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- 1. GENETIC DIVERSITY ANALYSIS Presenting to you by: AKHISHA P. A. BAM-15-57 17.02.2016 1
- 2. GGENETIC DIVERSITY ANALYSIS Variability and its sources Features of polygenic traits Types of polygenic variation Methods of assessment of variability 2
- 3. What is variability?? • Presence of differences among the individuals of plant population • Due to differences in genetic constitution • Due to differences in environment • Essential for resistance to biotic and abiotic factors and adaptability 3
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- 6. Sources of variability • Spontaneous mutations • Natural outcrossing • Recombination Measures of conservation • Global gene pool • Deliberate use of heterogeneous populations • Use of multiline varieties 6
- 7. Features of polygenic traits • Continuous variation • Small and undetectable effect of individual gene • Several genes involved • No possibility of grouping into distinct classes • High effect of environment • Analysed based on mean, variance and covariance • Possibility of metric measurements • Low stability 7
- 8. Types of polygenic variation 1. Phenotypic variation: • Observable; Genotypic + environmental; Measured as phenotypic variance 2. Genotypic variation: • Inherent; Unaltered by environment; Measured as genotypic variance 3. Environmental variation: • Non heritable; uncontrolled; measured as error mean variance 8
- 9. Assessment of polygenic variation • Requires metric measurements • Observes several individuals and mean values are used in studies • Uses mean, variance, covariance etc. from replications 9
- 10. Methods of assessment of variability i. Simple measures of variability ii. Variance component analysis iii. Metroglyph analysis iv. D² statistic 10
- 11. i) Simple measures of variability • Range, standard deviation, variance, standard error, coefficient of variation • ANOVA provides estimates of CV% 11 PCV=√VP/X̅ x 100 GCV=√VG/X̅ x 100 ECV=√VE/X̅ x 100
- 12. • GCV>PCV : little influence of environment, selection will be rewarding • PCV>GCV : apparent influence of environment, selection may be misleading • ECV>PCV&GCV : significant influence of environment, selection will be ineffective 12
- 13. Variance component analysis • Crossing of a number of genotypes in a definite fashion • Evaluation of progenies in replicated trials • Diallel, partial diallel, line X tester, generation mean analysis etc. are used. 13
- 14. Metroglyph analysis • Semi-graphic method • Assess pattern of morphological variation in a large number of germplasm lines taken at a time • Developed by E Anderson in 1957 14
- 15. • Main features are: Analysed based on first order statistics, hence more reliable and robust Simple analysis Possible from replicated and non replicated data Depicts pattern of variability by glyph on the graph 15
- 16. Main steps 1. Selection of genotypes: germplasm lines, strains, varieties and hybrids; based on phenotypic or geographical differences 2. Evaluation of material: in replicated trials; observations on each trait are recorded; mean values over replications for each trait are worked out and tabulated 16
- 17. 3. Assessment of variability: semi-graphic method of Anderson; has the following steps: i. Plotting glyph on the graph: • Small circle representing position of genotype on the graph is a glyph • Two characters having high variability are chosen • One on X axis and other on Y axis based on their means • Each glyph occupies a definite position on the graph • Exotic or hybrids by solid glyph • Indigenous or parents by open glyph 17
- 18. ii. Depiction of variation: • Remaining characters displayed on glyph by rays • Rays for same character have the same position on glyph • Length of ray depends on index value iii. Construction of index score: • Variation for each character is divided into three groups viz., low, medium and high with index score 1,2 and 3 respectively • Sum of index values= worth of genotype • Max and min scores are 3n and n (n is the total number of characters) 18
- 19. iv. Analysis of variation: • Genotypes are divided into various groups • Max number of groups will be nine • Within and between groups variances are analysed 19
- 20. Example 1 • Take 5 genotypes A,B,C,D,E • A,B are exotic and C,D,E are indigenous • 5 characters are analysed viz., m, n, p, q and r • Mean values are worked out for each character and tabulated 20
- 21. Genotype m n p q r Total A 25(2) 20 (1) 25(2) 35(3) 20(1) 9 B 35(3) 35(3) 35(3) 20(1) 20(1) 11 C 20(1) 30(2) 20(1) 25(2) 25(2) 8 D 30(2) 25(2) 35(3) 20(1) 35(3) 11 E 30(2) 30(2) 25(2) 30(2) 25(2) 10 21 Charact ers Range of means Score 1 Score 2 Score 3 Value less than sign Value from - to sign Value more than sign p 20-35 25 25-35 35 q 20-35 25 25-35 35 r 20-35 25 25-35 35 Index scores
- 22. Metroglyph 22 Low medium high High medium low m n
- 23. Merits & demerits Helps to study the pattern of morphological variation in large number of germplasm lines at a time Simple in procedure Can be applied to unreplicated as well as replicated data Analysis is based on mean values Useful for classification of germplasm X Inclusion of more genotypes leads to overlapping of glyphs 23
- 24. D² statistic • Developed by P. C Mahalanobis (1928) in anthropometry and psychometry • Rao (1952) suggested this for genetic diversity assessment in plants • Potent technique of measuring genetic divergence 24
- 25. Main features • Numerical approach • Estimates are based on 2nd order statistics; less precision • More difficult analysis • Possible from replicated data only • Cluster diagram depicts genetic diversity 25
- 26. Main steps 1. Selection of genotypes: germplasm lines, strains and varieties; based on phenotypic or geographical differences 2. Evaluation of material: in replicated trials; observations on each trait are recorded 3. Biometrical analysis: variances for characters and covariance for their combinations are estimated; D² analysis; has following steps: 26
- 27. • Computation of D² values and testing its significance against χ² tab value for p degrees of freedom (p= total number of characters) • If D² calculated > χ² tab : significant • Finding out the contribution of individual characters towards total divergence • Grouping genotypes into clusters • Construction of cluster diagram 4. Interpretation: based on cluster diagram 27
- 28. Cluster diagram • Square root of average intra and inter cluster D² values are used • Depicts genetic diversity in an easily understandable manner • Number of clusters represent number of groups the population can be classified into • Inter cluster distance is a measure of degree of diversification • Genotypes grouped in one cluster are less divergent • Tells about relationship between various clusters 28
- 29. Example 2 • 20 genotypes and 5 characters • Genotypes are classified into 4 clusters based on D² values • Square root of D² values (D) are calculated 29
- 30. Clusters I II III IV I 4 (2) 16 (4) 36 (6) 49 (7) II 9 (3) 49 (7) 81 (9) III 4 (2) 9 (3) IV 1 (1) 30 IV I III II 7 3 4 7 6 9
- 31. Considerations in selection of parents • Relative contribution of each character to the total divergence • Choice of clusters with maximum genetic distance • Selection of one or two genotypes from such clusters 31
- 32. Merits • Helps to select genetically divergent parents • Measures degree of diversification • Determines relative proportion of each component character • Forces of differentiation measured at inter and intra cluster levels • Large number of germplasm lines can be evaluated at a time 32
- 33. Demerits • Analysis is difficult because of variances and covariances • Estimates are not statistically very robust • Analysis not possible from unreplicated data 33
- 34. Metroglyph Vs D² analysis Sl. 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 34
- 35. Conclusion “ Metroglyph analysis and D² statistics are extensively used for the assessment of genetic diversity and phenotypic variability as two-tier system. First the germplasm is evaluated by metroglyph analysis and then by D² statistics” 35
- 36. References • Prof. R K Singh, Dr. B D Chaudhary,2010, Biometrical methods in Quantitative Genetic Analysis, Kalyani Publishers, New Delhi, pages:224-252 • Phundan Singh, S S Narayanan, Biometrical techniques in Plant Breeding, pages:8-23 • Jawahar R Sharma, 2006, Statistical and Biometrical Techniques in Plant Breeding, New Age International Publishers, New Delhi, pages:51-68 36
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