to identify suitable primer pairs. This test panel of six clones was selected based on expected dissimilarity. Four primer combinations were selected for analysis of the total sample based on resulting fingerprinting profiles that could be scored unambiguously for multiple variable AFLP fragments. As a control for the reproducibility of the patterns, four replicate tissue samples from a single individual were included in all experimental steps in order to estimate the frequency of artifact bands on the autoradiograms.
Autoradiograms (approximate size range of the fragments: 50 to 500 bp) were manually scored and segregating bands were recorded as polymorphic AFLP fragments. The number of polymorphic and monomorphic fragments was determined for each primer pair. Clones were only designated identical when all AFLP fragments generated with the four primer pairs fully matched. Band sharing data were used to calculate genetic similarities between samples based on Jaccard's coefficient. The similarity values were used to graphically represent genetic relationships between the clones by the unweighted pair-group method using an arithmetic average (UPGMA) clustering algorithm (Nei, 1987) and principal coordinate plots. Matrices of Jaccard's similarity coefficients based on different primer pairs were tested for significant correlation (1000 permuted data sets) by a Mantel test (Mantel, 1967). These analyses were carried out using the Genstat 5 software package (release 4.1, VSN Int. Ltd., Oxford). To investigate regional variation, an analysis of molecular variance (AMOVA) was used to compute molecular variance components for AFLP phenotypes within and between geographical regions. These analyses were carried out using Version 1.55 of the software package WINAMOVA (Excoffier et al., 1992), after preparing the input files for the analysis of dominant data using Mark Miller's AMOVA-PREP software program. Monomorphic loci were included in all data analyses.
RESULTS AND DISCUSSION
Amplified Fragment Length Polymorphism Variation in Enset
Out of the 12 primer combinations that were analyzed on a test panel of six clones, four primer pairs (E + AA/M + CCA, E + AA/M + CCT, E + AG/M + CCA, and E + AG/M + CCT) that generated optimally clear AFLP profiles with multiple polymorphic bands that could be scored unambiguously were selected to analyze the total sample (Fig. 2). A total of 180 AFLP fragments was scored among the 146 clones. One hundred and four fragments (58%) were polymorphic (Table 1).
[FIGURE 2 OMITTED]
The level of polymorphism observed for each of the four primer pairs was similar, with values ranging from 0.52 to 0.61 (Table 1). Mantel tests revealed significant correlations between all pairs of primer combinations tested (range of correlation coefficients: r = 0.12 to 0.27, P < 0.001 in all cases). Thus, with respect to the observed level of variation and the genetic similarities among clones, consistent results were obtained with the four primer pairs investigated. Identical AFLP profiles were observed among four replicate tissue samples for each of the four primer pairs, indicating that the results obtained were not substantially affected by the generation of artifact bands.
Identification of Duplicate Clones
Within the 146 clones, 21 duplication groups consisting of 58 clones were identified based on the AFLP data (Table 2). In other words, 37 clones, or 25% of the collection consisted of duplicated clones. Duplication of clones may have at least two different origins.
First, a number of identical clones appeared to have similar or related vernacular names, that is, the clones in Groups 1 to 6 (Table 2A). In most cases, these duplications can be ascribed to different use purposes for the same clone (Groups 1 to 4). According to our own observations, farmers can consciously use different names for identical clones based on variation in the pseudostem size, time until maturity, or other phenotypic characteristics. According to a related custom, in Kaffa-Shaka and the neighboring North Omo zone, farmers characterized several clones as male or female, with no apparent relationship to the genotype of the plant. This habit appeared to reflect the qualities desired by male and female farmers. For example, the male Nobo is a large, vigorous, and strong plant and results in a high yield, whereas its taste is less preferred. The female Nobo is thin, less vigorous, and preferred for its taste (Alemu and Sandford, 1996; Habtewold et al., 1996; Negash, 1998, unpublished data). The male or female naming of a plant appeared to be based on environmental factors influencing its development. Clones with similar or related vernacular names may also result from the use of different dialects among communities, such as the clones Ketano and Katino in Group 5 and the clones Bekecho and Bokucho in Group 6 (Table 2A).
Second, the majority of duplications displayed unrelated vernacular names and can probably be ascribed to the changing of vernacular names after the exchange of clones within zones (Table 2B) or between zones (Table 2C). According to the information supplied by farmers, exchange of planting materials is intense, and vernacular names may be altered after some period of adaptation of the exchanged clone, corresponding to a farmer's own preferences and languages. Consequently, identical clones have been named differently by various communities. This might have contributed to duplication of clones included in this study that did not exhibit similar vernacular names. Although the majority of duplicate clones were collected within districts and zones rather than between zones, identical genotypes were also observed across largely varying agroecological systems, geographical distances, and language groups (Table 2C). This finding indicates that exchange of clones among farmers has not been restricted to single zones or similar environmental conditions. Traditional migration or exchange of clones among regions irrespective of geographical distances in order to increase the diversity of individuals for utilization has been reported to occur frequently among enset farmers (Tsegaye, 1991; Tsegaye and Struik, 2000).
Although full identity between clones can only be ascertained when the entire genomes of individuals are compared, a limited number of primer pairs is often found to be sufficient for cultivar discrimination (Schut et al., 1997; Cervera et al., 1998). In our study, two AFLP primer combinations appeared to be sufficient to distinguish the majority of enset clones. The number of clones distinguished increased only slightly after extending the set with a third and fourth primer pair, respectively (Table 3). Although the maximum number of clones that can be distinguished with 104 polymorphic AFLPs is 2.03 X [10.sup.31], further extension of the number of primer combinations may reveal polymorphisms between clones that were found identical in the present study. An example is given by the clones Ketano and Choro, that despite reported phenotypic differences, appeared identical based on four primer pairs (Table 2: Duplication Group 5). Coincidentally, both clones were involved in the testing of the 12 different primer pairs in the initial phase of the study. Reexamination of the fingerprinting profiles revealed one or two polymorphic fragments between these clones for three out of the eight additional primer pairs tested (E + AC/M + CCT, E + AC/M + CGG and E + AG/M + CGG). However, a genetic resources program aiming at optimization of conservation efforts will usually not be jeopardized by the loss of some closely related clones.
Regional Variation of Enset in Ethiopia
The Southern Nation, Nationalities, and Peoples' Regional Government (SNNPR) is the major enset growing regional state in Ethiopia. The zones in which clones were collected represent the major enset-based farming systems and agroecological zones in this state. In total, enset is cultivated on an estimated 110 000 hectares in the SNNPR, which is two-thirds of the total area under enset in the country (Central Statistical Authority, 1997), the remainder being located in the neighboring states of Oromiya and Gambella. Thus, the clones analyzed in this study probably represent a major part of the total genetic diversity in the crop. The agroecological conditions in the sampled zones vary in altitude (m above sea level: Hadiya, 2220-2400, Kaffa-Shaka, 700-3400; Sidama, 2600-2650; Wolaita, 1750-1820), annual rainfall (average in mm: Hadiya, 1500; Kaffa-Shaka, 1400-1600; Sidama, 1350; Wolaita, 1440), average temperature (minimum and maximum mean air temperature in [degrees]C: Hadiya, 9.1-22.3; Kaffa-Shaka, 15-26; Sidama, 6.7-19.2; Wolaita, 14.8-25.7), and soil type (Hadiya, sandy loam; Kaffa-Shaka, clay loam; Sidama, clay loam; Wolaita, silt loam).
Regional differentiation appeared to be limited, as no clear clustering of genotypes from a single district or zone could be detected by UPGMA cluster analysis (results not shown). Absence of regional differentiation was also revealed by a principal coordinate plot of all the enset clones studied (Fig. 3). The two principal axes together explained only 17% of the total variation, indicating the limited genetic differentiation according to geographic origin among the clones investigated in this study. Mean similarity values among clones between regions ranged from 0.83 to 0.85 and were comparable to those (range 0.83-0.86) found within regions (Table 4). An AMOVA revealed that only 4.8% of the total genetic variance is distributed between the five regions studied, whereas 95.2% can be found within regions. Even in the light of historical long distance exchange, this result was rather unexpected based on the large variation in agroecology and the comparatively large geographic distances between the different regions within Ethiopia. Identical enset clones apparently perform quite well under very different growing conditions. A possible explanation for this finding is phenotypic plasticity. Enset is known for its ability to adapt to different agroecological conditions, with appropriate elevations ranging from 1400 to 3100 m above sea level (Endale et al., 1996). It is unknown whether this coverage of a wide range of altitudes involves genetic or phenotypic adaptation. A detailed study on the adaptive behavior of enset clones under varying environments may resolve this issue.
[FIGURE 3 OMITTED]
Implications for the Conservation of Enset in Ethiopia
Lack of empirical knowledge about the genetic diversity of a crop hampers the efficient conservation and utilization of its genetic resources. No genetic data on the clonal variation in enset have so far been available. Our results indicate that AFLP analysis can be successfully applied to study clonal diversity in enset. In addition to this genetic analysis, studies on agro-morphological diversity and utilization of enset clones by farmers are described in an accompanying paper (Tsegaye et al., 2001, unpublished data). In brief, farmers' classification of agro-morphological diversity and use value correlated positively, but weakly with the molecular data. In particular, farmers identified considerably fewer duplications (11 duplication groups consisting of 23 enset clones), of which the majority was identified also by molecular genetic analysis. Major characters by which farmers distinguished the enset clones were leaf color, pseudostem color, midrib color, fiber quality, time to maturity, corm use, and medicinal value. In agreement with the molecular analysis, regional clustering of the diversity was virtually absent. Together with a detailed study of the local knowledge of farmers, molecular and agromorphological data provide the necessary information to develop an efficient strategy for the management of genetic resources of enset.
Conservation of enset genetic resources ex situ as seed in cold storage is difficult or even impossible. Seeds cannot be obtained easily, and if so they are difficult to store because of their bulky size and are hard to germinate. Moreover, conservation of seeds has limited value for utilization in view of the preferred clonal propagation of the crop. Therefore, genetic resources of the crop can only be conserved either in situ (on-farm) or ex situ (in vitro, or in field genebanks). Since these approaches are capital-intensive and enset has been a neglected crop due to its geographically limited use, optimal effectiveness of an enset conservation program is of major importance. Knowledge about clonal diversity allows the selection of clones prioritized for conservation by removing duplications and optimizing genetic diversity, and hence optimizing the cost:benefit ratio in maintaining the crop's germplasm. Absence of regional differences in diversity strongly suggest that the identified clones largely represent dominant diversity in the major enset growing regions in Ethiopia. Additional diversity qualifying for conservation programs might be found in strongly divergent agroecosystems and culturally different ethnic groups. Additional collecting missions should focus on such areas. Duplications in the clones identified by both the molecular analsyis and farmers' classification (Tsegaye et al., 2001, unpublished data) may be safely removed from a conservation program. Moreover, duplication according to molecular analysis probably indicates close relationships between the clones involved, thus allowing a substantial structural reduction of up to 25% of total conservation costs, and justifying this type of analysis from an economic perspective.
Our results indicate that there is considerable diversity in the crop, despite the reported loss of several important clones from farmers' fields. Our findings are comparable to those reported for Musa (Engelborghs et al., 1998). A more extensive investigation including divergent production areas not yet covered would extend the current overview of enset genetic diversity in Ethiopia and allow its effective conservation.
Table 1. Number of bands scored and degree of polymorphism for the primer combinations E + AA/M + CCA, E + AA/M + CCT, E + AG/M + CCA, and E + AG/M + CCT among the 146 enset clones analyzed in the present study. Primer combination E + AA/M E + AA/M E + AG/M E + AG/M + CCA + CCT + CCA + CCT Total Total number of bands scored 49 35 48 48 180 Number of polymorphic bands 30 20 29 25 104 Degree of polymorphism 0.61 0.57 0.60 0.52 0.58 Table 2. Duplication groups (1 to 21) of enset clones based on identical AFLP fingerprinting profiles obtained for the primer combinations E + AA/M + CCA, E + AA/M + CCT, E + AG/M + CCA, and E + AG/M + CCT. Enset clones are denoted by their vernacular name, with their geographic origin in Ethiopia given in parentheses (C = Chena, D = Decha, H = Hadiya, S = Sidama, and W = Wolaita). Clones are categorized in three groups based on similar or related vernacular names (A), duplication within zones (B), and duplication between zones (C). Group Enset clones A. Duplication groups consisting of clones with similar or related vernacular names 1 Chele ariko (C), Tuti ariko (C) 2 Bajo (D), Yahi bajo (D), Woiro (D) 3 Ganji bocho (C), Chele bocho (D) 4 Aei nobo (C), Anami nobo (C), Chele nobo (C), Gebi nobo (C), Machi nobo (C), Neche nobo (C), Goshno (C), Omo (C), Chongo (D) 5 Ketano (C), Katino (C), Akibero (C), Kachichi (C), Choro (D) 6 Bekecho (H), Bokucho (H) B. Duplication groups comprising single zones 7 Ofichi (C), Shelako (C) 8 Shimo (C), Chamero (D) 9 Gayo (D), Wango (D) 10 Geno (D), Utino (D) 11 Kekero (D), Utro (D) 12 Agade (H), Mariye (H) 13 Beneja (H), Kombotra (H) 14 Manduluka (H), Orada (H) 15 Oniya (H), Torora (H), Wordes (H) 16 Woshamaja (H), Zobra (H) 17 Gamechela (S), Warukore (S) C. Duplication groups comprising different zones 18 Hichewi (C), Kapicho (C), Chichia (W) 19 Tayo (D), Birbo (S), Made (S) 20 Bedadeda (H), Dirbo (H), Hayiwona (H), Shewite (S) 21 Eshamwessa (H), Shalakumia (W) Table 3. Cumulative number of enset clones that could be distinguished based on the AFLP data from single primer combinations (one primer pair: E + AA/M + CCA, E + AA/M + CCT, E + AG/M + CCA, and E + AG/M + CCT), and from combined sets of two, three and four primer pairs. The range given in the columns under two and three primer pairs represents the upper and lower values depending on the choice of the second and third primer group, respectively. One primer Two primer Three primer Four primer Primer pairs pair pairs pairs pairs E + AA/M + CCA 69 101-103 105-107 109 E + AA/M + CCT 72 97-101 106-107 109 E + AG/M + CCA 86 101-103 105-107 109 E + AG/M + CCT 59 97-102 105-106 109 Table 4. Mean Jaccard's similarity coefficients based on the AFLP data from the primer combinations E + AA/M + CCA, E + AA/M + CCT, E + AG/M + CCA, and E + AG/M + CCT, providing an indication on the genetic relationships of clones within and between regions. Mean values within and among the five regions (Chena, Decha, Hadiya, Sidama, and Wolaita) studied are presented on the diagonal and below the diagonal, respectively. Standard deviations are given between parentheses. Regions Chena Decha Sidama Hadiya Wolaita Chena 0.84 (0.05) Decha 0.83 (0.05) 0.83 (0.05) Sidama 0.84 (0.03) 0.83 (0.04) 0.85 (0.03) Hadiya 0.84 (0.04) 0.84 (0.04) 0.85 (0.03) 0.86 (0.03) Wolaita 0.84 (0.04) 0.83 (0.04) 0.85 (0.03) 0.84 0.85 (0.03) (0.02)
Abbreviations: AFLP, amplified fragment length polymorphism; AMOVA, analysis of molecular variance; PCR, polymerase chain reaction; SNNPR, Southern Nation, Nationalities, and Peoples' Regional Government; UPGMA, unweighted pair-group method using an arithmetic average.
This study was carried out at the Centre for Genetic Resources, The Netherlands. A. Negash would like to thank the Global Environmental Fund (GEF) for financial assistance provided through the United Nations Development Program (UNDP) for a Ph.D. fellowship. She also gratefully acknowledges the Institute of Biodiversity Conservation and Research for permission to take a study leave. A. Tsegaye acknowledges Crop Physiology Chair of the Wageningen University and Research Centre for providing funds for the molecular analyses, the Norwegian Agency for Development and Co-operation (NORAD) for covering the costs for travel and living allowances, and the Norwegian Universities Committee for Development Research (NUFU) for financing the field work.
The authors also would like to thank Prof. Paul Struik and three anonymous reviewers for helpful comments on an earlier version of the manuscript, and Herman Nijland for helping to prepare the figures of the paper.
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Almaz Negash, Admasu Tsegaye, Rob van Treuren, * and Bert Visser
A. Negash, Inst. of Biodiversity Conservation and Research, P.O. Box 30726, Addis Ababa, Ethiopia; A. Tsegaye, Debub Univ., Awassa College of Agriculture, P.O. Box 5, Awassa. Ethiopia; R. van Treuren and L. Visser, Centre for Genetic Resources, the Netherlands, Plant Research Int., P.O. Box 16, 6700 AA Wageningen, the Netherlands. Received 11 Jan 2001. * Corresponding author (R.vanTreuren@plant. wag-ur.nl).
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|Author:||Negash, Almaz; Tsegaye, Admasu; van Treuren, Rob; Visser, Bert|
|Article Type:||Statistical Data Included|
|Date:||Jul 1, 2002|
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