Morphological – SRAP Molecular Markers Association in Grain Sorghum Genotypes

Reader Impact Factor Score
[Total: 2 Average: 5]

Published on International Journal of Agriculture & Agribusiness
ISSN: 2391-3991, Volume 2, Issue 2, page 7 – 18
Publication Date: March 4, 2019

Galal A.R., El-Sherbeny, Bahaa A., Zarea, Abdelsabour, G.A. Khaled and M.R.A., Hovny
Sorghum Res. Dep., FCRI, ARC
Genetics Dep., Faculty of Agri., Sohag University

Journal Full Text PDF: Morphological – SRAP Molecular Markers Association in Grain Sorghum Genotypes.

For assessment the level of genetic diversity and marker – trait association in ten grain sorghum genotypes (five maintainers and five restorer) by using eleven selected SRAP primer combinations. The Results showed that the percent of polymorphism (%P) of SRAP molecular markers were between 25.00% and 83.33% with an average of 46.49%. The Polymorphism information content (PIC) values varied from 0.00 to 0.36 with an average of 0.17. Moreover, the marker index (MI) values ranged from 0.00 to 1.80 with an average of 0.62 for the primer combinations ME7F-EM9R and ME9F-EM10R, respectively. In this trend the results revealed that the resolving power (Rp) varied from 0.00 to 3.20 with an average of 1.44. Single-marker analysis (SMA) indicated that four of the SRAP markers identified in this study showed significant association with 6 traits viz., days to 50% flowering (Fl), panicle length (PL), panicle width (PW), No. of green leaves (No. of GL), leaf area/plant (LA/P) and 1000 grain weight (1000 GW). The cluster analysis based on SRAP and means of morphological data showed similarity coefficient values ranged from 66.70 % to 84.30% with an average of 75.50 %. The Mantel test revealed, there was non significant negative correlation between the genetic distances based on phenotypic data and the similarity data based on SRAP markers, (r= – 0.18, P≥ 0.05)..

Keywords: Sorghum, SRAP, Polymorphism, Cluster Tree.

1. Introduction
Sorghum is the fifth most important cereal crop in the world after wheat, rice, maize and barley. It is a C4 photosynthesis crop adapted to semi-arid tropical environments that are too dry for cultivation of other cereals like maize (Dogget, 1988; Reddy et al., 2007). Sorghum is a major staple food and fodder crop and is considered as a pillar of food security in these regions around the world (Aluko and Olugbemi, 1990). Due to its versatile use, drought hardiness, stability of yield and adaptability over wide range of climates, sorghum has maintained its importance and dependability.
Sorghum breeders across the world are working on the development of high-yielding varieties and hybrids with better quality, disease resistance, drought tolerance and good agronomic traits (Kholováa et al., 2013).
The use of morphological traits in plants as markers for determining the genetic relationship dates back many years (Sory et al., 2017). However the morphological markers are highly influenced by the environmental conditions, therefore there is a need to supplement or compliment their clustering with molecular markers data.
When using both the morphological and molecular markers data two types of hierarchical classification are carried out independently (Li and Quiros, 2001). This enables genotypes to be clustered into groups that are as homogenous as possible.
The use of molecular markers like sequence related amplified polymorphism (SRAP), has proven to be a very good tool in assessing the genetic relatedness of different species, and many types of markers have been used in grain sorghum (Khatab et al., 2017) and in sweet sorghum (Jinwang et al., 2018). For that this work aims to evaluate the performances of sorghum genotypes, the level of polymorphism and assessment the genetic diversity existing marker that could be related or linked to the morphological traits, then identify potentially marker for marker assisted selection using SRAP molecular markers.

2. Materials and Methods:
2.1. Plant materials:
Ten 10 grain sorghum genotypes viz., five fertility restorer lines (R-lines) and five cytoplasmic male sterile (CMS) lines were used in this study (Table 1). These genotypes were kindly provided by Sorghum Research Department, Field Crop Research Institute, Agricultural Research Center, Giza, Egypt.

2.2. Agronomic traits:
The ten genotypes (five maintainer and five restorer lines) were sown on 27th June, 2017 at Shandaweel Research Station in a Randomized Complete Blocks Designs (RCBD) with three replications. Each block contained 10 plots, and each plot contained 3 rows, 4 m long, 60 cm apart and 15 cm. between hills within a row. After full emergence the seedlings were thinned to two plants per hill before the first irrigation, three weeks after sowing. The agricultural practices were followed as recommended throughout the growing season.
The following agronomic traits were recorded as the mean of ten random guarded plants in each plot; days from sowing to 50% flowering (days), plant height (cm), number of green leaves, leaf area/plant, panicle length, panicle width, 1000-grain weight (g) and grain yield/plant (g).

2.3. Genotypes evaluation:
Data was subjected to analysis of variance in a randomized complete block design (RCBD) according to Gomez and Gomez (1984). The mean squares of genotypes and replications for all studied traits were tested for significance according to the F-test (data not shown). The forms of the analysis of variance were as outlined by Cochran and Cox (1957).

2.4. DNA extraction and PCR procedures:
Fresh young leaves of ten sorghum genotypes were harvested and immediately ground in extraction buffer using cetyltrimethyl ammonium bromide (CTAB) protocol as described by Porebski et al. (1997), at molecular genetics Laboratory, Genetics Department, Faculty of Agriculture, Sohag University. For each genotype, 0.2 of ground leaf tissue was suspended in 2 ml of extraction buffer (20 mM of EDTA, 0.1 M of Tris-HCL, 1.4 of Nacl, 2% CTAB, 1% of PVP). The DNA pellet was then suspended in 100 µl of TE buffer. Genomic DNA was diluted 10-fold in water prior to 35 cycles of PCR amplification.
The PCR assays were performed for SRAP markers in a 20µl volume containing 0.2µl of Go Taq polymerase, 3.5µl of primer (8 pmol), 4µl 5X green buffer, 2µl Mgcl2, 2µl dNTPs (2.5mM), 5.3µl of free nuclease water and 3µl (150-200 ng) of genomic DNA templates. The thermal Cycler 96-Labmet (USA) was programmed by: 1 cycle (an initial denaturing step) of 5 min at 95 °C, 40 cycles of 30 sec at 95 °C (denaturation step), 30 sec at 49 °C to 56 °C (annealing step, optimized for each primer), 2 min 30 sec at 72 °C (elongation step) and 10 min at 72 °C (final extension), then kept at 20 °C. The amplified products were electrophoresed in a 1-1.5% agarose gel stained with 0.2 µl ethidium bromide. The amplified fragments were visualized and photographed using UVP Bio Doc-It imaging system (USA) Sambrook et al. (1989). SRAP technique was conducted using 10 primer combinations (Table 2).

2.5. Data of molecular markers analysis:
The DNA banding patterns generated by SRAP technique were analyzed by computer programme Gene Profiler software (version 4.03). The presence (1) or absence (0) of each band was recorded for each genotype for all studied primers. Genetic distance was estimated according to Jaccard (1908). To measure the in-formativeness of the SRAP markers in differentiating among sorghum genotypes, the polymorphic information content (PIC) was calculated according to the formula of Ghislain et al. (1999) as PIC= 1- [(p) 2 + (q)2] where p is the frequency of allele band present and q is frequency of allele band absent across the tested genotypes.
The marker index (MI) was calculated for each SRAP primer combination as MI = PIC x ηβ, where PIC is the mean PIC value, η the number of bands, and β is the proportion of polymorphism Powell et al. (1996). Analysis of variance (ANOVA) was conducted using the 0–1 data. The association analysis was conducted using simple linear regression. For this, data on individual phenotypic traits were regressed on whole 0-1 binary marker data for each individual phenotypic marker using Excel programme. The coefficient of determination (R2) was calculated as R2 = 1- (SSE/SST), where SSE is the sum of squares of error and SST is the total sum of squares.