Recent approaches in quantitative genetics

RECENT APPROACHES IN
QUANTITATIVE GENETICS
Term paper presentation
GP-503
 SUBMITTED TO:
Dr. K.B. ESWARI
PROFESSOR
DEPT. OF GENETICS AND PLANT
BREEDING
 SUBMITTED BY:
ANIL KUMAR
RAM/2020-68

Introduction
 The characters that are governed by several genes, each having small and
cumulative effect are called quantitative characters or polygenic characters.
 The mode of inheritance of polygenic characters is termed as polygenic
inheritance or quantitative inheritance or multiple factor inheritance.
 These characters are considerably affected by environment so that they show
continuous variation, and it is not possible to classify them into distinct classes.
Therefore, inheritance of quantitative characters can not be studied through
classical technique of genetic analysis.

Cont…
Since polygenic trait require metric measurement. Therefore inheritance
studies on quantitative characters have to employ statistical procedure.
 The branch of Genetics, which attempt to unravel the inheritance of
quantitative trait using statistical concept and procedure is called
quantitative genetics or biometrical genetics.
The various statistical procedures employed in biometrical genetics are
called biometrical techniques.

Biometrical techniques are useful to plant breeders in
the following different ways:
Assessment of genetic variability present in a population:
a.Simple measure of variability:- Range, Mean, Variance, Standard
Deviation, Coefficient of Variation, Standard Error etc.
b.Genetic Diversity:- D2 Statistics, Metroglyph Analysis
Selection of elite genotype from mixed population:- Correlation
Coefficient Analysis, Path Analysis, Discriminate Function.
Choice of Parent and Breeding procedure:- Diallel Analysis, Partial Diallel
Analysis, Triallel Analysis, Quardiallel Analysis, Line X tester Analysis,
Generation mean Analysis, Biparental Cross Analysis.

Cont...
Analysis of G X E interaction and stability parameter
 ANOVA
 Non-parametric method
 Regression Coefficient model:-
Finley & Wilkinson model (1963)
Eberhart & Russel model (1966)
Perkins & Jinks model (1968)
Freeman & Perkins model (1971)
Multivariate technique:-
Cluster Analysis
Principal Component Analysis
Factor Analysis
Additive Main Effects and Multiplicative Interaction (AMMI)

Additive Main Effects and Multiplicative
Interaction (AMMI) Model
AMMI model proposed by Gauch (1992) is a statistical tool which leads
to identification of stable genotype with their adaptation behaviour in
easy manner.
It is combination of ANOVA and PCA in which main effect (of genotype
and environment) are initially accounted for regular analysis of variance
and then interaction (G X E) is analyse through principal component
analysis.

Cont…
AMMI first calculate genotype and environment additive effect using
analysis of variance (ANOVA) and then analyse residual from these
model using principal component analysis (PCA).
Stability conclusion made from AMMI model are based on biplot.
AMMI models are usually called AMMI(1), AMMI(2),…..AMMI(n),
depending on the number of principal component used to study
interaction and graphical representations obtained using biplots.

Analysis of variance for stability-AMMI Model
Source df SS MS F
TOTAL (ger-1)
Treatment (ge-1)
Genotype (g-1)
Environment (e-1)
Interaction
IPCA 1
IPCA 2
Residual
(g-1)(e-1)
Blocks (r-1)
Error (r-1)(ge-1)

Principal components
Usually the first principal component represents responses of the
genotypes that are proportional to the environments, which are
associated with the G X E interaction without change of the range.
The second principal component provides information about
cultivation locations that are not proportional to the environments,
indicating that those are responsible of the G X E crossover
interaction.

BIPLOTS
Graphical representation of interaction using AMMI interaction
parameter is known as biplot.
Stability conclusion made from AMMI model are based on biplot.
Biplot analysis is possibly the most powerful interpretive tool for AMMI
models.
There are two basic AMMI biplots:-
AMMI 1 biplot and
AMMI 2 biplot

AMMI 1 biplot
 In AMMI 1 biplot, the main effects (genotype mean and environment mean) and
IPCA1 scores for both genotypes and environments are plotted against each other.
 A biplot is developed by placing both genotype and environmental mean on x-axis
and representing PCA axis eigen vector on the y-axis
 The biplot helps to visualize relationship between eigen values for PCA1 and
genotypic and environmental means.
 If a genotype or an environment has a IPCA1 score of nearly zero, it has small
interaction effects and considered as stable.
 When a genotype and environment have the same sign on the PCA axis, their
interaction is positive and if different, their interaction is negative.

AMMI 2 biplot
 In AMMI 2 biplot IPCA 2 score of genotypes and environments are plotted
against their respective IPCA 1 score.
 In AMMI 2 biplot, the environmental scores are joined to the origin by side lines.
Sites with short spokes do not exert strong interactive forces. Those with long
spokes exert strong interaction.
 The genotypes occurring close together on the plot will tend to have similar
yields in all environments, while genotypes far apart show a different pattern of
response over the environments.
 Hence, the genotypes near the origin are not sensitive to environmental
interaction and those distant from the origins are sensitive and have large
interaction.

Main features of AMMI Model
Combines conventional ANOVA with principal component analysis.
May provide more reliable estimates of genotype performance then
the mean across sites.
Enables to select varieties with god adaptation to target breeding
environments.
Biplots help to visualize relationship among genotypes and
environments ; show both main and interaction effect.
Enables to identify target breeding environments and to choose
representative testing site in those environments.

Abstract
 The objective of this study was to determine the genotype × environment
interaction (GEI) and stability performance of eight promising cotton genotypes
at four agro-ecologies in Telangana State.
 The experimental material consisting of eight genotypes were planted in
randomized block design with three replications.
 Analysis of variance was significant for environments and (G x E) components
indicating the use fullness of AMMI analysis in identifying the stable genotypes.
 Among the eight cotton genotypes, WGCV-109, Narasimha and ADB-645 were
found to be best yielders over environments whereas the genotypes G7 (WGCV-
48) and G4 (Narasimha) found to be stable.

Materials and Methods
 In the present investigation, the experiment material comprised a total of
eight cotton genotypes viz., WGCV-109, ADB-638, WGCV-122, Narasimha,
WGCV-119, Srirama, WGCV-48 and ADB 645.
 The present experiment conducted at four diverse environments of Telangana
state i.e. Warangal, Adilabad, Mudhole and Palem during 2017, Kharif season.
 The experiments were laid in randomized block design with three replications.
 Standard package of practices were followed to maintain a good crop in the
field.
 The data was subjected to analysis of variance and then taken for AMMI
analysis for identification of stable genotypes.

Results and Discussion
In AMMI 1 biplot:-
 Out of eight cotton genotypes G1 being the overall best genotype in yield specially
adapted the corresponding environment.
 Among eight genotypes, G7 was with near zero IPCA1 score and hence had less
interaction with the environments with above average yield performance.
 Of the environments, E1 and E4 were most favorable environments for most
genotypes.
In AMMI 2:-
 Environments E1, E3 and E4 exerted strong interaction forces.
 In the present case, G2, G8, G1 and G5 had more responsive since they were away
from the origin whereas the genotype G7 was close to the origin and hence this was
less sensitive to environmental interactive forces.
 The genotypes G5 (WGCV-119) and G3 (WGCV-122) may perform better in
environment E2 while the genotypes G2 (ADB-638) and G8 (ADB-645) in environment
E3, G1 (WGCV-109) and G4 (Narasimha) in Environment E4 and G6 (Srirama) in
environment E1.

Abstract
Genotype x environment interaction and stability performance were
investigated on grain yield with 12 rice genotypes in five environments.
The ANOVA for grain yield revealed highly significant (P<0.01) for
genotypes, environments and their interactions.
The significant interaction indicated that the genotypes respond
differently across the different environments.

Materials and Methods
 The experiments were conducted at five districts namely Gazipur (E1), Comilla
(E2), Barisal (E3), Rangpur (E4) and Jessore (E5) representing five different agro-
ecological zones (AEZ) of Bangladesh.
 Twelve genotypes consisting of 3 advanced lines (BRRI 1A/ BRRI 827R(G1),
IR58025A/ BRRI 10R (G2) and BRRI 10A/ BRRI 10R (G3)), 6 released hybrids
(BRRI hybrid dhan1(G4), Tea (G5), Mayna (G6), Richer (G7), Heera-2 (G8) and
Heeta 99-5 (G9)), and 3 inbreed check varieties (BRRI dhan31 (G10), BRRI
dhan33 (G11) and BRRI dhan39 (G12)) were used as experimental materials.

Cont…
The experiments were carried out in a randomized complete
block design (RCBD), with three replications.
Standard agronomic practices were followed and plant
protection measures were taken as required.
The grain yield data for twelve (12) genotypes in five (5)
environments were subjected to AMMI analysis of variance
using statistical analysis package software Cropstat version
6.1

Additive main effects and multiplicative interaction (AMMI) analysis
of variance for grain yield (tha-1) of 12 rice genotypes across 5
environments.

Stability parameters for grain yield (t/ha) of 12 rice genotypes in 5
environments.

AMMI 1 Biplot for grain yield (t/ha) of 12 rice genotypes (G) and
five environments (E) using genotypic and environmental
scores.

AMMI 2 Biplot for grain yield (th/a) showing the interaction of
IPCA2 against IPCA1 scores of 12 rice genotypes (G) in five
environments (E).

Results and Discussion
The mean grain yield value of genotypes averaged over environments
indicated that the genotypes G3 and G12 had the highest (5.99 tha-1) and
the lowest (3.19 tha-1) yield, respectively.
It is noted that the variety G3 showed higher grain yield than all other
varieties over all the environments.
The hybrids (G1), (G2), (G3) and (G4) were hardly affected by the G x E
interaction and thus will perform well across a wide range of
environments.

Recent approaches in quantitative genetics

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