The document discusses quantitative trait loci (QTL) mapping and its use in molecular marker-aided breeding. It provides details on QTL characteristics, mapping principles and methods. Key points include: QTLs control quantitative traits through the effects of multiple genes. QTL mapping identifies genomic regions associated with traits using DNA markers. Methods include single marker analysis, interval mapping and composite interval mapping. The document also presents a case study on QTL mapping for salt tolerance in rice using bulked segregant analysis with a 50K SNP chip, identifying 34 QTL regions including 29 novel loci.
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.
QTL mapping is the statistical study of the allels that occur in a locus and phenotypes that they produce.
Common name : Maize/ corn
Scientific name : Zea mays
No of chromosomes : 20
Linkage groups : 10
A total of 220 molecular markers were used in construction of linkage maps and to map QTL. One-hundred and six (RFLP) probes were
mapped to 110 diVerent loci for additional information
regarding the RFLPs In addition to the RFLPs, 32 SSRs and 78 SNPs were used to
construct the linkage map.
Used Mapping Population is : Back cross population
No of populations used : 02
Population size : 337 (144 & 193)
Disease chosen for QTLs : southern leaf blight (SLB)
QTL Cartographer version 2.5 was used for dQTL mapping.
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.
QTL mapping is the statistical study of the allels that occur in a locus and phenotypes that they produce.
Common name : Maize/ corn
Scientific name : Zea mays
No of chromosomes : 20
Linkage groups : 10
A total of 220 molecular markers were used in construction of linkage maps and to map QTL. One-hundred and six (RFLP) probes were
mapped to 110 diVerent loci for additional information
regarding the RFLPs In addition to the RFLPs, 32 SSRs and 78 SNPs were used to
construct the linkage map.
Used Mapping Population is : Back cross population
No of populations used : 02
Population size : 337 (144 & 193)
Disease chosen for QTLs : southern leaf blight (SLB)
QTL Cartographer version 2.5 was used for dQTL mapping.
This is a lecture for Bio4025, a graduate class at Washington University in St. Louis. Some slides are derived from Julin Maloof (University of California, Davis), some of which were altered.
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.
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.
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
Strategies for mapping of genes for agronomic traits in plantstusharamodugu
The genomic regions associated with the expression of a quantitative trait is referred to as quantitative trait loci (QTL).
A QTL may contain one or more genes affecting the concerned quantitative trait.
Sax(1923) reported linkage between seed coat colour and seed size, which are qualitative and quantitative traits in common bean and the work highlighted the basic principles for mapping of polygenes based on the detection of association between a quantitative trait phenotype and a genetic marker.
Thoday (1961) explored this QTL concept further by combining elaborate cytogenetic techniques with genetic analysis to map QTLs for several quantitative traits in Drosophila
This is a lecture for Bio4025, a graduate class at Washington University in St. Louis. Some slides are derived from Julin Maloof (University of California, Davis), some of which were altered.
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.
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.
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
Strategies for mapping of genes for agronomic traits in plantstusharamodugu
The genomic regions associated with the expression of a quantitative trait is referred to as quantitative trait loci (QTL).
A QTL may contain one or more genes affecting the concerned quantitative trait.
Sax(1923) reported linkage between seed coat colour and seed size, which are qualitative and quantitative traits in common bean and the work highlighted the basic principles for mapping of polygenes based on the detection of association between a quantitative trait phenotype and a genetic marker.
Thoday (1961) explored this QTL concept further by combining elaborate cytogenetic techniques with genetic analysis to map QTLs for several quantitative traits in Drosophila
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.
Role of molecular marker play a significant supplementary role in enhancing yield along with conventional plant breeding methods. the result obtain through molecular method are more accurate and at genotypic level. It had wider applications in field of plant breeding, biotechnology, physiology, pathology, entamology, etc. The mapping information obtained from these markers had created a revolution in the sequencing sector and open many pathways for developments, innovations and research.
QTL MAPPING AND APPROACHES IN BIPARENTAL MAPPING POPULATIONS.pptxPABOLU TEJASREE
• The loci controlling quantitative traits are called quantitative trait loci or QTL.
• Term first coined by Gelderman in 1975.
Principles of QTL mapping
• Genes and markers segregate via chromosome recombination during meiosis, thus allowing their analysis in the progeny.
• The detection of association between phenotype and genotype of markers.
• QTL analysis depends on the linkage disequilibrium.
• QTL analysis is usually undertaken in segregating mapping populations.
Key steps for the QTL mapping
• Collection of parental strains that differ for traits of interest
• Selection of molecular markers such as RFLP, SSR and SNP that distinguish between the two parents
• Development of a mapping population
• Genotyping and phenotyping of the mapping population
• Detection of QTL using a suitable statistical method
• For practical purposes, in general recombination events considered to be less than 10 recombinations per 100 meiosis, or a map distance of less than 10 centi Morgans(cM).
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.
Association mapping approaches for tagging quality traits in maizeSenthil Natesan
Association mapping has been widely used to study the genetic basis of complex traits in human and animal systems and is a very efficient and effective method for confirming candidate genes or for identifying new genes (Altshuler et al., 2008). Association mapping is now being increasingly used in a wide range of plants (Rafalski, 2010), where it appears to be more powerful than in humans or animals (Zhu et al., 2008). Unlike linkage mapping, association mapping can explore all the recombination events and mutations in a given population and with a higher resolution (Yu and Buckler, 2006). However, association mapping has a lower power to detect rare alleles in a population, even those with large effects, than linkage mapping (Hill et al., 2008). Yan et al., (2010) demonstrated that the gene encoding β-carotene hydroxylase 1 (crtRB1) underlies a principal quantitative trait locus associated with β-carotene concentration and conversion in maize kernels has been identified through candidate gene strategy of association mapping.
Advances in Molecular Cytogenetics: Potential for Crop Improvement.pptxKanshouwaModunshim
Title: Exploring Advances in Cytogenetics and Molecular Cytogenetics
Description:
Delve into the intricate world of cytogenetics and its cutting-edge counterpart, molecular cytogenetics, through this insightful presentation. Understand the profound relationship between chromosome structure, behavior, and gene function, with a particular focus on their relevance to crop improvement programs.
Key Points:
Introduction to Cytogenetics: Explore the fundamental principles of cytogenetics, its historical significance, and the recent influence of molecular tools, leading to the emergence of molecular cytogenetics.
Importance in Crop Improvement: Uncover the pivotal role of molecular cytogenetics in crop improvement programs, offering insights into the structural and functional organization of genomes within chromosomes.
Karyotyping: Gain a comprehensive understanding of karyotyping, its significance in identifying chromosomal abnormalities, and its applications in studying evolutionary relationships among different taxa.
Chromosome Identification and Sorting: Learn about the techniques involved in the identification and sorting of individual chromosomes, crucial steps in cytogenetics research for various crops.
Chromosome Banding Techniques: Explore different chromosome banding techniques, such as G-Banding and C-Banding, and understand their applications in detecting structural rearrangements.
CHIAS (Chromosome Image Analyzing System): Get insights into the CHIAS software and its role in mapping and identifying chromosomes automatically.
Flow Cytometry: Discover the applications of flow cytometry in detecting and measuring physical and chemical characteristics of cells, with a focus on its relevance in chromosome research.
In Situ Hybridization: Explore the technique of in situ hybridization, particularly the fluorescent variant, and its applications in precise localization of specific DNA segments.
Genomics and Whole Genome Sequencing: Delve into the realm of genomics and whole-genome sequencing, understanding the approaches like BAC to BAC and Whole Genome Shotgun.
Case Study: Uncover a case study involving the identification of a Wheat-Psathyrostachys huashanica ditelosomic addition line, showcasing the practical applications of the discussed techniques.
Conclusion: Summarize the key takeaways from the presentation, emphasizing the role of these techniques in advancing precision breeding and crop improvement.
Similar to Pavithra- Systems Biology- Molecular marker aided breeding (20)
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Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Honest Reviews of Tim Han LMA Course Program.pptxtimhan337
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How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
2. Quantitative Trait Loci
2
The loci controlling quantitative traits are called quantitative
trait loci or QTL.
Term first coined by Gelderman in 1975.
It is the region of the genome that is associated with an effect on
a quantitative trait.
It can be a single gene or cluster of linked genes that affect the
trait.
3. QTLs have the following
characteristics
3
These traits are controlled by multiple genes, each
segregating according to Mendel's laws.
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.
4. QTL Mapping
4
The process of constructing linkage maps and conducting
QTL analysis i.e. to identify genomic regions associated with
traits is known as QTL mapping.
Identification and location of polygenes or QTL by use of
DNA markers.
It involves testing DNA markers throughout the genome for
the likelihood that they are associated with a QTL.
5. Principles of QTL mapping
5
Genes and markers segregate via chromosome
recombination during meiosis, thus allowing their analysis
in the progeny.
The detection of association between phenotype and
genotype of markers.
Based on the presence or absence of a particular marker
loci, the mapping population is partitioned into different
genotypic groups and these groups are analyzed for
significant differences with respect to the trait
6. Objectives of QTL Mapping
6
The basic objective is to detect QTL, while minimizing the
occurrence of false positives.
To identify the regions of the genome that affects the trait of
interest.
To analyze the effect of the QTL on the trait.
How much of the variation for the trait is caused by a specific
region?
What is the gene action associated with the QTL – additive
effect? Dominant effect?
7. Prerequisites for QTL mapping
7
Availability of a good linkage map (this can be done at the same time the QTL mapping)
A segregating population derived from parents that differ for the trait(s) of interest, and
which allow for replication of each segregant, so that phenotype can be measured with
precision (such as RILs or DHs)
A good assay for the trait(s) of interest
Software available for analyses
Molecular Markers
Sophisticated Laboratory
8. Steps involved in QTL Mapping:
8
Selection of parental lines
Sufficient polymorphism
Parental lines are highly contrasting phenotypically
Genetically divergent
Selection of molecular markers (dominant/codominant)
Making crosses
Creation of mapping population
Phenotyping of the progenies
Genotyping of the progenies
Construction of linkage map
9. 9
Screening the mapping population using polymorphic
molecular markers
Segregation patterns
Data is then analyzed using a statistical package such as
MAPMAKER or JOINMAP
Assigning them to their linkage groups on the basis of
recombination values
10. Methods to detect QTLs
10
Single-marker analysis,
Simple interval mapping and
Composite interval mapping
11. Single-Marker Analysis (SMA)
11
Also known as single- point analysis. It is the simplest method
for detecting QTLs associated with single markers.
This method does not require a complete linkage map and
can be performed with basic statistical software programs.
The statistical methods used for single-marker analysis
include t-tests, analysis of variance (ANOVA) and linear
regression.
12. 12
Limitations
Likelihood of QTL detection significantly decreases as the distance
between the marker and QTL increases
It cannot determine whether the markers are associated with one or
more markers QTLs
To overcome these limitations the use of large number of segregating
DNA markers covering the entire genome may minimize these
problems. QGene and MapManager QTX are commonly used
computer programs to perform single-marker analysis.
13. Simple Interval Mapping (SIM)
13
It was first proposed by Lander and Bolstein.
It takes full advantages of the linkage map.
The principle behind interval mapping is to test a model for the
presence of a QTL at many positions between two mapped loci.
The use of linked markers for analysis compensates for recombination
between the markers and the QTL, and is considered statistically more
powerful compared to single-point analysis.
MapMaker/QTL and QGene are used to conduct SIM.
14. Composite Interval Mapping
(CIM)
14
Developed by Jansen and Stam in 1994
It combines interval mapping for a single QTL in a given
interval with multiple regression analysis on marker
associated with other QTL.
Combines interval mapping with linear regression and
includes additional genetic markers in the statistical model in
addition to an adjacent pair of linked markers for interval
mapping
More precise and effective at mapping QTL
QTL Cartographer, MapManager QTX and PLABQTL are
15. Logarithm of the odds ratio (LOD
score):
15
Linkage between markers is usually calculated using odds ratio.
This ratio is more conveniently expressed as the logarithm of the
ratio, and is called a logarithm of odds (LOD) value or LOD
score.
LOD values of >3 are typically used to construct linkage maps.
LOD of 2 means that it is 100 times more likely that a QTL exists
in the interval than that there is no QTL.
16. 16
LOD of 3 between two markers indicates that linkage
is 1000 times more likely (i.e. 1000:1) than no
linkage.
The LOD score is a measure of the strength of
evidence for the presence of a QTL at a particular
location.
17. 17
Comparison of methods of QTL Mapping
Particulars Interval
mapping
Composite
Interval
Mapping
Multiple
Interval
Mapping
Bayesian
Interval
Mapping
1
.
Markers
used
Two markers Markers used
as cofactors
Multiple
markers
Two markers
2
.
Information
obtained
about
Number and
position of
QTL
Number and
position of
QTL and
interaction of
QTLs
Number and
position of
QTL
Number and
position of
QTL and their
effects
3
.
Designated
as
SIM SIM MIM BIM
4
.
Precision High Very high Very high Very high
19. Merits of QTL Mapping
19
Identification of novel genes
Where mutant approaches fail to detect genes
with phenotypic functions, QTL mapping can help
Good alternative when mutant screening is
laborious and expensive e.g circadium rhythm
screens
Can identify new functional alleles of known
function genes e.g. Flowering time QTL
Natural variation studies provide insight into the
origins of plant evolution
20. LIMITATIONS
20
Mainly identifies loci with large effects.
Less strong ones can be hard to pursue.
No. of QTLs detected, their position and effects are subjected to
statistical error.
Small additive effects / epistatic loci are not detected and may
require further analyses.
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
21. CASE STUDY
21
MAPPING QTLS FOR SALT TOLERANCE IN
RICE (ORYZA SATIVA) BY BULKED
SEGREGANT ANALYSIS OF
RECOMBINANT INBRED LINES (RIL’S)
Sushma Tiwari, et al
JOURNAL:PLOS GENETICS
http://journals.plos.org/plosone/article?id=10.1371/jo
urnal.pone.0153610
22. 22
Rapid identification of QTLs for reproductive stages tolerance
using bulked segregant analysis(BSA) of bi-parental
recombinant inbredlines(RIL).
The parents and bulks were genotype using a 50K SNP chip to
identify genomic regions showing homogeneity for contrasting
allele showing polymorphic SNPs in the two bulks.
The method was validated further with ‘CSR27/MI48’ RILs used
earlier for mapping salt tolerance QTLs using low density SSR
markers.
BSA with 50K SNP chip revealed 5,021 polymorphic loci and 34
QTL regions. This not only confirmed the location of previously
mapped QTLs but also identified several new QTLs, and
provided a rapid way to scan the whole genome for mapping
QTLs for complex agronomic traits in rice.
23. 23
MATERIALS AND METHODS
A mapping population of 216 recombinant
inbred lines (RILs) was developed from across
between rice varieties CSR11 and MI48 using
single seed descent method.
Mapping QTLs for Salt Stress in Rice by BSA
Using 50K SNP Chip.
25. QTL positions identified in CSR27/MI48 population by BSA using 50k SNP
chip
Physical map position of QTLs with green color showing tolerant allele coming from
tolerant parent CSR27 (11loci), red color showing tolerant allele coming from
sensitive parent MI48 (23loci). Blue and violet bars represent earlier identified QTLs
by (Ammar et al and Panditeta) ,respectively
26. RESULTS AND DISCUSSION
In this study out of 34 QTLs of CSR27/MI48 population five QTLs were reported
earlier and found 29 novel QTL regions on rice chromosomes 1,2,3,5,6,9,11 and 12
due to dense SNP map of polymorphic locus covering all regions of the genome.
Earlier highest 41 QTLs have been reported by Ghomi et al, on all the 12 rice
chromosomes for salinity tolerance at seedling stage in rice.
There are several reports on QTL mapping for salt stress by SSR genotyping on
whole population in rice but no one has done QTL mapping by BSA approach for salt
stress in rice. It gives clear picture that QTL mapping effective in identification of
tolerant alleles.