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GENETIC DIVERSITY
ANALYSIS
Presenting to you by:
AKHISHA P. A.
BAM-15-57
17.02.2016 1
GGENETIC DIVERSITY ANALYSIS
 Variability and its sources
 Features of polygenic traits
 Types of polygenic variation
 ...
What is variability??
• Presence of differences among the
individuals of plant population
• Due to differences in genetic ...
4
5
Sources of variability
• Spontaneous mutations
• Natural outcrossing
• Recombination
Measures of conservation
• Global gen...
Features of polygenic traits
• Continuous variation
• Small and undetectable effect of individual
gene
• Several genes inv...
Types of polygenic variation
1. Phenotypic variation:
• Observable; Genotypic + environmental;
Measured as phenotypic vari...
Assessment of polygenic variation
• Requires metric measurements
• Observes several individuals and mean values
are used i...
Methods of
assessment of variability
i. Simple measures of variability
ii. Variance component analysis
iii. Metroglyph ana...
i) Simple measures of variability
• Range, standard deviation, variance, standard
error, coefficient of variation
• ANOVA ...
• GCV>PCV : little influence of environment,
selection will be rewarding
• PCV>GCV : apparent influence of
environment, se...
Variance component analysis
• Crossing of a number of genotypes in a
definite fashion
• Evaluation of progenies in replica...
Metroglyph analysis
• Semi-graphic method
• Assess pattern of morphological
variation in a large number of
germplasm lines...
• Main features are:
Analysed based on first order statistics, hence
more reliable and robust
Simple analysis
Possible ...
Main steps
1. Selection of genotypes: germplasm lines,
strains, varieties and hybrids; based on
phenotypic or geographical...
3. Assessment of variability: semi-graphic method
of Anderson; has the following steps:
i. Plotting glyph on the graph:
• ...
ii. Depiction of variation:
• Remaining characters displayed on glyph by rays
• Rays for same character have the same posi...
iv. Analysis of variation:
• Genotypes are divided into various groups
• Max number of groups will be nine
• Within and be...
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...
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) 2...
Metroglyph
22
Low medium high
High
medium
low
m
n
Merits & demerits
Helps to study the pattern of morphological
variation in large number of germplasm lines
at a time
Sim...
D² statistic
• Developed by P. C
Mahalanobis (1928) in
anthropometry and
psychometry
• Rao (1952) suggested this
for genet...
Main features
• Numerical approach
• Estimates are based on 2nd order
statistics; less precision
• More difficult analysis...
Main steps
1. Selection of genotypes: germplasm lines,
strains and varieties; based on phenotypic or
geographical differen...
• Computation of D² values and testing its
significance against χ² tab value for p degrees
of freedom (p= total number of ...
Cluster diagram
• Square root of average intra and inter cluster D²
values are used
• Depicts genetic diversity in an easi...
Example 2
• 20 genotypes and 5 characters
• Genotypes are classified into 4 clusters
based on D² values
• Square root of D...
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...
Considerations in selection of parents
• Relative contribution of each character to
the total divergence
• Choice of clust...
Merits
• Helps to select genetically divergent parents
• Measures degree of diversification
• Determines relative proporti...
Demerits
• Analysis is difficult because of variances
and covariances
• Estimates are not statistically very robust
• Anal...
Metroglyph Vs D² analysis
Sl.
No.
Particulars Metroglyph
analysis
D² statistics
1 Statistics involved First order Second o...
Conclusion
“ Metroglyph analysis and D² statistics
are extensively used for the assessment
of genetic diversity and phenot...
References
• Prof. R K Singh, Dr. B D Chaudhary,2010, Biometrical
methods in Quantitative Genetic Analysis, Kalyani
Publis...
37
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Genetic diversity analysis

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The presentation was done as part of the course STAT 504 titled Quantitative Genetics in Second Semester of MSc. Agricultural Statistics at Agricultural College, Bapatla under ANGRAU, Andhra Pradesh

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Genetic diversity analysis

  1. 1. GENETIC DIVERSITY ANALYSIS Presenting to you by: AKHISHA P. A. BAM-15-57 17.02.2016 1
  2. 2. GGENETIC DIVERSITY ANALYSIS  Variability and its sources  Features of polygenic traits  Types of polygenic variation  Methods of assessment of variability 2
  3. 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
  4. 4. 4
  5. 5. 5
  6. 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. 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. 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. 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. 10. Methods of assessment of variability i. Simple measures of variability ii. Variance component analysis iii. Metroglyph analysis iv. D² statistic 10
  11. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 22. Metroglyph 22 Low medium high High medium low m n
  23. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 33. Demerits • Analysis is difficult because of variances and covariances • Estimates are not statistically very robust • Analysis not possible from unreplicated data 33
  34. 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. 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. 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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