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

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
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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
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4. 4

5. 5

6. Sources of variability
• Spontaneous mutations
• Natural outcrossing
• Recombination
Measures of conservation
• Global gene pool
• Deliberate use of heterogeneous populations
• Use of multiline varieties
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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
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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
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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
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10. Methods of
assessment of variability
i. Simple measures of variability
ii. Variance component analysis
iii. Metroglyph analysis
iv. D² statistic
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11. i) Simple measures of variability
• Range, standard deviation, variance, standard
error, coefficient of variation
• ANOVA provides estimates of CV%
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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
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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.
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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
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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
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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
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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
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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)
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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
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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
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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
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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
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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
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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
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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
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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:
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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
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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
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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
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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)
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IV
I
III
II
7
3
4
7
6
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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
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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
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33. Demerits
• Analysis is difficult because of variances
and covariances
• Estimates are not statistically very robust
• Analysis not possible from unreplicated
data
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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
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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”
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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
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