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.
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
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.
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.
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
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.
Genome-wide association study (GWAS) technology has been a primary method for identifying the genes responsible for diseases and other traits for the past ten years. GWAS continues to be highly relevant as a scientific method. Over 2,000 human GWAS reports now appear in scientific journals. Our free eBook aims to explain the basic steps and concepts to complete a GWAS experiment.
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.
Molecular marker technology in studies on plant genetic diversityChanakya P
A molecular marker is a molecule contained within a sample taken from an organism (biological markers) or other matter. It can be used to reveal certain characteristics about the respective source. DNA, for example, is a molecular marker containing information about genetic disorders, genealogy and the evolutionary history of life. Specific regions of the DNA (genetic markers) are used to diagnose the autosomal recessive genetic disorder cystic fibrosis, taxonomic affinity (phylogenetics) and identity (DNA Barcoding). Further, life forms are known to shed unique chemicals, including DNA, into the environment as evidence of their presence in a particular location.Other biological markers, like proteins, are used in diagnostic tests for complex neurodegenerative disorders, such as Alzheimer's disease. Non-biological molecular markers are also used, for example, in environmental studies.
Introduction:
Proposed by Meuwissen et al. (2001)
GS is a specialized form of MAS, in which information from genotype data on marker alleles covering the entire genome forms the basis of selection.
The effects associated with all the marker loci, irrespective of whether the effects are significant or not, covering the entire genome are estimated.
The marker effect estimates are used to calculate the genomic estimated breeding values (GEBVs) of different individuals/lines, which form the basis of selection.
Why to go for genomic selection:
Marker-assisted selection (MAS) is well-suited for handling oligogenes and quantitative trait loci (QTLs) with large effects but not for minor QTLs.
MARS attempts to take into account small effect QTLs by combining trait phenotype data with marker genotype data into a combined selection index.
Based on markers showing significant association with the trait(s) and for this reason has been criticized as inefficient
The genomic selection (GS) scheme was to rectify the deficiency of MAS and MARS schemes. The GS scheme utilizes information from genome-wide marker data whether or not their associations with the concerned trait(s) are significant.
GEBV: GenomicEstimated Breeding Values-
The sum total of effects associated with all the marker alleles present in the individual and included in the GS model applied to the population under selection
Calculated on a single individual basis
Gene-assisted genomic selection:
A GS model that uses information about prior known QTLs, the targeted QTLs were accumulated in much higher frequencies than when the standard ridge regression was used
The sum total of effects associated with all the marker alleles present in the individual and included in the GS model applied to the population under selection
Calculated on a single individual basis
Population used:
Training population: used for training of the GS model and for obtaining estimates of the marker-associated effects needed for estimation of GEBVs of individuals/lines in the breeding population.
Breeding population: the population subjected to GS for achieving the desired improvement and isolation of superior lines for use as new varieties/parents of new improved hybrids.
Training population-
large enough: must be representative of the breeding population: max. trait variance with marker : by cluster analysis
should have either equal or comparable LD, LD decay rates with breeding populations
Updated by including individuals/lines from the breeding population
Training more than one generation
Low colinearity between markers is needed since high colinearity tends to reduce prediction accuracy of certain GS models. (colinearity disturbed by recombination)
Genome-wide association study (GWAS) technology has been a primary method for identifying the genes responsible for diseases and other traits for the past ten years. GWAS continues to be highly relevant as a scientific method. Over 2,000 human GWAS reports now appear in scientific journals. Our free eBook aims to explain the basic steps and concepts to complete a GWAS experiment.
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.
Molecular marker technology in studies on plant genetic diversityChanakya P
A molecular marker is a molecule contained within a sample taken from an organism (biological markers) or other matter. It can be used to reveal certain characteristics about the respective source. DNA, for example, is a molecular marker containing information about genetic disorders, genealogy and the evolutionary history of life. Specific regions of the DNA (genetic markers) are used to diagnose the autosomal recessive genetic disorder cystic fibrosis, taxonomic affinity (phylogenetics) and identity (DNA Barcoding). Further, life forms are known to shed unique chemicals, including DNA, into the environment as evidence of their presence in a particular location.Other biological markers, like proteins, are used in diagnostic tests for complex neurodegenerative disorders, such as Alzheimer's disease. Non-biological molecular markers are also used, for example, in environmental studies.
Introduction:
Proposed by Meuwissen et al. (2001)
GS is a specialized form of MAS, in which information from genotype data on marker alleles covering the entire genome forms the basis of selection.
The effects associated with all the marker loci, irrespective of whether the effects are significant or not, covering the entire genome are estimated.
The marker effect estimates are used to calculate the genomic estimated breeding values (GEBVs) of different individuals/lines, which form the basis of selection.
Why to go for genomic selection:
Marker-assisted selection (MAS) is well-suited for handling oligogenes and quantitative trait loci (QTLs) with large effects but not for minor QTLs.
MARS attempts to take into account small effect QTLs by combining trait phenotype data with marker genotype data into a combined selection index.
Based on markers showing significant association with the trait(s) and for this reason has been criticized as inefficient
The genomic selection (GS) scheme was to rectify the deficiency of MAS and MARS schemes. The GS scheme utilizes information from genome-wide marker data whether or not their associations with the concerned trait(s) are significant.
GEBV: GenomicEstimated Breeding Values-
The sum total of effects associated with all the marker alleles present in the individual and included in the GS model applied to the population under selection
Calculated on a single individual basis
Gene-assisted genomic selection:
A GS model that uses information about prior known QTLs, the targeted QTLs were accumulated in much higher frequencies than when the standard ridge regression was used
The sum total of effects associated with all the marker alleles present in the individual and included in the GS model applied to the population under selection
Calculated on a single individual basis
Population used:
Training population: used for training of the GS model and for obtaining estimates of the marker-associated effects needed for estimation of GEBVs of individuals/lines in the breeding population.
Breeding population: the population subjected to GS for achieving the desired improvement and isolation of superior lines for use as new varieties/parents of new improved hybrids.
Training population-
large enough: must be representative of the breeding population: max. trait variance with marker : by cluster analysis
should have either equal or comparable LD, LD decay rates with breeding populations
Updated by including individuals/lines from the breeding population
Training more than one generation
Low colinearity between markers is needed since high colinearity tends to reduce prediction accuracy of certain GS models. (colinearity disturbed by recombination)
Discussion of latest work on simulating "evolve and resequence" experiments. Covers issues brought up by Burke et al.'s 2010 paper and how the simulations in Baldwin-Brown et al. (2014) address them.
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.
QSAR STUDY ON READY BIODEGRADABILITY OF CHEMICALS. Presented at the 3rd Chemo...Kamel Mansouri
The goal of this study was to predict ready biodegradation of
chemicals by QSAR modeling. The dataset used for this purpose was
produced by the Japanese Ministry of International Trade and Industry
(MITI) with experimental results according to the OECD test guideline
301C. Molecular descriptors from Dragon 6 were calculated. Variable
selection coupled with classification methods were applied to find the
most predictive models with low cross-validation error rate. The best
models were after that validated using the preselected test set to check
its prediction reliability and for further analysis.
71st ICREA Colloquium "Intrinsically disordered proteins (id ps) the challeng...ICREA
The unbearable lightness of being (a protein)
Proteins adopt beautiful shapes that enable them to perform an incredible array of tasks. But these wiggly little creatures cannot stay still. Is this a nuisance or a blessing?
After a basic introduction to proteins, Xavier Barril focuses on the implications of protein flexibility for drug discovery. Showing that a rigid representation has been, and continues to be, extremely useful. He will present some of the failures and challenges in introducing a more realistic view, but also how the dynamic perspective is gaining ground thanks to the advances in structural biology and computational chemistry.
Xavier Salvatella discusses where the limit is: can proteins be completely disorganised? Can we study and understand intrinsically disordered proteins from a structural point of view? How can this class proteins perform functions if they have no structure? Why have they evolved? Is it ever going to be possible to modify the function of this class of proteins with small molecules, as we have learned to do with proteins that fold?
Proteins adopt beautiful shapes that enable them to perform an incredible array of tasks. But these wiggly little creatures cannot stay still. Is this a nuisance or a blessing?
Xavier Salvatella discusses where the limit is: can proteins be completely disorganised? Can we study and understand intrinsically disordered proteins from a structural point of view? How can this class proteins perform functions if they have no structure? Why have they evolved? Is it ever going to be possible to modify the function of this class of proteins with small molecules, as we have learned to do with proteins that fold?
Inferring microbial ecosystem function from community structureJeff Bowman
Poster presented at the OCB scoping workshop: Traits-based approaches to ocean life and at the Sustainable Oceans Symposium at the Earth Institute, Columbia University.
Bowman and Ducklow 2016 GRC Marine MicrobesJeff Bowman
In the marine environment bacterial communities are structured by a variety of physical and biogeochemical factors, including turbulence and carbon and nutrient availability. In turn bacterial community structure has a direct impact on the ecosystem functions performed by the community, including bacterial production and nutrient remineralization. To facilitate the incorporation of community structure data in biogeochemical models we’ve developed a technique to establish “modes” of community structure with emergent self-organizing maps (ESOMs). Using a multi-year time series of 16S rRNA amplicon data, flow cytometry, and biogeochemical data from a long term study site off the West Antarctic Peninsula we’ve observed that bacterial production, a key indicator of biomass flow in the marine ecosystem, is best described by a linear model combining flow cytometry observations and community mode. Here we use 16S rRNA gene fragments recovered from the Tara global ocean expedition dataset to identify bacterial modes, equivalent to biomes, for the global ocean. For biogeochemical context we compare these biomes to data available from WOCE and JGOFS. To identify microbial “dark matter” captured in the Tara dataset we compared the results of a metabolic inference conducted with paprica to direct observations of metabolic pathways. Poor correlations between the metabolic inference and direct observations indicate samples with poor genomic representation among the completed genomes in Genbank.
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.
Similar to Mapping and Applications of Linkage Disequilibrium and Association Mapping in Crop Plants (20)
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.
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.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
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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.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
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
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Safalta Digital marketing institute in Noida, provide complete applications that encompass a huge range of virtual advertising and marketing additives, which includes search engine optimization, virtual communication advertising, pay-per-click on marketing, content material advertising, internet analytics, and greater. These university courses are designed for students who possess a comprehensive understanding of virtual marketing strategies and attributes.Safalta Digital Marketing Institute in Noida is a first choice for young individuals or students who are looking to start their careers in the field of digital advertising. The institute gives specialized courses designed and certification.
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4. Linkage
• Loci that are close enough together on the
same chromosome to deviate from
independent assortment are said to display
genetic linkage
BUT
• The linked loci that are far from each others
are in danger of
CROSSINGOVER
5. Deviations from independent
assortment
In the early 1900s, William Bateson and R. C. Punnett
were studying inheritance of two genes in the sweet
pea.
In a standard self of a dihybrid F1, the F2 did not show
the 9:3:3:1 ratio predicted by the principle of
independent assortment.
In fact Bateson and Punnett noted that certain
combinations of alleles showed up more often than
expected, almost as though they were physically
attached in some way. They had no explanation for this
discovery.
6. Thomas Hunt Morgan found a similar deviation
from Mendel’s second law while studying two
autosomal genes in Drosophila. Morgan
proposed a hypothesis to explain the
phenomenon of apparent allele association.
One of the genes affected eye color (pr, purple, and pr, red), and
the other wing length (vg, vestigial, and vg, normal). The wild-
type alleles of both genes are dominant.
DEVIATIONS FROM INDEPENDENT ASSORTMENT
7. When two genes are close together on the same chromosome pair (i.e.,
linked), they do not assort independently.
8.
9. • Chiasmata (the visible
manifestations of
crossing-over): a cross-
shaped structure
forming the points of
contact between non-
sister chromatides of
homologous
chromosomes.
10.
11. Frequencies of recombinants arising from
crossing-over. The frequencies of such
recombinants are less than 50 percent.
12. Linkage maps (distance between the genes.)
• Recombinant frequencies are significantly lower
than 50 percent and the recombinant frequency
was 12.97 percent.
(146+157) * 100 / 2335 = 12.97
• Morgan studied
– linked genes,
– proportion of recombinant progeny
– varied considerably,
• Morgan concluded actual distances separating
genes on the chromosomes.
• Alfred Sturtevant suggested that we can use this
percentage of recombinants as a quantitative
index of the linear distance between two genes
on a genetic map, or linkage map.
13. • Sturtevant postulated the greater the distance
between the linked genes, the greater the chance
of crossovers in the region between the genes.
• Sturtevant defined one genetic map unit (m.u.)
as that distance between genes for which one
product of meiosis in 100 is recombinant. Put
another way, a recombinant frequency (RF) of
0.01 (1 percent) is defined as 1 m.u. A map unit is
sometimes referred to as a centimorgan (cM) in
honor of Thomas Hunt Morgan.
LINKAGE MAPS (DISTANCE BETWEEN THE GENES.)
14.
15. A chromosome region containing three linked genes. Calculation of AB and
AC distances leaves us with the two possibilities shown for the BC distance.
Recombination between linked genes can be used to map their distance
apart on the chromosome. The unit of mapping (1 m.u.) is defined as a
recombinant frequency of 1 percent.
16. example
For the v and ct loci 89+94+3+5 =191
For the ct and cv, loci 45+40+3+5 = 93
For the v and cv, loci 45+40+89+94 = 268
20. Morphological Markers
1. Small Number
2. Limited genomic coverage
3. Could be influence by environment
4. Most of them exhibit dominance nature
21. Linkage Mapping
• Genes are points on the genome and there are a
flanking regions around them link to these genes.
• The central idea of the linkage mapping is to put a
lot of points on the genome in order to get points
that linked to another interesting points (genes).
• These points that we add are called as:
“MARKERS”
23. Linkage mapping
populations
The mapping resolution and the genetic
diversity in the linkage mapping
populations will depend on the number
of founders, generations of inter-mating
and generations
of selfing.
AI-RILs, advanced intercross–
recombinant inbred lines
HIF, heterogeneous inbred family
MAGIC lines, multiparent
advanced generation intercross
lines
NIL, near-isogenic line
RILs, recombinant inbred lines
(Bergelson and Roux, 2010) Nature Review, Genetics (December), Vol 11: 867-879
24. Hamwieh et al. 2005
Molecular markers:
•RFLP
•AFLP
•RAPD
•SSR
•SNP
•STS
•ISSR
Genetic map of lentil
RAPD
AFLP
SSR
35. Softwares
ProgramSystemLic.InterfacePop. TypesRef.
CARTHAGENEWin, UNIXFree
Graphical,
Command line
F2, backcross,
RIL, outcross
de Givry et al.
2005
CRIMAPWin, UNIXFreeCommand linepedigree
Green et al
1990
JOINMAPWinCom.Graphical
F2, backcross,
RIL, DH, outcross
Stam 1993
LINKMFEXWinFreeGraphicaloutcross
Danzann and
Gharbi 2001
MAPMAKER
Win,UNIX,
MAC
FreeCommand line
F2, backcross,
RIL, DH
Landr et al.
1987
MAPMANAGERWin, MACFreeGraphical
F2, backcross,
RIL
Manly and
Olson 1999
36. QTL mapping
• genotype and phenotype individuals
• look for statistical correlation between
genotype and phenotype
37. Quantitative trait loci (QTL) analysis:
Correlate segregation of the
quantitative trait with that of
qualitative trait, i.e., markers
38. Marker Distance
Line1
Line2
Line3
Line4
Line5
Line6
Line7
Line8
Line9
Line10
Line11
Line12
Line13
Line14
Line15
Line16
_3_0363_ 0 A B B A A A B A B B A B B B B B
_1_1061_ 0.8 A B B A A A B A B B A A A B B A
_3_0703_ 1.5 B A A B B B A B A A B B B B B B
_1_1505_ 1.5 B A A B B B A B A B B B B B B B
_1_0498_ 1.5 B B B B B B B B B B B B B B B A
_2_1005_ 3.8 A B B A A A B A B A A B B B B B
_1_1054_ 3.8 A A A A A A A A A B A A A A A A
_2_0674_ 6 A B B A A A B A B A A A A A A B
_1_0297_ 8.8 A A B B B B B A A A A A A A A B
_1_0638_ 10.7 A A B B B B B A A B A A A A A A
_1_1302_ 11.4 B A A A B B A A A B A B B B B A
_1_0422_ 11.4 B A A A B B A A A B A B B B B A
_2_0929_ 15.3 A B B B A A B B B A B A A A A B
_3_1474_ 15.4 A B B B A A B B B A B A A A A A
_1_1522_ 17.3 A B B B A A B B B A B A A A A A
_2_1388_ 17.3 A A A A A A A A A A A A A A A A
_3_0259_ 18.1 B B B B B B B B B B B A A A A A
_1_0325_ 18.1 B B B B B B B B B B B A A A A A
_2_0602_ 20.8 A A B A A A A B A B A A A A A A
_1_0733_ 23.9 B B B B B B B B B B B A A A A A
_2_0729 23.9 B B B B B B B B B B B A A A A A
_1_1272_ 23.9 A B B B A A B B B B B B B B B B
_2_0891_ 26.1 A A A A A A A A A B A A A A A A
_2_0748_ 26.6 B B B B B B B B B A B B B B B B
_3_0251_ 27.4 A B A A A B A A A B A A A B A A
_1_0997_ 35.5 B B A A A B B B B B B B B B B B
_1_1133_ 41.8 B B A A A B B B B A B A A A A A
_2_0500_ 42.5 A A A A A A A A A B A B B B B B
_3_0634_ 43.3 B B B B B B B B B A B A A A A A
0
10
5Disease
severity
39. Ref.Software
Lander et al. 1987MapMaker/QTL
Basten et al. 1999QTL Cartographer
Broman et al. 2003R/qtl
Mester et al. 2004MultiQTL
van Ooijen and Maliepaard 1996MapQTL
Seaton et al. 2002QTL Express
Utz and Melchinger 1996PLABQTL
Meer et al. 2004MapManager/QTX
Wang et al. 2003WebQTL
Yang et al. 2005QTLNetwork
QTL Detection Softwares
45. Hamwieh, A., Udupa, S., Sarker, A., Jung, C. and Baum, M. (2009). Development of new microsatellite markers and their application in the
analysis of genetic diversity in lentils. Breeding Science 59: 77-86.
Project 2: Genetic diversity in lentils
46. 300 accessions2915 accessions
Chickpea Reference Set (GCP)
Upadhyaya HD, Dwivedi SL, Baum M, Varshney RK, Udupa SM, Gowda CLL, Hoisington D and Singh S (2008) Genetic structure, diversity, and
allelic richness in composite collection and reference set in chickpea (Cicer arietinum L.). BMC Plant Biology 8: 106.
47. Allele frequency
–frequency (A) = p,
–frequency (B) = q,
then the next generation will have:
–frequency of the AA genotype = p2
–The frequency of the AB genotype = 2pq
–The frequency of the BB genotype = q2
48. Allele and Genotype Frequencies in H-
W equilibrium
p2 (AA)
2pq (Aa)
q2 (aa)
49. Hardy-Weinberg Equilibrium
Hardy–Weinberg equilibrium
Females
A (p) a (q)
Males
A (p) AA (p2) Aa (pq)
a (q) Aa (pq) aa (q2)
(p2) + (2pq) + (q2) = 1
P= AA + ½ Aa
q= aa + ½ Aa
where p is the frequency of the A allele, q is the frequency of the a allele, and p + q= 1.
51. • LD is measuring non
random association
between alleles
m2
m3
m4
m5
m6
m7
m8
m9m1
52. Hardy–Weinberg equilibrium
p + q = 1
p2 + 2pq + q2 = 1
Example
p: is the frequency of the dominant allele.
p: is the frequency of the recessive allele.
p2:is the frequency of individuals with the homozygous dominant genotype.
2pq: is the frequency of individuals with the heterozygous genotype.
q2 :is the frequency of individuals with the homozygous recessive genotype.
53. Hardy–Weinberg equilibrium
p + q = 1
p2 + 2pq + q2 = 1
The frequency of white fruits is 160, the homozygous recessive genotype, as they have
only one genotype, (bb). Black fruits can have either the genotype (Bb) or the genotype
(BB), and therefore, the frequency cannot be directly determined. Population size is 1000.
𝑓𝑟𝑒𝑞𝑢𝑒𝑛𝑐𝑒 𝑜𝑓 𝑖𝑛𝑑𝑖𝑣𝑖𝑑𝑢𝑎𝑙 =
𝐼𝑛𝑑𝑖𝑣𝑖𝑑𝑢𝑎𝑙
𝑇𝑜𝑡𝑎𝑙 𝑝𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛
160
1000
= 0.16
bb = q2 = 0.16 q = 0.4 p = 1 – q p = 1 – 0.4 = 0.6
2pq = 2 X 0.6 X 0.4 = 0.48 p2 = 0.62 = 0.36
q2 X total population = 0.16 X 1000 = 160 White fruits, bb genotype
p2 X total population = 0.36 X 1000 = 360 Black fruits, BB genotype
2pq X total population = 0.48 X 1000 = 480 Black fruits, Bb genotype
55. Introduction to Linkage Disequilibrium
B b Total
A PAB PaB PA
a PaB Pab Pa
Total PB Pb 1.0
A B
A b
a B
a b
A, B: major alleles
a, b: minor alleles
PA: probability for A alleles at SNP1
Pa: probability for a alleles at SNP1
PB: probability for B alleles at SNP2
PB: probability for b alleles at SNP2
PAB: probability for AB haplotypes
Pab: probability for ab haplotypes
SNP1 SNP2
56. Linkage Equilibrium
• PAB = PAPB
• PAb = PAPb = PA(1-PB)
• PaB = PaPB = (1-PA) PB
• Pab = PaPb = (1-PA) (1-PB)
B b Total
A PAB PAb PA
a PaB Pab Pa
Total PB Pb 1.0
SNP1
SNP2
58. Linkage Disequilibrium
PAB ≠ PAPB DAB=PAB-PAPB
D’ = D/DmaxWhen D≥ 0
Dmax is the smaller of p1q2 and p2q1
D’ = D/DminWhen D≤ 0
Dmin is the larger of -p1q2 and -p2q1
59. Linkage Disequilibrium
Another LD measure is r2 and this is calculated as the following:
r2= D2/(p1p2q1q2)
0 ≤ r2 ≤ 1
r2 = 0: Loci in complete linkage equilibrium
r2 = 1: Loci are in complete linkage disequilibrium
60. Haplotype Observed Frequency
A1B1 0.6
A1B2 0.1
A2B1 0.2
A2B2 0.1
Example
SNP locus A: A1 = T, A2 = C
SNP locus B: B1 = A, B2 = G
Allele Symbol Allelic freq.
A1 p1 0.7
A2 p2 0.3
B1 q1 0.8
B2 q2 0.2
D=0.6-(0.7 * 0.8) D = 0.04 D>0 then we use Dmax
p1q2 = 0.14
p2q1 = 0.24
D’ = 0.04/0.14 = 0.286
r2= (0.04)^2/(0.7*0.3*0.8*0.2)
r2= 0.048
65. 65
An Example of LD Bins (1/3)
• SNP1 and SNP2 can not form an LD bin.
– e.g., A in SNP1 may imply either G or A in SNP2.
Individual SNP1 SNP2 SNP3 SNP4 SNP5 SNP6
1 A G A C G T
2 T G C C G C
3 A A A T A T
4 T G C T A C
5 T A C C G C
6 T G C T A C
7 A A A T A T
8 A A A T A T
66. 66
An Example of LD Bins (2/3)
• SNP1, SNP2, and SNP3 can form an LD bin.
– Any SNP in this bin is sufficient to predict the values of others.
Individual SNP1 SNP2 SNP3 SNP4 SNP5 SNP6
1 A G A C G T
2 T G C C G C
3 A A A T A T
4 T G C T A C
5 T A C C G C
6 T G C T A C
7 A A A T A T
8 A A A T A T
67. 67
An Example of LD Bins (3/3)
• There are three LD bins, and only three tag SNPs are required to
be genotyped (e.g., SNP1, SNP2, and SNP4).
Individual SNP1 SNP2 SNP3 SNP4 SNP5 SNP6
1 A G A C G T
2 T G C C G C
3 A A A T A T
4 T G C T A C
5 T A C C G C
6 T G C T A C
7 A A A T A T
8 A A A T A T
73. Genome-Wide Association Studies (GWAS): Hunting for Genes in
the New Millennium
•GWAS scan the
genomes of thousands of
individuals who have a
particular phenotype for
DNA sequences that they
share, but are much
rarer in individual who
do not have the trait
•GWAS: to identify of
new regions containing
no a priori candidate
genes, and potentially
enhancing the
knowledge of complex
traits.
Accessions with disorder Accessions without disorder
The new way to track genes (Genome wide association)
74. Advantages of combining association and
traditional linkage mapping methods.
(Bergelson and Roux, 2010) Nature Review, Genetics(December), Vol 11: 867-879