DETECTION OF MOLECULAR MARKERS ASSOCIATED WITH SALT TOLERANCE IN ALFALFA (Medicago sativa L.)

Authors

  • FATTHY M. ABDEL-TAWAB Department of Genetics, Faculty of Agriculture, Ain Shams University, Cairo
  • EMAN M. FAHMY Genetic Dept., Fac. Agric., Ain Shams Univ., Cairo, Egypt
  • M. A. EL- NAHRAWY Forage Crops Res. Sec., Field Crop Res. Inst., ARC, Giza, Egypt
  • WAFAA M. SHARAWY Forage Crops Res. Sec., Field Crop Res. Inst., ARC, Giza, Egypt
  • MERVAT R. I. SAYED Forage Crops Res. Sec., Field Crop Res. Inst., ARC, Giza, Egypt

Abstract

Alfalfa (Medicago sativa L.) is one of the world's most valuable forage legumes. It is grown for hay, pasture and silage, and is valued highly as a livestock feed. It is characterized by adaptability to wide range of soils and growing conditions, flexibility in being able to grow alone or mixed with grasses and high yields. It is the most cultivated forage legume due to its ability to fix atmospheric nitrogen and its high protein content.
Soil salinization significantly limits crop production and consequently has negative impact on food security. It is one of the major abiotic stresses that affects crop productivity and quality and has been described as one of the most serious threats to agriculture and the natural status of the environment. Increased salinization of arable land is expected to have devastating global effects, resulting in a 30% land loss within the next 25 years and up to 50% by the year 2050 (Lorenzo et al., 2007).
The analysis of the genetic variability within and among populations of cultivated alfalfa can assess future risk of genetic erosion and help in the development of sustainable conservation and genetic improvement strategies. Successful assessment of the genetic diversity of alfalfa has been hampered by the statistical methods available (Stanford, 1951; Flajoulot et al., 2005). Alfalfa species are composed of ecotypes, population complexes adapted to the environment of a given climatic region or to definite habitats within a region (Helmy et al., 2003). Environmental constraints represent the most limiting factors for agricultural productivity and play a major role in the distribution of plant species across different types of environments. Environmental factors, such as drought and salinity are responsible for significant yield reductions. Developing cultivars that tolerate abiotic stresses is one of the major goals of breeding programs of alfalfa. Molecular markers can assist these programs by identifying the important traits, helping in screening the genotypes and selecting them.
Molecular markers can be identified by a range of molecular techniques such as restriction fragment length polymorphisms (RFLPs), randomly amplified polymorphic DNAs (RAPDs), amplified fragment length polymorphism (AFLP), DNA amplification fingerprinting (DAF), sequence characterized amplified regions (SCARs), microsatellites (SSR) etc, (Lin et al., 1996). Also, there are several different DNA analytical procedures that have been used to identify, characterize and determine genetic diversity among cultivars. AFLP is one of the most the recently DNA analysis procedures, which combines assay flexibility with a high degree of sensitivity and reproducibility to yield significantly more information about the plant genome under study than other techniques (Lin et al., 1996). AFLP is a method for genotyping individuals for a large number of loci using a minimal number of PCR reactions. AFLP markers are efficient tools for estimating genetic similarity in plant species and effective management of genetic resources. They are a reliable method of genetic fingerprinting and have been successfully used for characterization and evaluation of genetic relationships in several species (Vos et al., 1995; Neqi et al., 2000).
The objectives of this study were to test the most tolerant and the most sensitive alfalfa (Medicago sativa L.) genotypes, their F1, and F2 under salt condition for some yield-related traits and obtain molecular genetic markers associated with salt tolerance by bulked segregant’s analysis technique using RAPD, ISSR and AFLP analysis.

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2016-01-11

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