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)
Presentation delivered by Dr. Jesse Poland (Kansas State University, USA) at Borlaug Summit on Wheat for Food Security. March 25 - 28, 2014, Ciudad Obregon, Mexico.
http://www.borlaug100.org
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)
Presentation delivered by Dr. Jesse Poland (Kansas State University, USA) at Borlaug Summit on Wheat for Food Security. March 25 - 28, 2014, Ciudad Obregon, Mexico.
http://www.borlaug100.org
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
Advanced biometrical and quantitative genetics akshayAkshay Deshmukh
Additive and Multiplicative Model
Shifted Multiplicative Model
Analysis and Selection of Genotype
Methods and steps to select the best model
Bioplot and mapping genotype
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
Advanced biometrical and quantitative genetics akshayAkshay Deshmukh
Additive and Multiplicative Model
Shifted Multiplicative Model
Analysis and Selection of Genotype
Methods and steps to select the best model
Bioplot and mapping genotype
This study aims to determine the genetic components like Vg(Variance of genotype), Vp ( Variance of phenotype), GCV (Genotypic co-efficient of variation), PCV (Phenotypic coefficient of variation), Hb (Heritability) and GA% (Genetic advance in percentage of means) in F2 generation of the cross Nagina x Bushbeef-steak for predicting quantitative traits. Data was collected on P1, P2 F1 and F2 generation for various yield components and were analyzed. Analyzed data showed relatively high difference between, GCV, Vp and PCV for the traits: Flowers/cluster, Fruits/cluster and Fruit weight and relatively low difference was noted for Vg, GCV and Vp, PCV values in the traits: Fruit diameter, Fruit length and fruits/plant. Highest value of GCV (79.90%) and PCV (92.79%) were noted in the trait: yield/plant and the lowest values of GCV (14.68%) and PCV (16.78%) were noted for fruit-length. Highest value (84.08%) of broad sense heritability %(Hb%) was noted in fruit diameter and the lowest value of heritability(27.58) was noted for the trait fruits/cluster. Moderate value of heritability (74.13%) along with low value (15.22) of GA% was noted for yield/plant.
Molecular Identification of Specific Virulence Genes in EnteropathogenicEsche...iosrjce
IOSR Journal of Pharmacy and Biological Sciences(IOSR-JPBS) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of Pharmacy and Biological Science. The journal welcomes publications of high quality papers on theoretical developments and practical applications in Pharmacy and Biological Science. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
VARIABILITY, CORRELATION AND PATH COEFFICIENT ANALYSIS OF YIELD ATTRIBUTING TRAITS IN 6 GENOTYPES OF Lentil (Lens culinaris) AT IAAS, PAKLIHAWA, RUPANDEHI, NEPAL. This research had been undertaken as a part of UPA (Undergraduate Practicum Assessment)
DOI: 10.21276/ijlssr.2016.2.3.19
ABSTRACT- By using gamma rays (physical) & EMS (chemical) mutagens the various genetic variability parameters
were estimated of two soybean cultivars i.e. PKV-1 & JS-335. Characters studied i.e. Plant height, no. of branches per
plant, no. of clusters per plant, no. of pods per plant, yield per plant, 100 grain wt. shows that genotypic coefficient of
variation (G.C.V.) & phenotypic coefficient of variation (PCV), heritability was significantly high. In both the varieties, all
the mutagenic treatments were effective in inducing genetic variability.
Key-words- Gamma rays, EMS, Mutagens, Genetic Variability
Seminar on Genetic improvement in cucumber.pptxAKHILRDONGA
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Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
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!
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
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.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
Adversarial Attention Modeling for Multi-dimensional Emotion Regression.pdf
5 gpb 621 components of variance
1. COMPONENTS OF VARIANCE- ESTIMATION OF
HERITABILITY FOR F2 GENERATION
GPB 621 – PRINCIPLES OF QUANTITATIVE GENETICS
Class – 5
Dr. K. SARAVANAN
Professor
Department of Genetics and Plant Breeding
Faculty of Agriculture
Annamalai University
2. Dr. K. Saravanan,
GPB, AU
Phenotypic Variance
Non Heritable variance
/Environmental Variance
Heritable variance /
Genotypic Variance
.
Additive gene effects
Variance due to
dominance deviation
Variance due to
epistasis
Genetic variance
Interation between genes of
the same locus (Intra allelic /
with in locus)
Interation between genes of
different loci (Inter allelic /
between loci)
Fixable
D (or)
Fully Non Fixable Partly Non Fixable
E
H (or) and
d
h
j
i,
l
3. Dr. K. Saravanan, GPB, AU
• Gene action in different breeding programme
.
Types of Gene Action Breeding procedure to be followed
Self Pollinated Crops
1. Additive Pureline selection, Mass selection, Progeny selection and
Hybridization and selection with pedigree breeding
2. Non-additive Heterosis breeding and recombination breeding with
postponement of selection at later generations.
Cross Pollinated Crops
1. Additive Synthetic breeding, composite breeding and population
improvement by recurrent selection for gca
2. Non-additive Heterosis breeding and population improvement by recurrent
selection for sca
3. Both Additive and Non-
additive
Population improvement by Reciprocal recurrent selection
4. Dr. K. Saravanan, GPB, AU
Components of Variance (work sheet)
• Results on cotton Single Plant yield in gram in a cross between Reba
Bo50 x Laxmi.
.
Generation Mean Variance
P1 7.20 24.38
B1 20.42 294.68
F1 24.07 160.24
F2 21.66 380.30
B2 19.25 220.52
P2 9.20 44.13
5. Dr. K. Saravanan, GPB, AU
A). Estimation of “D”
Step 1. VF2 =
𝟏
𝟐
D +
𝟏
𝟒
H + E = 380.30
---------------------------------------------------
Step 2. VB1 =
𝟏
𝟒
D +
𝟏
𝟒
H + E = 294.68
VB2 =
𝟏
𝟒
D +
𝟏
𝟒
H + E = 220.52
VB1 + VB2 =
𝟏
𝟐
D +
𝟏
𝟐
H + 2E = 515.20
----------------------------------------------------
Step 3. 2VF2 = D +
𝟏
𝟐
H + 2E = 760.60
deduct VB1 + VB2 =
𝟏
𝟐
D +
𝟏
𝟐
H + 2E = 515.20
-----------------------------------------------------
𝟏
𝟐
D -- -- = 245.40
D -- -- = 490.80 ------(A)
.
Generation Mean Variance
P1 7.20 24.38
B1 20.42 294.68
F1 24.07 160.24
F2 21.66 380.30
B2 19.25 220.52
P2 9.20 44.13
6. Dr. K. Saravanan, GPB, AU
B). Estimation of “E”
Step 4.
E = 76.25 --------(B)
.
3
1
2
1 F
P
P
E
V
V
V
V
3
75
.
228
3
13
.
44
24
.
160
38
.
24
E
V
Generation Mean Variance
P1 7.20 24.38
B1 20.42 294.68
F1 24.07 160.24
F2 21.66 380.30
B2 19.25 220.52
P2 9.20 44.13
7. Dr. K. Saravanan, GPB, AU
C). Estimation of “H”
VF2 =
𝟏
𝟐
D +
𝟏
𝟒
H + E = 380.30
𝟏
𝟐
490.80 +
𝟏
𝟒
H + 76.25 = 380.30
𝟏
𝟒
H = 380.30 – 245.40 – 76.25
𝟏
𝟒
H = 58.65
H = 234.60 -------- (C)
.
Generation Mean Variance
P1 7.20 24.38
B1 20.42 294.68
F1 24.07 160.24
F2 21.66 380.30
B2 19.25 220.52
P2 9.20 44.13
8. Dr. K. Saravanan, GPB, AU
Results :
VF2 = 380.30 (Phenotypic variance)
D = 490.80 (Variance due to additive effects)
H = 234.60 (Variance due to dominance deviation)
E = 76.25 (Environmental variance)
.
9. Dr. K. Saravanan, GPB, AU
HERITABILITY FOR F2 :
• Heritability h2 (Narrow sense) =
• h2 = 245.40/380.30 = 0.6453
• h2 = 64.53 %
• Heritability h2 (Broad sense) =
𝑉𝑔
𝑉𝑝ℎ
=
𝑉𝑝ℎ−𝑉𝑒
𝑉𝑝ℎ
• h2 =
380.30 −76.25
380.30
= 0.799 = 79.90 %
.
2
2
1
2
F
V
D
h
iance
phenotypic
effects
Additive
var
2
1
VF2 = 380.30 (Phenotypic variance)
D = 490.80 (Variance due to
additive effects)
H = 234.60 (Variance due to
dominance deviation)
E = 76.25 (Environmental
variance)
10. Dr. K. Saravanan, GPB, AU
Genotypic Coefficient of Variation :
• GCV =
• 𝑉
𝑔 = 𝑉𝑝ℎ − 𝑉
𝑒
• General Mena = F2 mean
GCV = = 80.33 %
.
100
var
X
mean
General
iance
Genotypic
100
66
.
21
05
.
304
X
VF2 = 380.30 (Phenotypic variance)
D = 490.80 (Variance due to
additive effects)
H = 234.60 (Variance due to
dominance deviation)
E = 76.25 (Environmental
variance)
Generation Mean Variance
P1 7.20 24.38
B1 20.42 294.68
F1 24.07 160.24
F2 21.66 380.30
B2 19.25 220.52
P2 9.20 44.13