1. The document discusses components of variation, heritability, types of heritability, genetic advance, environment, and genotype-environment interaction. It defines key terms like phenotypic variation, genotypic variation, broad sense heritability, narrow sense heritability, genetic advance, and genotype-environment interaction.
2. Heritability is the ratio of genotypic variance to phenotypic variance and indicates the proportion of the phenotypic variance caused by genetic factors. Broad sense heritability includes all genetic effects while narrow sense only considers additive genetic effects.
3. Genetic advance measures the expected genetic gain from selection and depends on genetic variability, heritability, and selection intensity. High genetic advance indicates a character is
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.
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.
Dr. Sushil Neupane's notes on "Introductory Genetics and Animal Breeding" for the 2nd year, 1st semester of the Diploma in Animal Science (latest syllabus of CTEVT) provide a comprehensive overview of key concepts and principles related to genetics and animal breeding. The notes cover fundamental topics in genetics and their practical applications in livestock production and breeding programs.
Mendelian genetics put forward the concept of dominant and recessive traits, where the phenotypes are controlled by single genes. These traits are known as monogenic or Mendelian traits.
There are features or traits in human genetics which are controlled by multiple genes and whose inheritance does not follow the rules of Mendelian genetics. Such traits are known as complex traits.
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.
Dr. Sushil Neupane's notes on "Introductory Genetics and Animal Breeding" for the 2nd year, 1st semester of the Diploma in Animal Science (latest syllabus of CTEVT) provide a comprehensive overview of key concepts and principles related to genetics and animal breeding. The notes cover fundamental topics in genetics and their practical applications in livestock production and breeding programs.
Mendelian genetics put forward the concept of dominant and recessive traits, where the phenotypes are controlled by single genes. These traits are known as monogenic or Mendelian traits.
There are features or traits in human genetics which are controlled by multiple genes and whose inheritance does not follow the rules of Mendelian genetics. Such traits are known as complex traits.
Show drafts
volume_up
Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
1. HERITABILITY , GENETIC ADVANCE , GENOTYPE -
ENVIRONMENT INTERACTION
SUBMITTED BY
Pawan Nagar
M Sc. Horti
ROLL NO . O4-2690S-2015
2. Components of variation
Heritability
Types of heritability
Genetic advance
Environment
Genotype x Environment interaction
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