Although the altered alleles are not always expressed due to recessive and dominant allele phenomena, the variation taken place will have an impact in the future. These variations would be important to adapt the demands of the changing environmental conditions or be helpful to survive the natural selection. Genetic diversity could be defined as the variations within and among species in the form of the genetic makeup.
There are two important points to comprehend about the term; one is that it relates with genetic material, and the other is that it could be related with either one species or more than that.
Genetic diversity is considered the baseline level of biodiversity. Diversity is a combination of both variety and variability; the genetic diversity serves all the species to be adaptable for the challenging environmental demands. The potential for a particular species to change is highly valuable over the differing demands of the environment; indeed, the environment has always been changing over time.
The dinosaurs could not stand up to the demands after the meteorite has struck the Earth, and went extinct. If there were enough genetic diversity and time to adapt to the conditions as the mammals did, dinosaurs would still be on this planet. Smith et al. Where, d a,b is the Euclidean distance between genotype a and b; X1 and X2 are the values for ith trait for inbred lines a and b and Var X i is the variance for ith trait over all inbred.
Where, d a,b is the Euclidean distance between genotype a and b; Xai is the frequency of the allele a for individual i ; Xaj is the frequency of the allele a for individual j and r is the constant based on coefficient used. In such cases, data is used to generate binary matrix for further analysis. Allelic diversity can be described by i the percentage of polymorphic loci p , ii mean number of alleles per locus n , iii total gene diversity or average expected heterozygosity H , and iv polymorphism information content PIC.
Percentage of polymorphic loci p gives an estimate of number of polymorphic loci with respect to total loci including polymorphic and monomorphic loci and can be expressed as:. Where, Np is the number of polymorphic loci and Nt is the number of total loci polymorphic and monomorphic. Mean number of alleles per locus n is calculated by dividing total number of alleles by the number of loci and can be expressed as:. Polymorphism information content PIC is an indirect estimate of number of alleles per locus.
This can be expressed as below:. These techniques have a very sound theoretical basis to provide most reliable information regarding the real genetic distances between genotypes and thus can be used for assessment of genetic diversity.
Some of the multivariate techniques being used are detailed below:. This method represents each genotype by a circle of fixed radius called glyph with rays emanating from its periphery. Each variable is assigned a position on the glyph. The length of the ray represents index score of the variate. This method uses a range of variations arising from trait such that extent of trait variation is determined by the length of rays on the glyph.
The performance of a genotype is adjudged by the value of the index score of that genotype. The score value determines the length of ray which may be small, medium or long. D2 values are estimated by transforming correlated variables into uncorrelated variables using pivotal condensation method. In general, the Mahalanobis distance is a measure of distance between two points in the space defined by two or more correlated variables. For example, if there are two variables that are uncorrelated, then we could plot points in a standard two-dimensional scatterplot; the Mahalanobis distances between the points would then be identical to the Euclidean distance.
If there are three uncorrelated variables, we could also simply use a ruler in a 3-D plot to determine the distances between points. If there are more than 3 variables, we cannot represent the distances in a plot any more. In those cases, the simple Euclidean distance is not an appropriate measure, while the Mahalanobis distance will adequately account for the correlations.
This analysis assumes discontinuities within the data. It depicts the pattern of relatedness between genotypes based on evolutionary relationships or phenotypic performance.
UPGMA and UPGMC provide more accurate grouping information on breeding materials used in accordance with pedigrees and calculated results found most consistent with known heterotic groups than the other clusters. Principal components analysis PCA can be defined as a data reduction technique applicable to quantitative type of data.
PCA transforms multi-correlated variables into another set of uncorrelated variables for further study. These new set of variables are linear combinations of original variables.
It is based on the development of eigen-values and mutually independent eigen-vectors principal components ranked in descending order of variance size. Such components give scatter plots of observations with optimal properties to study the underlying variability and correlation. Suppose x1, x2,……. This technique is not an end rather a mean for further analysis. This technique does not require any statistical model or assumption about distribution of original variate.
It is worth mentioning that when original variables are uncorrelated then there is no need to carry out this analysis. This is most suitable when different variables have same unit. The difficulty of different scales can be avoided by standardizing all the variables. Standardization is done by dividing each variable by its estimated standard deviation. Recently, a spurt has been reported in the use of PCA in genetic diversity studies.
It is another ordination method, somewhat similar to PCA, was developed by Schoenberg. It produces a 2 or 3 dimensional scatter plot of the samples such that the distances among the samples in the plot reflect the genetic distances among them with a minimum of distortion.
This suffers from the disadvantages of i not providing a direct link between the components and the original variables and ii being complex functions of the original variables. Bartlett 44 , 45 was the first to give the idea of canonical analysis. It assumes additivity in all characters and improves prediction by eliminating linear correlations between characters.
Hotelling 46 , 47 proposed the technique to describe the dependencies between two sets of variants. This method has the advantage of being neutral to scale. Further, comparison of group of variables is easier when compared to that in PCA.
This technique reduces data into smaller meaningful groups based on their inter-correlations or shared variance. It is based on the assumption that correlated variables variables measure a similar factor or trait.
It is used to describe the covariance relationships among many variables in terms of few underlying random quantities called factors. The main goal of factor analysis is to explain as much variance as possible in a data set by using the smallest number of factors and the smallest amount of items or variables within each factor.
For interpretation of analysis, the factors with Eigen values greater than 1. Correspondence analysis CA is an ordination method, somewhat similar to PCA, but for counted or discrete data.
It uses Chi-square distance between the objects under study. Correspondence analysis can compare associations containing counts of taxa or counted taxa across associations.
Different methods of genetic diversity analysis have been found to give similar results and hence can be used interchangeably. Chandra 49 compared two methods Mahalanobis D2 distance and Metroglyph analysis and found strikingly similarity in grouping pattern of flax genotypes.
On this basis, he suggested that metroglyph analysis can be used for preliminary grouping in large number of germplasms. Ariyo 50 compared the extent of genetic diversity in okra using factor, principal component and canonical analysis and found similar results between factor and principal component analysis.
Many types of software have been developed for analyzing genetic diversity. Most of these softwares are based on multivariate statistics. Most of the softwares are freely available on internet and suitable for PCs. Tanavar et al. Some of the softwares are briefed below:. SAS offers the package for different multivariate techniques. It involves canonical correlation, correspondence analysis, cluster analysis, factor analysis, principal component analysis etc. Apart from other modules, it is also capable of carrying out multivariate statistics.
Paleontological Statistics software was developed by Hammer et al. Functions found in PAST include parsimony analysis with cladogram plotting, detrended correspondence analysis, principal component analysis, principal coordinates analysis, time-series analysis, geometrical analysis etc. It is a popular program used to analyze genetic diversity from molecular marker data and has been used in different areas of science. It is based on similiarity indices and works on 0, 1 matrix of genotypic data.
It is used for several applications namely cluster analysis, principal component analysis, principal coordinate analysis, etc. It is an Excel-based and user-friendly program. It accepts three types data viz. It is another user-friendly package for the analysis of genetic diversity among and within natural populations.
It accepts three types of data viz. The analysis include gene frequency, allele number, effective allele number, polymorphic loci, gene diversity, Shannon index, homozygosity test, F-statstics, gene flow, genetic distance based on Nei cofficent and dendrogram based on UPGMA and neighbor-joining method and neutrality.
It is a new program, with the first official version released in January Data can be imported from Excel or other formats, making data set-up very easy. Available options include summary statistics allele number, gene diversity, inbreeding coefficient; estimation of allelic, genotypic and haplotypic frequency; Hardy-Weinberg disequilibrium and linkage disequilibrium , population structure, phylogenetic analysis, association analysis and tools Utility tools such as SNP simulation and identification, Mantel test and exact p-values for contingency tables.
Gene banks across the world maintain a large number of germplasm about 6 million of important crop plants. This is because of lopsided approach of plant breeding aiming at only few important traits contributing towards yield at the cost of other traits. Many other germplasm accessions possessing diverse traits remain unutilized. This leads to narrow genetic base of crop varieties leading to genetic vulnerability which may be devastating in context of changing climatic conditions.
Increased mechanization in agriculture has paved the way for monoculture over a large tract of land. Apart, destruction of natural habitats in the name of urbanization and modernization has reduced the scope of generating natural variation in the form of wild forms and wild relatives of crop plants. This has resulted in yield plateau and susceptibility of these varieties to different biotic and abiotic stresses. Genetic diversity in form of different landraces and germplasm serve as the source of important genes like for biotic and abiotic stresses.
Plant breeding is facing challenge to feed the ever increasing population with diminishing cultivable land. Modern plant breeding has achieved some success in this regard. However, it has resulted in the genetic vulnerability because of narrow genetic base of cultivated varieties in many crops. Hence, there is a need of paradigm shift in plant breeding focussing on diverse genetic resources. Genetic diversity has now been acknowledged as a specific area that can contribute in food and nutritional security.
Better understanding of genetic diversity will help in determining what to conserve as well as where to conserve. Genetic diversity of crop plants is the foundation for the sustainable development of new varieties. So there is a need to characterize the diverse genetic resources using different statistical tools and utilize them in the breeding programme.
Morphological data in conjunction with molecular data are used for precise characterisation of germplasm resources. With the advent of high throughput molecular marker technologies it is possible to characterize larger number of germplasm with limited time and resources. The analysis is based on statistical tools for better interpretation. The most used statistical tools for morphological data are D2 statistics and PCA because of their easy interpretation.
PCoA is very much in use for molecular diversity analysis. The diversity indicated by different analysis can further be utilized in heterosis breeding, transgressive breeding and interogression of alien genes for specific traits. This is an open access article distributed under the terms of the, which permits unrestricted use, distribution, and build upon your work non-commercially. Withdrawal Guidlines. Publication Ethics.
Withdrawal Policies Publication Ethics. Advances in. Review Article Volume 7 Issue 3. Figure 1 Hierarchy of diversity. Morphological markers These analyses are carried out by raising germplasm lines, purelines, improved varieties etc. Geneticd Genetic distance was first defined by Nei 36 as the difference between two entities that can be described by allelic variation.
Cluster analysis This analysis assumes discontinuities within the data. Canonical analysis Bartlett 44 , 45 was the first to give the idea of canonical analysis. Most organisms that reproduce sexually have two copies of each gene , because each parent cell or organism donates a single copy of its genes to its offspring.
Additionally, genes can exist in slightly different forms, called alleles, which further adds to genetic variation. The combination of alleles of a gene that an individual receives from both parents determines what biologists call the genotype for a particular trait, such as hair texture. The genotype that an individual possesses for a trait, in turn, determines the phenotype —the observable characteristics—such as whether that individual actually ends up with straight, wavy, or curly hair.
Genetic variation within a species can result from a few different sources. Mutations, the changes in the sequences of genes in DNA , are one source of genetic variation. Another source is gene flow , or the movement of genes between different groups of organisms.
Finally, genetic variation can be a result of sexual reproduction , which leads to the creation of new combinations of genes. Genetic variation in a group of organisms enables some organisms to survive better than others in the environment in which they live.
Organisms of even a small population can differ strikingly in terms of how well suited they are for life in a certain environment.
An example would be moths of the same species with different color wings. Moths with wings similar to the color of tree bark are better able to camouflage themselves than moths of a different color. As a result, the tree-colored moths are more likely to survive, reproduce, and pass on their genes.
This process is called natural selection , and it is the main force that drives evolution. The audio, illustrations, photos, and videos are credited beneath the media asset, except for promotional images, which generally link to another page that contains the media credit. The Rights Holder for media is the person or group credited.
Tyson Brown, National Geographic Society. National Geographic Society.
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