A limit to plant breeding has been due to the lack of robust markers such as molecular markers, previous work was based on pedigree data, morphological, physiological and cytological measurements (Garcia et al., 2004). The advent of molecular markers has meant that plant breeders could estimate genetic diversity faster and easier. Since different marker types differ in their properties, it is possible they give different estimates of genetic diversity (Rauf et al., 2010). The comparison of molecular markers for estimating genetic diversity could show how useful a marker is for a plant breeding purpose(Franco et al., 2001). The estimates of genetic diversity of makers can be done based on correlation, regression, scatter plots and cluster analysis (Weir, 1996).
The efficiency and utility of six primer combinations for AFLP and RAPD, 100 RFLP and 36 SSR markers were investigated in12 soybean germplasm by Powell et al., (1996.). The study consisted of a total of 12 genotypes of Glycine max of which 2 are wild type Glycine soja, the similarity matrices for the markers were compared, it revealed that the average similarity matrix was lower for SSR(0.341) while the other markers were similar AFLP (0.655), RFLP (0.639), RAPD (0.664). The Mantel test was used to determine the correlation between the markers and found significant correlation between all maker types (P<0.001). The highest correlation was between SSR and AFLP (0.855) while the lowest was recorded between RAPD and RFLP (0.744). Both markers proved to be useful in the assessment of the selected genotypes.
Lu et al., (1996) compared PCR based methods (RAPDs, AFLP, microsatellites- AFLP, and inter-SSR) with DNA based RFLP to determine the most informative, and useful in genetic diversity studies based on ten pea genotypes. Their results revealed that the PCR based method were more informative than RFLP, and trees
29 derived from PCR based markers were significantly correlated with the exception of inter-SSR derived tree.
Other studies for the comparison of markers were conducted in other crops as well, Pejic et al.,(1998) investigated the efficiency of RAPD, SSR and AFLP in the analysis of maize, inbred lines. Garcia et al., (2004), compared the utility of RAPDs, RFLP, AFLP and SSR markers to find the best marker suitable for maize inbred lines selection. In wheat, Stodart et al., (2005) compared AFLP and SSR markers to determine their utility in genetic diversity measurements among the 44 bread wheat landraces from different regions.
The geneticdistanceestimates compared in leguminous crops, include the one from Maras et al., (2008) who evaluated the ability of AFLP and SSR to detect genetic diversity among 29 common bean (Phaseolus vulgaris) accessions. Ten primer combinations of AFLP produced 112 polymorphic bands, while 14 SSR markers produced 100 polymorphic bands and both markers were able to separate the two gene pools of Andean and Mesoamerican origin. Jaccard coefficient of similarity was employed to generate similarity matrix in both markers, the two genetic distances GSAFLP and GSSSR were evaluated for correlation using the Mantel
correspondence test (Mantel, 1967), and a significant correlation r =0.67 was found, which shows a good similarity between the two markers.
In comparison of the morphological and RAPDs markers in estimating the differences among 15 common beans (Phaseolus vulgaris), Dursun et al., (2010), employed 8 RAPDs and 16 morpho-agronomic markers. The difference between the two markers was revealed in the displaying of clusters as they differed in topology. The Euclidean matrix produced by the morphological marker and the Dice similarity matrix from the RAPDs markers were compared using Mantel matrix correspondence tests, the results showed no correlation between the two markers. This lack of correlation was thought to be possibly incorrect measurements for morphological traits and few samples sizes for RAPDs used in the study (Dursun et al., 2010). However, in most of the studies conducted to reveal the genetic distances estimates the relationship between molecular and morphological markers had been observed to show non-significant correlations (Burstin and Charcosset, 1997).
30 No study has been conducted to compare the genetic distance estimates of markers in the germplasm of bambara groundnut. Therefore this study aims to determine the genetic diversity among the selected bambara groundnut germplasm employing both morpho-agronomic (qualitative and quantitative) markers, and molecular markers, and determine the relationship between the two techniques. 1.9.1 The objectives of the study
To develop and characterise microsatellites markers; the development of
markers will have a major impact on the genetic analysis and breeding of bambara groundnut, particularly in genetic diversity, population structure analysis implementation of pure line selection.
To characterise selected landraces based on morpho-agronomic characters
and to determine the agro-morphological diversity among landrace and consequently produce a genetic distances estimate to correlate with the genetic distance estimates based on SSR.
To conduct a genetic diversity estimate based on SSR markers, which will
consequently produce a genetic distance estimates to correlate with the morphological marker distance estimates.
To compare morpho-agronomic markers with the SSR markers and
identify any significant correlations and evaluate which is more informative and whether the costs associated with molecular analysis are justified.
To establish the genetic similarity among bambara groundnut landraces
sampled across a vast area of sub-Saharan Africa using microsatellites (SSR) markers since there is little information about this germplasm. There is constant movement of bambara groundnut germplasm between various neighbouring countries, and among farmers within the same country.
The existence of landraces in bambara groundnut means that there are
likely to be multiple genotypes planted in any trial for a landrace. This will add genetic variability to the already existing environmental variability and interaction (i.e. VP = VG + EG + VGXE). Co-dominant microsatellite
markers will allow us to determine whether this is more of a problem in some landraces than others
31