**8. Conclusion**

344 Genetic Diversity in Plants

frequencies can be calculated and the genetic distance between i and j individuals estimated

d(ij) 1 ( )

Where Xai is frequency of the allele a for individual I, and n is the number of alleles per loci; r is the constant based on the coefficient used. In its simple form, i.e *r* = 1, genetic distance

> d (ij) 1/2 ( ) *n*

Where r = 2, d(*i,j)* is referred to as Rogers (1972) measure of distance (RD), where

RDij = I/2[ ( xai-xaj) 2]1/2 Where allele frequencies are to be calculated for some of the molecular markers, the data must first generate a binary matrix for statistical analysis. Binary data has been long and widely used before the advent of molecular marker data to measure genetic distance by Rogers (1972); Nei and Chesser (1983) coefficient and known as GDMR and GDNL

In the use of any given statistical formula to determine genetic diversity in molecular based data, one specific problem usually encountered is the failure of some genotypes to show amplification for some primer pairs. Robinson and Harris (1999) noted that lack of amplification may be due to "null alleles". Most often, it is difficult to ascribe lack of amplification to "null allele". It is therefore the reposed confidence of the researcher, that a "null allele" status of a genotype will not be considered as missing data during computation of genetic similarity- distance matrix so as to avoid gross error during result interpretation. DNA based marker data have been successfully used to measure genetic distance in some crops (Pritchard *et al.* (2000) in pigeon pea; Beaumont *et al.* (1998) in wheat; Franco *et al.*,

Genetic relationship among and with breeding materials can be identified and classified using multivariate grouping methods. The use of established multivariate statistical algorithms is important in classifying breeding materials from germplasm, accessions, lines, and other races into distinct and variable groups depending on genotype performance. Such groups can be resistant to diseases, earliness in maturity, reduced canopy drought resistant etc. The widely used techniques irrespective of the data source (morphological, biochemical and molecular marker data) are cluster analysis, Principal Component Analysis (PCA), Principal Coordinate

Cluster analysis presents patterns of relationships between genotypes and hierarchical mutually exclusive grouping such that similar descriptions are mathematically gathered

1

*n r X X ai aj* 

*X X ai aj*

as follows.

can be calculated as:

respectively.

(2001) in maize; Dje *et al.* (2000) in Sorghum.

**7. Grouping techniques in measuring genetic diversity** 

Analysis (PCOA) Canonical Correlation and Multidimensional Scaling (MDS).

Genetic diversity studies is in no measure the first basic step in meaningful breeding programme and therefore require accurate and reliable means for estimation. Data sets sourced can morphological biochemical several workers successfully utilized various statistical tools in analysis diverse data sets and identified two major framework to really explain divergence in genotype performance. Genetic distance among and within individual data sets can be conveniently determined using specific tools while classificatory and cluster analysis require principal component and polymorphic sequence tools. Since each data set provide different molecular type of information, based marker data set is visualized to provide more reliable differentiate information on the genotypes. Analysis of data sets can be complex. Many software packages are available. There is still a need for a comprehensive and user-friendly software packages that would integrate different data set for analysis and generate reliable and useable information about genetic relationship. Equally important in genetic diversity studies is the need for a genetic resource centre. Studies should incorporate utilization of genetic diversity information in developing genetic resource centre accessible to breeders.
