عنوان مقاله [English]
Introduction: The pistachio (Pistacia vera), a member of the cashew family, is a small tree originating from Central Asia and the Middle East. The tree produces seeds that are widely consumed as food. Pistacia vera often is confused with other species in the genus Pistacia that are also known as pistachio. These other species can be distinguished by their geographic distributions and their seeds which are much smaller and have a soft shell. Continual advances in crop improvement through plant breeding are driven by the available genetic diversity. Therefore, the recognition and measurement of such diversity is crucial to breeding programs. In the past 20 years, the major effort in plant breeding has changed from quantitative to molecular genetics with emphasis on quantitative trait loci (QTL) identification and marker assisted selection (MAS). The germplasm-regression-combined association studies not only allow mapping of genes/QTLs with higher level of confidence, but also allow detection of genes/QTLs, which will otherwise escape detection in linkage-based QTL studies based on the planned populations. The development of the marker-based technology offers a fast, reliable, and easy way to perform multiple regression analysis and comprise an alternative approach to breeding in diverse species of plants. The availability of many makers and morphological traits can help to regression analysis between these markers and morphological traits.
Materials and Methods: In this study, 20 genotypes of Pistachio were studied and yield related traits were measured. Young well-expanded leaves were collected for DNA extraction and total genomic DNA was extracted. Genotyping was performed using 15 RAPD primers and PCR amplification products were visualized by gel electrophoresis. The reproducible RAPD fragments were scored on the basis of present (1) or absent (0) bands and a binary matrix constructed using each molecular marker. Association analysis between molecular date (as independent variable) and morphological data (as dependent variable) was performed using multiple regression analysis to identify informative markers associated with the yield related traits. Multiple regression analysis was conducted using stepwise method of linear regression analysis option of SPSS. Student t-test was performed to assess significance difference between mean trait estimates of genotypes where specific markers were present and absent. Markers shown significant regression values were considered to be associated with the trait under consideration.
Results and Discussion: Finally 11 primers were polymorphic and a total of 56 pieces (loci) were amplified that among these, 36 segments (64.29%) showed polymorphism with an average of 5.09% per primers and the rate of this polymorphism ranged from at least 25% for AJ05 primer up to 87.5% for OPAD02 primer. Polymorphic information content ranged from 0.095 (AJ05 and OPAD14) to 0.39 (OPC05), with an average of 0.23. Stepwise regression analysis between molecular data and traits was performed to identify informative markers associated with yield component traits. Nineteen RAPD fragments were found associated with six yield related traits. Some of RAPD markers were associated with more than one trait in multiple regression analysis that may be due to pleiotropic effect of the linked quantitative trait locus on different traits. However, to better understand these relationships, preparation of segregating population and linkage mapping is necessary. Also, these results could be useful in marker-assisted breeding programs when no other genetic information is available.
Conclusion: This investigation on molecular markers associated with yield traits in Pistachio has provided clues for identification of the genotypes with higher yield value. In breeding programs selection of quality material is often a time-consuming process, and thus marker-assisted selection could be of great useful in identification of promising genotypes with high value of yield traits. Some of RAPD markers can be used for elite selection of Pistachio, particularly when no other genetic information like linkage maps and quantitative trait loci are available for the species. The applications of the RAPD approach enable us to predict positive correlation between data generated by molecular markers and studied traits. Also, the marker–trait association identification will play an important role in plant MAS/QTL breeding programs, especially in plants where genetic information such as linkage map and QTL is not available.