AFLP MARKER IN GENETIC DIVERSITYASSESSMENT OF FIG (Ficus carica L.) POPULATIONS IN KURDISTAN REGION – IRAQ

In this study, the genetic relatedness of 12 cultivars of fig from different populations in Kurdistan regionIraq were analyzed using eleven AFLP primers pairs combinations by using the technology of molecular analysis the DNA. Genetic similarity matrices were produced for the AFLP data to calculate genetic distances among their cultivars. Genetic similarity coefficient ranged from 0.1261 to 0.3905. The lowest genetic similarity was observed between Kola and Gala Zard (0.1261). The Hejeera Rash and Shela cultivars were most similar ones with a coefficient of 0.3905. Clustering based on AFLP data for the 12 fig cultivars was identified at the 0.32 similarity level. In the developed dendogram two main groups were found, the first one combined Ketek and Shela together, while the second group contained two sub group Shingaly and Benatty combined together, while in the other sub group cluster three other sub-group were identified. The results of this study may help in the formulation of appropriate strategies for conservation and cultivar improvement in figs, for which limited knowledge of the genetic diversity is available.


‫العراقية‬ ‫الزراعية‬ ‫العلوم‬ ‫مجلة‬
. The genus Ficus is made up of about 1,000 species from pan-tropical to subtropical origins (32) . Fig plants are all woody in the family, from trees and shrubs to climbers (22). The name carica is named after the Caria place in Asia Minor, home of the fig. (11, 22) F. carica is presumed to originate from Western Asia and spread to the Mediterranean by humans (9). Today, it is considered as one of an important world crop, because of their nutritional, medicinal, food industry and ornamental values (13,15). According to FAO reports, the planet generates more than one million tons of figs per annum (12). Large edible fig producers include Turkey, Egypt, Morocco, Spain, Greece, California, Italy, Brazil and other usually mild winters and hot dry summers (29). The available methods for fig plants diversity analysis include the classical research methods which mainly include morphological and agronomical traits, biochemical markers and cytological such as cell karyotype analysis and isoenzymes (4,14,19). These methods are considered as sensitive to environmental factors and the number of markers is limited, thus the research of fig diversity has been limited. Molecular marker techniques such as RFLP, ISSR, RAPD, and AFLP have vastly improved knowledge on genome structure, organization, and evolution of many cultivates plants (1,2,5,10,18,21,24). AFLP analysis has been used to detect DNA polymorphisms and the genetic relationships of many economically important

DNA Extraction
Genomic DNA was extracted from fresh healthy tissue as mentioned by Weigand, et al (33). Fresh tissue (3g) was homogenized to powder with 40 ml in liquid nitrogen. The fine powder was dissolved in a pre-heated (60 C o ) 2x CTAB extraction buffer (2x CTAB, 5M NaCl, 1M Tris-HCl, 0.5 M EDTA), and incubated at 60 o C in a water bath with shaking for 30 min. The mixture was extracted with an equal volume of choloroform / isoamyl alcohol (24:1, v/v) (20). The mixture was then centrifuged at 4000 rpm for 30 min. The aqueous phase was transferred into fresh tube and precipitated with 0.66 volume of isopropanol. Precipitated nucleic acids were then dissolved in 500µl Tris EDTA TE-buffer (1 ml of 1M tris-HCl (PH8.0) 0.2µl of 0.5M EDTA.

PCR Amplification of AFLP-primers
The AFLP procedure was performed as described by Vos, et al (31) as follows; 500ng of DNA from each sample was double digested with 5U each of the two restriction enzymes, MseI (recognition site 5'T↓TAA3') and PstI (recognition site 5'CTGCA↓G3'). The digestion reaction was prepared in 30µl final volume containing, 1x one-phor all buffer (Pharmacia Bioteh, Uppsala, Sweden), and incubated for three hours at 37 o C. DNA fragments, were then ligated to Pst I and MseI adapters by adding 50 pmol of MseI-adapter, 5 pmol PstI-adapter in a reaction containing 1U of T4-DNA ligase, 1mM rATP and 1x of onephore-buffer and incubated for 3hr. at 37C. After ligation, the reaction mixture was diluted to 1:5 using sterile distilled water. Pre selective PCR amplification was performed in a reaction volume of 20 µl containing 50ng of each of the primers (P00, M00) corresponding to the MsI and Pst I adapters, 2µl of template-DNA, 1U Taq DNA polymerase, 1x PCR buffer and 5mM dNTPs. PCR amplification was performed in WMG thermal cycler using the following program: 30 cycles of 30s at 94 ºC, 1min at 60ºC, 1min at 72 ºC. Preamplification products were then diluted to 1:5 and 2µl were used as template for selective amplification. Selective amplification was conducted using MseI and Pst1 selective primer combinations, (Table 1). Amplification was performed using a selective program of 36 cycles with the following profile: a 30sec. DNA denaturation step at 94ºC, 30sec. annealing step, and a 1 min extension step at 72ºC. The annealing temperature in this program varied in the first cycle where it was 65ºC and in each subsequent cycle for the next 12 cycles it was reduced by 0.7ºC (touchdown PCR). Then for the remaining 23 cycles, it was 56ºC. Selective amplification products were loaded onto 6% polyacrylamid gels, and DNA fragments were visualized by silver staining kit (Promega, Madison, Wis) as described by the supplier. Silverstained gels were scanned to capture digital images of the gels after air drying.

Data analysis
The digital photographs of gels were used to score the data for AFLP analysis starting from the higher molecular weight product to lowest molecular weight product. Presence of a product was identified as (1) and absence was identified as (0). Data were scored for all genotypes, their amplification product and primers. The data then entered into NTSYS-PC (Numerical Taxonomy and multivariate Analysis System), Version 2.1 (Applied Biostatistics) program (26) using the program editor. The data were analyzed using SIMQUAL (Similarity for Qualitative Data) routine to generate genetic similarity index (23).

RESULTS AND DISCUSSION
The results of selective primer amplifications are shown in (Figure 1 A,B)  . What supports any study is the appearance of polymorphic bands or bands with different sizes that provide the database with the ability to make it eligible to carry out the necessary genetic analyzes that are consistent with the objective of the study (30,3). So the importance of primer combinations is measured by the number of polymorphic bands, it stands out for the discrimination Power for each combination, it is compared to the total product polymorphic bands that showed by all combinations that used in any study. Another important variation by using AFLP marker as in all molecular markers is the differences in molecular weight (bp) for bands, those present on gel. In currently study the size of the AFLP amplified fragments ranged from 50bp. to 1500bp. Other study was reported by Laddomada, et al (17) to assess polymorphism and relationships among 24 fig accessions using AFLP markers; 553 amplification products of which 535 were polymorphic among the analyzed genotypes. A high degree of polymorphism was revealed by these primer combinations. The results showed (6) that using AFLP marker with Tunisian fig germplasm is characterized by having a large genetic diversity at the deoxyribonucleic acid level, as most of AFLP bands were detected. In fact, 351 (342 polymorphic) were detected using AFLP primers. AFLP markers showed the highest effective multiplex ratio (56.9). It was not accurate to identification of varieties depending on morphological traits only. May be a variety have many names in different plantation and genetically different varieties may have the same name (28). There were several different DNA marker analysis techniques that have been used to identify and characterize fruits to determine genetic diversity (16).  Genetic Similarity Genetic similarity matrices were produced for the AFLP data to calculate genetic distance. As shown in (Table 3) genetic similarity coefficient ranged from 0.1261 to 0.3905. The lowest genetic similarity was observed between Kola and Gala Zard (0.1261). The Hejeera Rash and Shela populations were most similar ones with coefficient of 0.3905. These data were used to generate a dendogram.

Cluster analysis
Dendogram was established with UPGMA cluster analysis based on the AFLP data using 11 combination primers. Clustering based on AFLP data for the twelve figs was identified at the 0.32 similarity level ( Figure 2). In this dendogram there was two main groups, the first one combined C3 Ketek and C10 Shela together, while the second group contain two sub group C1 Shingaly and C2 Benatty combine together, while the other sub group cluster there are three other sub-group, the first one C8 Rehan Zard and C9 Zarda Roon clustered together, the other sub-group dived to more sub group which first include C5 Hejeer Rash and C6 Rash khomali together in one cluster, second C7 Rehan Rash cluster alone. The third sub group also contains C4 Rebwary Rash clustered alone.