Stability parameters for comparing varieties (eberhart and russell 1966)Dhanuja Kumar
Phenotype is a result of genotype, environment and GE interaction. GENOTYPE- environment interactions are of major
importance to the plant breeder in developing
improved varieties. The performance of a single variety is not the same in all the environments. To identify a genotype whose performance is stable across environments various models were proposed. One such model was proposed by EBERHART and RUSSELL in 1966. Even after decades, this model is still preferred over others and used till date for stability analysis.
Stability parameters for comparing varieties (eberhart and russell 1966)Dhanuja Kumar
Phenotype is a result of genotype, environment and GE interaction. GENOTYPE- environment interactions are of major
importance to the plant breeder in developing
improved varieties. The performance of a single variety is not the same in all the environments. To identify a genotype whose performance is stable across environments various models were proposed. One such model was proposed by EBERHART and RUSSELL in 1966. Even after decades, this model is still preferred over others and used till date for stability analysis.
Stability analysis and G*E interactions in plantsRachana Bagudam
Gene–environment interaction is when two different genotypes respond to environmental variation in different ways. Stability refers to the performance with respective to environmental factors overtime within given location. Selection for stability is not possible until a biometrical model with suitable parameters is available to provide criteria necessary to rank varieties / breeds for stability. Different models of stability are discussed.
QTL is a gene or the chromosomal region that affects a quantitative trait, which should be polymorphic (have allelic variation) to have an effect in a population, must be linked to a polymorphic marker allele to be detected. The QTL mapping consists of 4 steps, like the development of mapping population, generation of polymorphic marker data set among the parents, construction of linkage map, and finally the QTL analysis
All the above steps are described in these slides very briefly along with two case studies.
Introduction:
Proposed by Meuwissen et al. (2001)
GS is a specialized form of MAS, in which information from genotype data on marker alleles covering the entire genome forms the basis of selection.
The effects associated with all the marker loci, irrespective of whether the effects are significant or not, covering the entire genome are estimated.
The marker effect estimates are used to calculate the genomic estimated breeding values (GEBVs) of different individuals/lines, which form the basis of selection.
Why to go for genomic selection:
Marker-assisted selection (MAS) is well-suited for handling oligogenes and quantitative trait loci (QTLs) with large effects but not for minor QTLs.
MARS attempts to take into account small effect QTLs by combining trait phenotype data with marker genotype data into a combined selection index.
Based on markers showing significant association with the trait(s) and for this reason has been criticized as inefficient
The genomic selection (GS) scheme was to rectify the deficiency of MAS and MARS schemes. The GS scheme utilizes information from genome-wide marker data whether or not their associations with the concerned trait(s) are significant.
GEBV: GenomicEstimated Breeding Values-
The sum total of effects associated with all the marker alleles present in the individual and included in the GS model applied to the population under selection
Calculated on a single individual basis
Gene-assisted genomic selection:
A GS model that uses information about prior known QTLs, the targeted QTLs were accumulated in much higher frequencies than when the standard ridge regression was used
The sum total of effects associated with all the marker alleles present in the individual and included in the GS model applied to the population under selection
Calculated on a single individual basis
Population used:
Training population: used for training of the GS model and for obtaining estimates of the marker-associated effects needed for estimation of GEBVs of individuals/lines in the breeding population.
Breeding population: the population subjected to GS for achieving the desired improvement and isolation of superior lines for use as new varieties/parents of new improved hybrids.
Training population-
large enough: must be representative of the breeding population: max. trait variance with marker : by cluster analysis
should have either equal or comparable LD, LD decay rates with breeding populations
Updated by including individuals/lines from the breeding population
Training more than one generation
Low colinearity between markers is needed since high colinearity tends to reduce prediction accuracy of certain GS models. (colinearity disturbed by recombination)
Heterotic group “is a group of related or unrelated genotypes from the same or different populations, which display similar combining ability and heterotic response when crossed with genotypes from other genetically distinct germplasm groups.”
It comprises on mating designs used in plant breeding programs. 6 basic mating designs are briefly explained in it with their requirements as well limiting factors...
Quantitative trait loci (QTL) analysis and its applications in plant breedingPGS
Abstract
Many agriculturally important traits such as grain yield, protein content and relative disease resistance are controlled by many genes and are known as quantitative traits (also polygenic or complex traits). A quantitative trait depends on the cumulative actions of many genes and the environment. The genomic regions that contain genes associated with a quantitative trait are known as quantitative trait loci (QTLs). Thus, a QTL could be defined as a genomic region responsible for a part of the observed phenotypic variation for a quantitative trait. A QTL can be a single gene or a cluster of linked genes that affect the trait. The effects of individual QTLs may differ from each other and change from environment to environment. The genetics of a quantitative trait can often be deduced from the statistical analysis of several segregating populations. Recently, by using molecular markers, it is feasible to analyze quantitative traits and identify individual QTLs or genes controlling the traits of interest in breeding programs.
Stability analysis and G*E interactions in plantsRachana Bagudam
Gene–environment interaction is when two different genotypes respond to environmental variation in different ways. Stability refers to the performance with respective to environmental factors overtime within given location. Selection for stability is not possible until a biometrical model with suitable parameters is available to provide criteria necessary to rank varieties / breeds for stability. Different models of stability are discussed.
QTL is a gene or the chromosomal region that affects a quantitative trait, which should be polymorphic (have allelic variation) to have an effect in a population, must be linked to a polymorphic marker allele to be detected. The QTL mapping consists of 4 steps, like the development of mapping population, generation of polymorphic marker data set among the parents, construction of linkage map, and finally the QTL analysis
All the above steps are described in these slides very briefly along with two case studies.
Introduction:
Proposed by Meuwissen et al. (2001)
GS is a specialized form of MAS, in which information from genotype data on marker alleles covering the entire genome forms the basis of selection.
The effects associated with all the marker loci, irrespective of whether the effects are significant or not, covering the entire genome are estimated.
The marker effect estimates are used to calculate the genomic estimated breeding values (GEBVs) of different individuals/lines, which form the basis of selection.
Why to go for genomic selection:
Marker-assisted selection (MAS) is well-suited for handling oligogenes and quantitative trait loci (QTLs) with large effects but not for minor QTLs.
MARS attempts to take into account small effect QTLs by combining trait phenotype data with marker genotype data into a combined selection index.
Based on markers showing significant association with the trait(s) and for this reason has been criticized as inefficient
The genomic selection (GS) scheme was to rectify the deficiency of MAS and MARS schemes. The GS scheme utilizes information from genome-wide marker data whether or not their associations with the concerned trait(s) are significant.
GEBV: GenomicEstimated Breeding Values-
The sum total of effects associated with all the marker alleles present in the individual and included in the GS model applied to the population under selection
Calculated on a single individual basis
Gene-assisted genomic selection:
A GS model that uses information about prior known QTLs, the targeted QTLs were accumulated in much higher frequencies than when the standard ridge regression was used
The sum total of effects associated with all the marker alleles present in the individual and included in the GS model applied to the population under selection
Calculated on a single individual basis
Population used:
Training population: used for training of the GS model and for obtaining estimates of the marker-associated effects needed for estimation of GEBVs of individuals/lines in the breeding population.
Breeding population: the population subjected to GS for achieving the desired improvement and isolation of superior lines for use as new varieties/parents of new improved hybrids.
Training population-
large enough: must be representative of the breeding population: max. trait variance with marker : by cluster analysis
should have either equal or comparable LD, LD decay rates with breeding populations
Updated by including individuals/lines from the breeding population
Training more than one generation
Low colinearity between markers is needed since high colinearity tends to reduce prediction accuracy of certain GS models. (colinearity disturbed by recombination)
Heterotic group “is a group of related or unrelated genotypes from the same or different populations, which display similar combining ability and heterotic response when crossed with genotypes from other genetically distinct germplasm groups.”
It comprises on mating designs used in plant breeding programs. 6 basic mating designs are briefly explained in it with their requirements as well limiting factors...
Quantitative trait loci (QTL) analysis and its applications in plant breedingPGS
Abstract
Many agriculturally important traits such as grain yield, protein content and relative disease resistance are controlled by many genes and are known as quantitative traits (also polygenic or complex traits). A quantitative trait depends on the cumulative actions of many genes and the environment. The genomic regions that contain genes associated with a quantitative trait are known as quantitative trait loci (QTLs). Thus, a QTL could be defined as a genomic region responsible for a part of the observed phenotypic variation for a quantitative trait. A QTL can be a single gene or a cluster of linked genes that affect the trait. The effects of individual QTLs may differ from each other and change from environment to environment. The genetics of a quantitative trait can often be deduced from the statistical analysis of several segregating populations. Recently, by using molecular markers, it is feasible to analyze quantitative traits and identify individual QTLs or genes controlling the traits of interest in breeding programs.
Genetic parameters is an important issue in animal breeding. Parameters that are of interest are heritability, genetic correlation and repeatability, and those are computed as functions of the variance components.
Heritability (h2) refers to the degree of resemblance between relatives i.e. how much the progeny resemble its parents. Heritability (h2) is the most important genetic parameter on which different breeding strategies depend. The knowledge of h2 is a frontline for the formulation of breeding plans on scientifi c lines, which are used for selection of parents for future breeding program. In order to made breeding plans, there is need to know the h2 of different characters (traits). The extent of genetic control is different for different traits. The higher the h2, the greater is the genetic control on the trait, and the more rapidly selection will result in genetic progress. For
highlyheritable traits, differences in breeding values of animals have large effect on performance, and differences in environments have less important effect on performance. The opposite is true for lowly heritable traits. In other words, heritability could increase if genetic variation increases and it might also increase if the environmental variation decreases. As a rule, signifi cant genetic change can be made by selecting for highly heritable traits. For lowly heritable traits, selection is less effective; so performance may be improved through management. Therefore, the aim of this review is to defi ne heritability (h2) and assess its role in animal breeding.
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
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Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
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3. COMPONENTS OF VARIATION
The quantitative variation in a population is of three types ,
Phenotypic variation
Genotypic variation
Environmental variation
FISHER 1918 , divided the genetic variance into three
components
Additive variance
Dominance variance
Epistasis variance
4. In crop improvement only the genetic component of variation is
important since only this component is transmitted to the next
generation
Heritability is the ratio of genotypic variance to the phenotypic
variance
Heritability denotes the proportion of phenotypic variance that is due
to genotype i.e., heritable .
It is generally expressed in percent (%)
It is a good index of transmission of characters from parents to their
offspring
5. TYPES OF HERITABILITY
Depending upon the components of variance used as numerator
in the calculation ,there are 2 definitions of Heritability
1.Broad sense heritability
2. Narrow sense heritability
6. Broad sense heritability
• According to Falconer, broad sense heritability is the ratio of
genotypic variance to total or phenotypic variance
• It is calculated with the help of following formula
where , Vg= genotypic variance
Vp = phenotypic variance
Ve = error variance
Heritability (h²) = Vg / Vp x 100 = Vg / Vg + Ve x 100
7. Broad sense heritability
broad heritability (h2) separates genotypic from environmentally
induced variance: h2 = Vg / Vp
It can be estimated from both parental as well as segregating
populations
It express the extent to which the phenotype is determined by the
genotype , so called degree of genetic determination
It is most useful in clonal or highly selfing species in which genotypes
are passed from parents to offspring more or less intact
It is useful in selection of superior lines from homozygous lines
8. Narrow sense heritability
In outbreeding species evolutionary rates are affected by narrow-
sense heritability
It is the ratio of additive or fixable genetic variance to the total
or phenotypic variance
Also known as degree of genetic resumblance
it is calculated with the help of following formula
where VA or D = additive genetic variance
VP or VP = phenotypic variance
Heritability (h²) = VA / VP x 100 or ½ D / VP
9. NARROW SENSE HERITABILITY
It plays an important role in the selection process in plant breeding
For estimation of narrow sense heritability , crosses have to be
made in a definite fashion
It is estimated from additive genetic variance
It is useful for plant breeding in selection of elite types from
segregating populations
10. If heritability in broad sense is high
It indicates character are least influenced by environment
selection for improvement of such characters may be useful
If heritability in broad sense is low
The character is highly influenced by environmental effects
Genetic improvement through selection will be difficult
11. If heritability in narrow sense is high
characters are govern by additive gene action
Selection for improvement of such characters would be rewarding
If low heritability in narrow sense
Non additive gene action
Heterosis breeding will be beneficial
12. H2 varies from 0 (all environment) to 1 (all genetic)
Heritability of 0 are found in highly inbred populations with no
genetic variation.
Heritability of 1 are expected for characters with no environmental
variance in an outbred population if all genetic variance is additive.
Heritability are specific to particular populations living under specific
environmental conditions
Heritability (h²) and Additive Variance (VA ) are fundamentally
measures of how well quantitative traits are transmitted from one
generation to the next
13. Type of genetic material : the magnitude of heritability is
largely governed by the amount of genetic variance present in a
population for the character under study
Sample size : Large sample is necessary for accurate estimates
Sampling methods : 2 sampling methods , Random and
Biased . The random sampling methods provide true estimates of
genetic variance and hence of heritability
14. Layout or conduct of experiment : Increasing the plot size
and no. of replications we can reduce experimental error and get
reliable estimates
Method of calculation : heritability is estimated by several
methods
Effect of linkage : high frequency of coupling phase (AB/ab)
causes upward bias in estimates of additive and dominance variances
. Excess of repulsion phase linkage (Ab/aB ) leads to upward bias in
dominance variance and downward bias in additive variances
15. Improvement in the mean genotypic value of selected plants over the
parental population is known as genetic advance
It is the measure of genetic gain under selection
The success of genetic advance under selection depends upon three factors
(Allard , 1960)
Genetic variability : greater the amount of genetic variability in base
populations higher the genetic advance
Heritability : the G.A. is high with characters having high heritability
Selection intensity : the proportion of individuals selected for the study is
called selection intensity . high selection intensity gives better results
16. It is the difference between the mean phenotypic value of selected
population and mean phenotype of original population
This is the measure of the selection intensity and denoted by K
where , Xs = mean of phenotypic value of selected plants
Xo = mean of phenotypic value of parental population
Selection
intensity
1 % 2% 5% 10%
value of K 2.64 2.42 2.06 1.76
K = Xs – Xo
17. The difference between the mean phenotypic value of the progeny of
selected plants and the original parental population is known as
genetic gain
It is denoted by R
where , Xp = mean phenotypic value of progeny of selected plants
Xo = mean of phenotypic value of base population
R = Xp – Xo
18. The genetic advance is calculated by the following formula
where , K = standardize selection differential
h² = heritability of the character under selection
δp = phenotypic standard deviation
The estimates of GS have same unit as those of the mean
The genetic advance from mixture of purelines or clones should be
calculated using h² (bs)
From segregating populations using h² (ns)
GS = K x h² x δp
19. If the value of Genetic advance high
The character is governed by additive genes and selection will be
beneficial for such traits
If Genetic advance is low
The character is governed by non additive genes and heterosis
breeding may be useful
20. The external condition that affects the expression of genes
of genotype
Comstock and Moll, 1963 classified in two groups
Micro environment :
environment of single organism , as opposed to that of another growing
at the same time and place e.g. physical attributes of soil , temp ,
humidity , insect-pests and diseases
Macro environment :
associated with a general location and period of time . A collection of
micro environment
21. Allard and Bradshaw ,1964 classified Environmental
variables into two groups
Predictable or controllable environment :
includes permanent features of environment ( climate , soil type, day
length) controllable variable : fertilizer level, sowing date & density,
methods of harvesting . High level of interaction is desirable
Unpredictable or uncontrollable environment :
difference between seasons, amount & distribution of rainfall,
prevailing temperature . Low level of interaction is desirable
22. Algebraically, we can define the phenotypic value Of an individual as
the consequence of the alleles
It inherits together with environmental influences As
Where P = phenotype, G = Genotype, and E = Environment
P = G + E
P = G + E + GxE
23. A phenotype is the result of interplay of a genotype and
each environment .
A specific genotype does not exhibit the same phenotypic
characteristics under all environment, or different genotype
respond differently to a specified environment.
This variation arising from the lack of correspondence between
genetic and non genetic effects is known as Genotype X
Environment Interactions.
Differences in performance of genotypes in different
environments is referred to as Genotype X Environment
Interactions.
The low magnitude of genotype x environment interaction
indicates consistence performance of the population .Or it
shows high buffering ability of the population
24. Quantitative G x E interaction or Non crossover interaction
When performance of the varieties does not change over the
environments ,the differential response of genotypes is only a
matter of scale , such G x E interaction is termed as quantitative GxE
interaction
Qualitative or Cross over G x E interaction
In case of qualitative or cross over G x E interaction the
performance of varieties changes with the environment and a given
environment favours some genotype or detrimental to some . As a
result the differential response of genotypes differ in type (not
scale) of response (promotion or inhibition)
25. No
G x E interaction
G x E interaction is
quantitative
G x E interaction is
qualitative
26. Quantitative interactions are less important to breeders
while , Qualitative G x E interactions complicate
identification and selection of superior genotypes.
A common strategy to manage the G X E interaction is to
test the genotypes over a representative range of conditions
( both locations and years)
27. REFERENCE
B. D. SINGH , Plant Breeding : Principles and
Methods
N. K. S. Kute , A. R. Kumar : Principles of Plant
Breeding
J. Brown , P. Caligari : An introduction to Plant
Breeding