D2 analysis & it's Interpretation
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- The document discusses D2 analysis, a technique used to assess genetic diversity among plant genotypes. - D2 analysis involves calculating distances between genotypes based on multiple quantitative traits and clustering genotypes based on these distances. - The document provides details on the steps involved in D2 analysis, including data collection, calculation of variances/covariances, computation of D2 values, clustering genotypes, and interpretation. - An example application of D2 analysis to assess genetic diversity among litchi hybrids is described. Five clusters were identified among 18 hybrids based on quantitative traits.
D2 analysis & it's Interpretation
- 1. DEPARTMENT OF GENETICS AND PLANT BREEDING Course No :- GP-504 Course Title :- PRINCIPLES OF QUANTITATIVE GENETICS Assignment On :- D2 Analysis & it’s Interpretation D2 Analysis & it’s Interpretation Submitted to :- Dr. T.HARITHA Associate Professor Dept of GPBR Submitted by :- P.TEJASREE BAM-20-27 M.Sc 1st Year Dept of GPBR
- 2. D2 Analysis – Procedure – Merits and Demerits & Interpretation
- 3. Genetic Variability: The amount of variation present among the members of a population or species, may have reference to one or more characters and at genotypic or phenotypic levels Genetic Diversity: The variability present among different genotypes of a species Variance: Average of the squares of deviations of the observations of a sample from the mean of the sample drawn from a population
- 4. The variability present among different genotypes of a species is known as Genetic Diversity Geographical separation Genetic barriers to crossability Variability differs from Diversity Variability - Phenotypic differences, Diversity - May or may not have such an expression
- 5. D2 technique has been used for assessing variability present in crops like maize, sorghum, pearl millet, wheat, linseed, cotton, tobacco etc. However, the maximum heterosis generally occurs at an optimal or intermediate level of diversity Genetic diversity plays an important role in plant breeding because hybrids between lines of diverse origin generally displays a greater heterosis than those between closely related strains
- 6. ‣ Multivariate Analysis ‣ A measure of group distance based on multiple characters ‣ Originally developed by P. C. Mahalanobis in 1928 ‣ He used this technique in the study of Anthropometry and Psychometry - Chinese head measurement ‣ C. R. Rao (1952) suggested the application of this technique for the assessment of genetic diversity in plant breeding – “Advanced Statistical Methods in Biometrical Research” ‣ Model to determine divergence among populations in terms of generalized group distance
- 7. Main Features of D2 Analysis →This is a numerical approach which is used for measuring genetic divergence, in the germplasm collections →Estimates of D2 statistics are based on second order statistics and therefore, have lesser precisions than metroglyph analysis →Analysis is more difficult than metroglyph analysis →Analysis is possible from replicated data only →Genetic diversity is depicted by cluster diagram →D2 statistics aids the selection of divergent parents for hybridization →D2 statistics measures the degree of diversification →D2 statistics determines the relative proportion of each component character to the total divergence
- 8. D2 Analysis →Analysis of D2 statistics requires replicated data →Multilocational or multiseasonal data provide more reliable results Main Steps in D2 Analysis: 1. Selection of Genotypes 2. Evaluation of Material 3. Biometrical Analysis
- 9. 1. Selection of genotypes: ‣ The materials which has to be evaluated for genetic diversity may include germplasm lines, strains ‣ The genotypes are generally selected on the basis of phenotypic variability or geographical origin 2. Evaluation of Material: ‣ The selected genotypes are evaluated in replicated field trial and observations are recorded on quantitative characters Procedure
- 10. 3. Biometrical Analysis: † Estimation of variances and covariances for the characters under study † Transformation of correlated variables – pivotal condensation method. † Computation of D2 values as per Mahalanobis (1928) and Testing their significance - against the table value of chi-square for p degrees of freedom. (p –total no. of characters) † Grouping of different genotypes into various clusters † Tocher's method † Canonical roots method † Estimation of average distance at i) intra – cluster level ii) inter – cluster level † Construction of cluster diagram † Finding out the contribution of individual character towards total divergence
- 11. Cluster Diagram ♠ It is the line diagram constructed with the help of D2 values ♠ The square roots of average intra and inter cluster D2 values are used in the construction of cluster diagram
- 12. Cluster Diagram ‣ This depicts the genetic diversity in a easily understandable manner ‣ The number of clusters represent the number of groups in which a population can be classified on the basis of D2 analysis ‣ The distance between two clusters is the measure of degree of diversification – (Greater distance between two clusters – Greater the divergence and vice versa) ‣ The clusters which are separated by the greatest statistical distance show the maximum divergence ‣ The genotypes grouped together in a single cluster are less divergent than the one’s which are placed in different clusters ‣ It provides information about relationship between various clusters
- 13. D2 Statistics While selecting the parents on the basis of D2 statistic the points to be considered are; 1. The relative contribution of each character to the total divergence 2. The choice of clusters with the maximum statistical distance 3. The selection of one or two genotypes from such clusters Other characters, like disease resistance, earliness, quality etc., should also be considered. Crossing of the lines thus selected in a diallel fashion may yield some useful segregants.
- 14. Merits and Demerits ‣ Merits: ‣ It helps in the selection of genetically divergent parents for their exploitation in hybridization programme ‣ It measures the degree of diversification and determines the relative proportion / contribution of each component character to the total divergence ‣ The forces of differentiation are measured at two levels i.e., intra-cluster and inter-cluster levels ‣ This technique provides reliable estimates of genetic divergence ‣ Large number of germplasm lines can be evaluated at a time for genetic diversity by this technique ‣ Demerits: ‣ The analysis is difficult as it involves variances and covariances ‣ The estimates are not statistically very robust as they are based on second order statistics ‣ The analysis is not possible from unreplicated data
- 15. Similarities and Dissimilarities between Metroglyph Analysis and D2 Statistics Similarities: ⁕Both are classificatory techniques ⁕Both of them provide measure of variability and genetic diversity ⁕Both the techniques are free from genetic assumptions Dissimilarities: S. No. Particulars Metroglyph Analysis D2 Statistics 1. Statistics involved First order Second order 2. Analysis Simple Difficult 3. Analysis is possible from Un-replicated data also Replicated data 4. Type of Approach Semi-graphic Numerical 5. Diagram used Metroglyph chart Cluster diagram
- 18. ‣ D2 Statistic Techniques used for Analysis of Genetic Divergence among Litchi Hybrids ‣ The current study on the D2 -based divergence analysis using Euclidian and Tocher’s methods indicated the existence of appreciable amount of genetic diversity in the eighteen hybrids of litchi. ‣ Test hybrids were grouped into five clusters based on different quantitative traits using above methods with variable number of entries in each cluster. ‣ In Euclidian’s method, cluster 1 had maximum number of hybrids (7) followed by cluster 3 (4), 2 (3), 4 (2) and 5 (2), while in Tocher’s method, cluster I had utmost number of hybrids (10), while the cluster III, IV and V contained the least (1). ‣ Maximum inter cluster distance indicate that hybrids falling in these clusters had wide diversity and can be used for improvement programme to get better recombinants in the segregating generations. ‣ The hybrids H-400, H-515 and H-711 recognized good for genetic improvement in litchi hybrids for commercial purposes
- 19. • twenty quantitative and qualitative characters viz., fruit weight (FW), aril weight (AW), seed weight (SW), peel weight (PW), fruit length (FL), fruit diameter (FD), aril percentage (A%), seed percentage (S%), peel percentage (P%), aril seed ratio (A/S), fruit length width ratio (FL/FW), inflorescence length (IL), inflorescence width (IW), leaflet-length (LL), leaflet-width (LW), leaf let length width ratio (LL/LW), number of leaflets leaf-1 (LPL), total soluble solids (TSS) • Mahalanobis D2 statistic was used for assessing the genotypic variance among the populations (Mahalanobis, 1936). The generalized distance between any two populations is given by formula: D2 = Σσλijσaiσaj
- 20. • The D2 values were calculated as the sum of squares of the differences between pairs of corresponding uncorrelated (gs) values of any two uncorrelated genotype of D2 value. All n (n-1)/2 D2 value were clustered using Toucher’s method described by Rao (1952). The intra cluster distances were calculated by the formula given by Singh and Choudhary (1997): • Square of the inter cluster distance = ΣD2 i /n • Square of the intra cluster distance = ΣD2 i/ninj
- 26. Result and Conclusion ‣ Cluster 1 was characterized by maximum aril weight (11.44) and low mean value for leaf-blade length (10.19) and total soluble sugar (17.98) ‣ Maximum mean value for inflorescence length (26.94), inflorescence width (18.22), leaf-blade length and width ratio (4.94) and total soluble sugar (21.33) whereas minimum mean value for seed percentage (12.80) and no. of leaflets per leaf (6.89) were represented by cluster 2. ‣ Cluster 3 was characterized by highest mean value for fruit weight (18.07), peel weight (3.87), fruit diameters (31.28) and peel percentage (21.61) and least value for aril seed ratio acidity (2.89) and leaf-blade length and width ratio (2.81). ‣ Cluster 4 was characterized by maximum mean value for seed weight (3.13), fruit length (35.44), seed percentage (17.37), fruit length and width ratio (1.16), TSS and acid ratio (74.17) and least mean value for aril percentage (58.58), aril seed ratio (2.89), inflorescence length (17.42) and titrable acidity (0.26).
- 27. Result and Conclusion ‣ Cluster 5 was characterized by maximum mean value for aril percentage (68.22), aril seed ratio and acidity (4.45), leafblade length (17.75), no. of leaflets per leaf (7.33), titrable acidity (0.72) whereas minimum mean value for fruit weight (13.20), aril weight (9.04), seed weight (1.78), peel weight (2.03), fruit length (29.67), fruit diameter (28.20), peel percentage (15.53), fruit length and width ratio (1.05), inflorescence width (8.54) and TSS and acid ratio (25.62) ‣ In above investigation, the hybrids H-400, H515 and H-711 recognized best on the basis of both Euclidian’s as well as Tocher’s methods to contribute in future breeding efforts aimed at genetic improvement in litchi hybrids for commercial achievement of production.
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