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.
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.”
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.
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)
Multiple inbred founder lines are inter-mated for several generations prior to creating inbred lines, resulting in a diverse population whose genomes are fine scale mosaics of contributions from all founders.
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.”
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.
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)
Multiple inbred founder lines are inter-mated for several generations prior to creating inbred lines, resulting in a diverse population whose genomes are fine scale mosaics of contributions from all founders.
The term balanced tertiary trisomic has three words of which (1) “trisomic” indicates the presence of extra chromosome, (2) “tertiary” indicates that the extra chromosome is a trans-located chromosome, and (3) “balanced” refers to the breeding behaviour of the trisomic.
Ramage defined the BTT as a tertiary trisomic constructed in such a way that the dominant allele of a marker gene, closely linked with the translocation breakpoint of the extra chromosome is carried on the extra chromosome, and the recessive allele is carried on the two normal chromosomes that constitute the diploid complement. The dominant marker gene may be located on the centromere segment or the trans-located segment of the extra chromosome.
Marker Assisted Selection in Crop BreedingPawan Chauhan
Marker Assisted Selection is a value addition to conventional methods of Crop Breeding. It has been gaining importance in plant breeding with new generation of plant breeders and to get accurate and fast desired result from plant breeding.
A new era of genomics for plant science research has opened due the complete genome sequencing projects of Arabidopsis thaliana and rice. The sequence information available in public database has highlighted the need to develop genome scale reverse genetic strategies for functional analysis (Till et al., 2003). As most of the phenotypes are obscure, the forward genetics can hardly meet the demand of a high throughput and large-scale survey of gene functions. Targeting Induced Local Lesions in Genome TILLING is a general reverse genetic technique that combines chemical mutagenesis with PCR based screening to identity point mutations in regions of interest (McCallum et al., 2000). This strategy works with a mismatch-specific endonuclease to detect induced or natural DNA polymorphisms in genes of interest. A newly developed general reverse genetic strategy helps to locate an allelic series of induced point mutations in genes of interest. It allows the rapid and inexpensive detection of induced point mutations in populations of physically or chemically mutagenized individuals. To create an induced population with the use of physical/chemical mutagens is the first prerequisite for TILLING approach. Most of the plant species are compatible with this technique due to their self-fertilized nature and the seeds produced by these plants can be stored for long periods of time (Borevitz et al., 2003). The seeds are treated with mutagens and raised to harvest M1 plants, which are consequently, self-fertilized to raise the M2 population. DNA extracted from M2 plants is used in mutational screening (Colbert et al., 2001). To avoid mixing of the same mutation only one M2 plant from each M1 is used for DNA extraction (Till et al., 2007). The M3 seeds produce by selfing the M2 progeny can be well preserved for long term storage. Ethyl methane sulfonate (EMS) has been extensively used as a chemical mutagen in TILLING studies in plants to generate mutant populations, although other mutagens can be effective. EMS produces transitional mutations (G/C, A/T) by alkylating G residues which pairs with T instead of the conservative base pairing with C (Nagy et al., 2003). It is a constructive approach for users to attempt a range of chemical mutagens to assess the lethality and sterility on germinal tissue before creating large mutant populations.
Association mapping, also known as "linkage disequilibrium mapping", is a method of mapping quantitative trait loci (QTLs) that takes advantage of linkage disequilibrium to link phenotypes to genotypes.Varioius strategey involved in association mapping is discussed in this presentation
Linkage and QTL mapping Populations and Association mapping population.
F2, Immortalized F2, Backcross (BC), Near isogenic lines (NIL), RIL, Double haploids(DH), Nested Association mapping (NAM), MAGIC and Interconnected populations.
MAGIC :Multiparent advanced generation intercross and QTL discovery Senthil Natesan
MAGIC or multiparent advanced generation inter-crosses is an experimental method that increases the precision with which genetic markers are linked to quantitative trait loci (QTL). This method was first introduced by (Mott et al., 2000) in animals as an extension of the advanced intercrossing (AIC) approach suggested by (Darvasi and Soller , 1995)for fine mapping multiple QTLs for multiple traits. Advanced Intercrossed Lines (AILs) are generated by randomly and sequentially intercrossing a population initially originating from a cross between two inbred lines.
MAGIC involves multiple parents, called founder lines, rather than bi-parental control. AILs increase the recombination events in small chromosomal regions for the purpose of fine mapping. These lines are then cycled through multiple generations of outcrossing. Each generation of random mating reduces the extent of linkage disequilibrium (LD), allowing the QTL to be mapped more accurately.
The term balanced tertiary trisomic has three words of which (1) “trisomic” indicates the presence of extra chromosome, (2) “tertiary” indicates that the extra chromosome is a trans-located chromosome, and (3) “balanced” refers to the breeding behaviour of the trisomic.
Ramage defined the BTT as a tertiary trisomic constructed in such a way that the dominant allele of a marker gene, closely linked with the translocation breakpoint of the extra chromosome is carried on the extra chromosome, and the recessive allele is carried on the two normal chromosomes that constitute the diploid complement. The dominant marker gene may be located on the centromere segment or the trans-located segment of the extra chromosome.
Marker Assisted Selection in Crop BreedingPawan Chauhan
Marker Assisted Selection is a value addition to conventional methods of Crop Breeding. It has been gaining importance in plant breeding with new generation of plant breeders and to get accurate and fast desired result from plant breeding.
A new era of genomics for plant science research has opened due the complete genome sequencing projects of Arabidopsis thaliana and rice. The sequence information available in public database has highlighted the need to develop genome scale reverse genetic strategies for functional analysis (Till et al., 2003). As most of the phenotypes are obscure, the forward genetics can hardly meet the demand of a high throughput and large-scale survey of gene functions. Targeting Induced Local Lesions in Genome TILLING is a general reverse genetic technique that combines chemical mutagenesis with PCR based screening to identity point mutations in regions of interest (McCallum et al., 2000). This strategy works with a mismatch-specific endonuclease to detect induced or natural DNA polymorphisms in genes of interest. A newly developed general reverse genetic strategy helps to locate an allelic series of induced point mutations in genes of interest. It allows the rapid and inexpensive detection of induced point mutations in populations of physically or chemically mutagenized individuals. To create an induced population with the use of physical/chemical mutagens is the first prerequisite for TILLING approach. Most of the plant species are compatible with this technique due to their self-fertilized nature and the seeds produced by these plants can be stored for long periods of time (Borevitz et al., 2003). The seeds are treated with mutagens and raised to harvest M1 plants, which are consequently, self-fertilized to raise the M2 population. DNA extracted from M2 plants is used in mutational screening (Colbert et al., 2001). To avoid mixing of the same mutation only one M2 plant from each M1 is used for DNA extraction (Till et al., 2007). The M3 seeds produce by selfing the M2 progeny can be well preserved for long term storage. Ethyl methane sulfonate (EMS) has been extensively used as a chemical mutagen in TILLING studies in plants to generate mutant populations, although other mutagens can be effective. EMS produces transitional mutations (G/C, A/T) by alkylating G residues which pairs with T instead of the conservative base pairing with C (Nagy et al., 2003). It is a constructive approach for users to attempt a range of chemical mutagens to assess the lethality and sterility on germinal tissue before creating large mutant populations.
Association mapping, also known as "linkage disequilibrium mapping", is a method of mapping quantitative trait loci (QTLs) that takes advantage of linkage disequilibrium to link phenotypes to genotypes.Varioius strategey involved in association mapping is discussed in this presentation
Linkage and QTL mapping Populations and Association mapping population.
F2, Immortalized F2, Backcross (BC), Near isogenic lines (NIL), RIL, Double haploids(DH), Nested Association mapping (NAM), MAGIC and Interconnected populations.
MAGIC :Multiparent advanced generation intercross and QTL discovery Senthil Natesan
MAGIC or multiparent advanced generation inter-crosses is an experimental method that increases the precision with which genetic markers are linked to quantitative trait loci (QTL). This method was first introduced by (Mott et al., 2000) in animals as an extension of the advanced intercrossing (AIC) approach suggested by (Darvasi and Soller , 1995)for fine mapping multiple QTLs for multiple traits. Advanced Intercrossed Lines (AILs) are generated by randomly and sequentially intercrossing a population initially originating from a cross between two inbred lines.
MAGIC involves multiple parents, called founder lines, rather than bi-parental control. AILs increase the recombination events in small chromosomal regions for the purpose of fine mapping. These lines are then cycled through multiple generations of outcrossing. Each generation of random mating reduces the extent of linkage disequilibrium (LD), allowing the QTL to be mapped more accurately.
In this presentation, we will delve into the principles of QTL mapping and explore various strategies for mapping QTLs in plants. We will also discuss the advantages and limitations, and provide insights into how QTL mapping is advancing our understanding of genetics.
Association genetics‟ or ‟association studies,” or ‟linkage disequilibrium mapping”.
Tool to resolve complex trait variation down to the sequence level by exploiting historical and evolutionary recombination events at the population level.
Natural population surveyed to determine MTA using LD.
what is association mapping, how LD is useful, how association mapping is useful in crop improvement. how to represent a association mapping analysis data, generalized model of association mapping
Genomic conflict-It arises when genes inside a genome are not transmitted by the same rules
Genes that cause such genomic conflict are called selfish genetic elements (also selfish DNA, ultra-selfish genes, genetic parasites) and can be harmful to the individual.
So selfish gene can be defined as stretches of DNA (genes, fragments of genes, noncoding DNA, portions of chromosomes, whole chromosomes, or sets of chromosomes) that act narrowly to advance their own interests—in other words, replication at the expense of the larger organism.
Here it also presented about what is genomic conflict, types of it, cytoplasmic inheritance, its relation with genomic conflict, ABC model, Molecular mechanism of CMS, Pollen hypothesis, ATP hypothesis, etc.
The green revolution is the significant increase in agricultural productivity resulting from the introduction of high - yield varieties of grains, use of pesticides , and improved management techniques.
MAPK Signaling pathway (Mitogen-activated protein kinase), how the pathway helps in regulation of mitosis, It's activation and inactivation inside the cell, roles of MAPK pathway in cancerous cell, different classes of MAP kinase in human
This presentation contains the Reproduction system of angiospermic plant, along with the production of the 2 gamets and it's fertilization and different pathways of the fertilization and factors affecting it(and much more).
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
What is greenhouse gasses and how many gasses are there to affect the Earth.moosaasad1975
What are greenhouse gasses how they affect the earth and its environment what is the future of the environment and earth how the weather and the climate effects.
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...Sérgio Sacani
Since volcanic activity was first discovered on Io from Voyager images in 1979, changes
on Io’s surface have been monitored from both spacecraft and ground-based telescopes.
Here, we present the highest spatial resolution images of Io ever obtained from a groundbased telescope. These images, acquired by the SHARK-VIS instrument on the Large
Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images
show that a plume deposit from a powerful eruption at Pillan Patera has covered part
of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive
optics at visible wavelengths.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...Sérgio Sacani
We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest
imaging field observed with JWST. We make use of the ancillary Hubble optical images (5 filters
spanning 0.4−0.9µm) and novel JWST images with 14 filters spanning 0.8−5µm, including 7 mediumband filters, and reaching total exposure times of up to 46 hours per filter. We combine all our data
at > 2.3µm to construct an ultradeep image, reaching as deep as ≈ 31.4 AB mag in the stack and
30.3-31.0 AB mag (5σ, r = 0.1” circular aperture) in individual filters. We measure photometric
redshifts and use robust selection criteria to identify a sample of eight galaxy candidates at redshifts
z = 11.5 − 15. These objects show compact half-light radii of R1/2 ∼ 50 − 200pc, stellar masses of
M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1
. Our search finds no candidates
at 15 < z < 20, placing upper limits at these redshifts. We develop a forward modeling approach to
infer the properties of the evolving luminosity function without binning in redshift or luminosity that
marginalizes over the photometric redshift uncertainty of our candidate galaxies and incorporates the
impact of non-detections. We find a z = 12 luminosity function in good agreement with prior results,
and that the luminosity function normalization and UV luminosity density decline by a factor of ∼ 2.5
from z = 12 to z = 14. We discuss the possible implications of our results in the context of theoretical
models for evolution of the dark matter halo mass function.
Nutraceutical market, scope and growth: Herbal drug technologyLokesh Patil
As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.
Comparing Evolved Extractive Text Summary Scores of Bidirectional Encoder Rep...University of Maribor
Slides from:
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Track: Artificial Intelligence
https://www.etran.rs/2024/en/home-english/
This presentation explores a brief idea about the structural and functional attributes of nucleotides, the structure and function of genetic materials along with the impact of UV rays and pH upon them.
Nucleic Acid-its structural and functional complexity.
QTL mapping for crop improvement
1.
2. QTL Mapping for Crop
Improvement
Presented by:
SANDEEP KUMAR SINGH
Adm. No. -02/PBG/Ph.D./17
Ph.D. Research scholar
(Plant Breeding and Genetics)
DEPARTMENT OF Plant Breeding and Genetics
COLLEGE OF AGRICULTURE,
ORISSA UNVERSITY OF AGRICULTURE AND TECHNOLOGY,
BHUBANESWAR, ODISHA-751003
Credit seminar
On
Chairman:
Dr. P. N. Jagdev
(Professor, Department of Plant Breeding and
Genetics, CA, OUAT, BBSR.)
3. Qualitative Quantitative
Qualitative Vs Quantitative traits
Few genes
Low environmental Influence
Distinct classes
Discontinuous variation
Polygenes
High environmental Influence
No distinct classes
Continuous variation
4. A gene or chromosomal region that affects a quantitative trait
Must be polymorphic (have allelic variation) to have an effect
in a population
Must be linked to a polymorphic marker allele to be detected
Term first coined by Gelderman in 1975.
What a QTL is?
5. QTLs have the following characteristics
These traits are controlled by multiple genes, each segregating
according to Mendel's laws.
These traits can also be affected by the environment to
varying degrees.
Many genes control any given trait and Allelic variations are
fully functional.
Individual gene effects is small &The genes involved can be
dominant, or co-dominant.
The genes involved can be subject to epistasis or pleiotrophic
effect.
6. A statistical method links
two types of information
Genotypic data
(Genetic Marker)
A linkage map of
polymorphic markers
Phenotypic data
(Quantitative Trait)
Variation within
a mapping population
QTL Analysis
Requirement of
QTL Analysis
9. Types of mapping population
Secondary MPPrimary MP
F2 Populattion
F2 derrived F3
Back cross
Multi Parent Advanced Generation Intercross (MAGIC) population
Double haploid (DH)
Recombinant Inbred Lines (RILs)
Near Isogenic Lines (NILs)
Chromosomal segment substitutional lines(CSSLs)
Advanced inter crossed lines
Immortalised F2
Recurrent selection back cross (RSB)
Inter connected mapping population
Mortal
Population
Immortal
Population
mortal
Population
10. •NIL: Introgression of a gene by repeated backcrossing
combined with selection for the gene.
** CSSL: Repeated backcrossing without selection; each
line has a distinct chromosome segment from the donor
parent.
@ RSB: The donor parent has high value for a quantitative
trait. In each back cross generation, the individual with the
highest value for the trait is selected and backcrossed to
the recurrent parent
A schematic
representation of the
various biparental
mapping populations
Adopted from Marker assisted plant breeding: Principle and Practices by B.D.Singh A.K.Singh
11.
12. F2 derived F3(F2:3) population
AA aa
F1
F2 AA Aa aa
All AA AA, Aa, aa All aaF3
Aa
Parents
Suitable for
Mapping quantitative
traits
Mapping recessive
genes
Useful for reconstitution
of individual F2
genotypes
Demerit
Like F2 population, it is
mortal
13.
14.
15.
16.
17. Immortalized F2 population
Parent 1 Parent 2
AAbb X aaBB
F1 AaBb
Conventional F2 population
Immortalized F2 population
(by open pollinating the RILs)
RILs produced from AAbb X aaBB
AABB, AAbb
aaBB, aabb
Six possible RIL
combinations
Six
heterozygous
genotypes
AABB X AAbb
X aaBB
X aabb
AABb
AaBB
AaBb
AAbb X aaBB
X aabb
AaBb
Aabb
aaBB X aabb aaBb
AB Ab aB ab
AB AAB
B
AABb AaBB AaBb
Ab AABb AAbb AaBb Aabb
aB AaBB AaBb aaBB aaBb
ab AaBb Aabb aaBb aabb
F2
Advantages
Population identical to the
conventional F2 population
can be produced and
replicated ‘n’ number of
times
Individual F2 genotypes
can be evaluated over the
years and locations
No need for genotyping
the immortalized F2s. Their
genotype can be deduced
based on their parental
RILs genotypes. Thus
economizing the cost of
mapping
It is possible to estimate
the additive X dominance
(j) and dominance X
dominance (l) effects
18. Chromosomal segment substitutional lines(CSSLs)
Phenotypic
characterization of each
line can reveal which
chromosome fragment
from the donor has the
gene(s) associated with an
interesting trait.
19. Advanced Inter crossed Lines (AIL)
Developed by intermating the individuals of F2 and subsequent generations from a
suitable cross.
Intermating in the segregating generations maintains heterozygosity in the population
and allows recombination between the QTLs and the markers linked to them in every
generation leading to a more precise location of the QTLs.
Advantages:
It was estimated that the confidence interval of QTLs would be reduced by up to five-
fold in AILs as compared to that in an F2 population (Darvasi and Soller 1995).
Disadvantages:
Appropriate statistical methods for modeling and analysis of the data from AILs are not
available
20. Recurrent selection back cross (RSB)
Given by Wright (1952).
F1 obtained from a cross between a homozygous line with high value for a quantitative
trait (the DP) and a homozygous line with low value for the trait (the RP) and the
subsequent backcross progeny are backcrossed to the RP.
In each backcross generation, a predetermined number of individuals with the top
phenotypic values (i.e., DP phenotype) for the trait are selected and backcrossed to the RP.
Advantages:
Used for high-resolution QTL mapping
Disadvantages:
High effort, resources, and time consuming.
RSB is suited for localization of large effect QTLs, while important quantitative traits like
yield are governed by moderate to low effects QTL.
22. Multi Parent Advanced Generation Intercross (MAGIC) population
Extension of AIL, proposed by Darvasi and Soller (1995) in Mice Mackay and Powell (2007)
It is differ from AIL with involvement of multi-parent
Disadvantages:
Large number of crossing
progrmme.
Time and labour consuming.
27. Linkage mapping
Finding those genes/markers that are linked together and co-inherited to
the next generation
Markers are mapped relative to one another on chromosomes and used
as signposts against which to map genes of interest that are linked with
marker
The distance between two genes - determined by their recombination
fraction
The map units centimorgan (cM)
1 cM = distance over which 1 crossover occurs (on average) per meiosis
(no general relationship between genetic distance and physical distance
in base pairs)
28. Mapping Functions
A mapping function translates recombination frequencies between two loci into a map
distance
Within small distances, a mapping function is simply:
map distance (d) = recombination fraction (r)
Two types of mapping functions
1. Haldane mapping function – When no interference exist (all crossovers occurs
independently of one another)
2. Kosambi mapping function – Allows some positive interference (one chiasma
deters the occurrence of the second in close proximity to the first)
29. Testing for Linkage – LOD (Log of Odds) scores
When 2 genes are segregating independently or not can be known by 2 method
1) Chi square test
2) LOD Score
Performs the likelihood of a certain recombination fraction (r) versus the
likelihood of no linkage ( r= 0.5)
LOD score - the log10 of this likelihood ratio
LOD score >3 --- null hypothesis (no linkage r= 0.5) is rejected (ratio of likelihoods
of 1000 to 1 ---- among the 1,000 plants, the chance of cross over is 1)
30. Mapping of genetic markers Genetic Segregation Ratio in
Different Marker-Population
Combinations
31. Bulk segregant analysis (BSA)
Resistant Parent Susceptible ParentX
F1
F2 individuals
R P S P R B S B R R S R S S
37. Single point analysis
Simplest and earliest method of QTL detection
In this method each marker is separately tested for its association with the targeted traits
based on linear model:
yj = μ + f (markerj) + ɛj, where
yj is trait value of the jth individual in the population, μ is population mean, f (markerj) is a function
of marker genotype, ɛj is the residual associated with the jth individual
SMA: (Soller and Brody, 1976)
38. • Marker genotypes treated as classification variable
- for a backcross (2 genotypes/ Classes): use t-test
- for F2 population (up to 3 genotypes/classes): use ANOVA
- For t-test individual in the population are classified according to the classes of genotype and
tested for its significance.
- Significant difference indicates the marker to be associated with the QTL affecting the trait.
- The chance of detection of QTL depends on:
1)the magnitude of the effect size of QTL (=yQq-yqq )
2) The recombination rate (r) between the trait and the marker
yMm-ymm=(1-r) (yQq-yqq)
So, for a given magnitude of QTL effect, larger the value of r, smaller will be the difference
in phenotypic mean of the 2 marker classes, same time the smaller will be the likelihood of
this difference being significant.
M Qr
40. 1. Conceptually and computationally
simple
2. Genetic linkage map
information not needed
3. Easily incorporates covariates
4. Informative when markers
sufficiently cover the genome
5. Can be extended to multiple
regression for multiple QTL model
1. Location and effects of detected QTLs are
Confounded larger QTL effect could be because the
marker is close to a QTL or farther from the QTL, but
the QTL contributes much significantly to the trait
2. QTL position cannot be precisely detected
3. Power to detect QTL is low when marker density
is low
4. Multiple comparison increases false positives
5. Missing genotypes are totally excluded from
analysis
6. Limited ability to separate linked QTLs and
no ability to assess interacting QTLs
Advantages Limitation
41. SIM: Lander & Botstein (1989)
Concept:
Based on joint segregation of a pair of adjacent markers and a putative QTL
within an interval flanked by the marker pair.
SIM makes a systematic linear or one dimensional search for a QTL at
several location say, at every 1 or 2 cm within each marker interval.
Genetic Model: yi=µ+axi+ei where, yi =trait phenotype of ith individual,
µ= Grand phenotypic mean of the population, a=QTL effect, xi=indicator
of QTL genotype, ei =random error term with σ2 as variance and mean as
0.
Xi represent the no. of positive allele at QTL locus for eg: 1 for Qq
genotype, 0 for qq genotype
M1 M2Q
r
r2r1
42. A linear regression programme use to estimates the (MLEs) Maximum Likelihood
estimates for µ, σ2 , and a of xi
The MLEs for these parameter are calculated again assuming that there is no QTL in the
marker interval.
The above MLEs are used to calculate the LOD score.
49. Limitation:
1) The arbitrariness in selection of co-factor for QTL analysis.
2) Unable to detect the interacting QTL. So, inefficient when epistasis is
present.
Using multiple marker intervals simultaneously to identify multiple putative
QTLs.
Study epistatic effects of QTLs.
MIM: Kao et al, 1999
50. Bayesian Multiple QTL mapping
Here a prior distribution is selected, from which the posterior
distribution is derived and inference are drawn from the posterior
distribution (here it is QTL).
It treat the QTL as random variable.
It has very little practical utility in case of bi-parental mapping
population
Limitation:
Difficulties in choosing the prior distribution
Complexities of the computation
Lack of user friendly software
55. Result:
A total of 12 QTLs were identified for sheath blight resistance using composite interval mapping. These QTLs
were located on chromosomes 1, 3,7, 8, 9 and 11 and the respective alleles explain 8.13– 26.05%, of the
total phenotypic variation
56.
57. Parent-Cocodrie (High yield in stress condition) x Vandana (Low yield under stress condition)
Mapping population-187 F2 : 3 families
Marker- 330 SSR markers
58. FIGURE 3 | Quantitiative trait loci on chromosomes 1, 5, 8, and 9 associated with grain yield under greenhouse
drought. QTLs (in green) represent the genomic regions associated with grain yield in non-stressed control
conditions. Markers identified through single marker analysis and within the QTL interval are depicted in bold
red fonts.
RESULT:
59. Table: Potential QTL’s mapped in rice using different mapping populations for various growth, physiological
and yield traits
Trait QTL Marker Population References
62. Identification of novel genes
Good alternative when mutant screening is laborious and Expensive
Small additive effects / epistatic loci are not detected and may require further analyses.
No. of QTLs detected, their position and effects are subjected to statistical error.
Future Prospects
Constant improvements of Molecular platforms
New Types of genetic materials( e.g. introgression lines: small effect QTLs
can be detected)
Advances in Bioinformatics
63. References:
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