The document discusses MAGIC (Multi-parent Advanced Generation Inter-Cross) populations, which are a new resource for plant genetics that allow for more precise mapping of quantitative trait loci (QTLs). MAGIC populations are developed by intercrossing multiple diverse founder lines over multiple generations, increasing recombination and genetic diversity. This allows for both coarse and fine mapping of QTLs contributing to complex traits. The document provides details on the development of MAGIC populations, including founder selection, mixing, advanced intercrossing, and inbreeding. It also discusses applications in breeding programs and case studies mapping traits like disease resistance in rice MAGIC populations.
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.
Introduction:
Proposed by Meuwissen et al. (2001)
GS is a specialized form of MAS, in which information from genotype data on marker alleles covering the entire genome forms the basis of selection.
The effects associated with all the marker loci, irrespective of whether the effects are significant or not, covering the entire genome are estimated.
The marker effect estimates are used to calculate the genomic estimated breeding values (GEBVs) of different individuals/lines, which form the basis of selection.
Why to go for genomic selection:
Marker-assisted selection (MAS) is well-suited for handling oligogenes and quantitative trait loci (QTLs) with large effects but not for minor QTLs.
MARS attempts to take into account small effect QTLs by combining trait phenotype data with marker genotype data into a combined selection index.
Based on markers showing significant association with the trait(s) and for this reason has been criticized as inefficient
The genomic selection (GS) scheme was to rectify the deficiency of MAS and MARS schemes. The GS scheme utilizes information from genome-wide marker data whether or not their associations with the concerned trait(s) are significant.
GEBV: GenomicEstimated Breeding Values-
The sum total of effects associated with all the marker alleles present in the individual and included in the GS model applied to the population under selection
Calculated on a single individual basis
Gene-assisted genomic selection:
A GS model that uses information about prior known QTLs, the targeted QTLs were accumulated in much higher frequencies than when the standard ridge regression was used
The sum total of effects associated with all the marker alleles present in the individual and included in the GS model applied to the population under selection
Calculated on a single individual basis
Population used:
Training population: used for training of the GS model and for obtaining estimates of the marker-associated effects needed for estimation of GEBVs of individuals/lines in the breeding population.
Breeding population: the population subjected to GS for achieving the desired improvement and isolation of superior lines for use as new varieties/parents of new improved hybrids.
Training population-
large enough: must be representative of the breeding population: max. trait variance with marker : by cluster analysis
should have either equal or comparable LD, LD decay rates with breeding populations
Updated by including individuals/lines from the breeding population
Training more than one generation
Low colinearity between markers is needed since high colinearity tends to reduce prediction accuracy of certain GS models. (colinearity disturbed by recombination)
Presentation delivered by Dr. Jesse Poland (Kansas State University, USA) at Borlaug Summit on Wheat for Food Security. March 25 - 28, 2014, Ciudad Obregon, Mexico.
http://www.borlaug100.org
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.
Introduction:
Proposed by Meuwissen et al. (2001)
GS is a specialized form of MAS, in which information from genotype data on marker alleles covering the entire genome forms the basis of selection.
The effects associated with all the marker loci, irrespective of whether the effects are significant or not, covering the entire genome are estimated.
The marker effect estimates are used to calculate the genomic estimated breeding values (GEBVs) of different individuals/lines, which form the basis of selection.
Why to go for genomic selection:
Marker-assisted selection (MAS) is well-suited for handling oligogenes and quantitative trait loci (QTLs) with large effects but not for minor QTLs.
MARS attempts to take into account small effect QTLs by combining trait phenotype data with marker genotype data into a combined selection index.
Based on markers showing significant association with the trait(s) and for this reason has been criticized as inefficient
The genomic selection (GS) scheme was to rectify the deficiency of MAS and MARS schemes. The GS scheme utilizes information from genome-wide marker data whether or not their associations with the concerned trait(s) are significant.
GEBV: GenomicEstimated Breeding Values-
The sum total of effects associated with all the marker alleles present in the individual and included in the GS model applied to the population under selection
Calculated on a single individual basis
Gene-assisted genomic selection:
A GS model that uses information about prior known QTLs, the targeted QTLs were accumulated in much higher frequencies than when the standard ridge regression was used
The sum total of effects associated with all the marker alleles present in the individual and included in the GS model applied to the population under selection
Calculated on a single individual basis
Population used:
Training population: used for training of the GS model and for obtaining estimates of the marker-associated effects needed for estimation of GEBVs of individuals/lines in the breeding population.
Breeding population: the population subjected to GS for achieving the desired improvement and isolation of superior lines for use as new varieties/parents of new improved hybrids.
Training population-
large enough: must be representative of the breeding population: max. trait variance with marker : by cluster analysis
should have either equal or comparable LD, LD decay rates with breeding populations
Updated by including individuals/lines from the breeding population
Training more than one generation
Low colinearity between markers is needed since high colinearity tends to reduce prediction accuracy of certain GS models. (colinearity disturbed by recombination)
Presentation delivered by Dr. Jesse Poland (Kansas State University, USA) at Borlaug Summit on Wheat for Food Security. March 25 - 28, 2014, Ciudad Obregon, Mexico.
http://www.borlaug100.org
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.
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
Genomic aided selection for crop improvementtanvic2
In last Several years novel genetic and genomics approaches are expended. Genetics and genomics have greatly enhanced our understanding of the structural and functional aspects of plant genomes.
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.
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.”
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 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.
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
Genomic aided selection for crop improvementtanvic2
In last Several years novel genetic and genomics approaches are expended. Genetics and genomics have greatly enhanced our understanding of the structural and functional aspects of plant genomes.
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.
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.”
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.
Marker assisted selection is the breeding strategy in which selection for a gene is based on molecular markers closely linked to the gene of interest rather than the gene itself, and the markers are used to monitor the incorporation of the desirable allele from the donor source. Selection of a genotype carrying desirable gene via linked marker (s) is called Marker Assisted Selection. MAS can be applied to possible to use this kind of information.
The prerequisites for the classical procedure of MAS are the tight linkage between molecular marker and gene of interest and high heritability of the gene of interest. It is noteworthy that the “quality” and the number of markers have a major impact on the success of MAS. The quality of markers relates to their characteristics and to the cost and the efficiency of the genotyping process. The number of markers affects the reliability of the linkage between them and the gene(s). In other words, screening a large number of markers has the potential to identify close and reliable linkage between the marker and the gene of interest. MAS has greater potential for efficient gene pyramiding combining several important genes in one cultivar. MAS is gaining considerable importance as it can improve the efficiency of plant breeding through precise transfer of genomic regions of interest and acceleration of the recovery of the recurrent parent genome. Marker-assisted selection is gaining considerable importance as it would improve the efficiency of plant breeding through precise transfer of genomic regions of interest (foreground selection) and accelerating the recovery of the recurrent parent genome (background selection). The use of MAS in crop improvement will not only reduce the cost of developing new varieties but will also increase the precision and efficiency of selection in the breeding program as well as lessen the number of years required to come up with a new crop variety.
TILLING AND ECO TILLING IN CROP IMPROVEMENT.pptxrushitahakik1
TILLING AND ECO TILLING in crop Improvement
A Reverse genetics Tool that enhences the potential to introduce specific mutation in oplants in order to improve crop diversity. i.e. Biotechnology beyound Genetically Modified crops.
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.
Cancer cell metabolism: special Reference to Lactate PathwayAADYARAJPANDEY1
Normal Cell Metabolism:
Cellular respiration describes the series of steps that cells use to break down sugar and other chemicals to get the energy we need to function.
Energy is stored in the bonds of glucose and when glucose is broken down, much of that energy is released.
Cell utilize energy in the form of ATP.
The first step of respiration is called glycolysis. In a series of steps, glycolysis breaks glucose into two smaller molecules - a chemical called pyruvate. A small amount of ATP is formed during this process.
Most healthy cells continue the breakdown in a second process, called the Kreb's cycle. The Kreb's cycle allows cells to “burn” the pyruvates made in glycolysis to get more ATP.
The last step in the breakdown of glucose is called oxidative phosphorylation (Ox-Phos).
It takes place in specialized cell structures called mitochondria. This process produces a large amount of ATP. Importantly, cells need oxygen to complete oxidative phosphorylation.
If a cell completes only glycolysis, only 2 molecules of ATP are made per glucose. However, if the cell completes the entire respiration process (glycolysis - Kreb's - oxidative phosphorylation), about 36 molecules of ATP are created, giving it much more energy to use.
IN CANCER CELL:
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
introduction to WARBERG PHENOMENA:
WARBURG EFFECT Usually, cancer cells are highly glycolytic (glucose addiction) and take up more glucose than do normal cells from outside.
Otto Heinrich Warburg (; 8 October 1883 – 1 August 1970) In 1931 was awarded the Nobel Prize in Physiology for his "discovery of the nature and mode of action of the respiratory enzyme.
WARNBURG EFFECT : cancer cells under aerobic (well-oxygenated) conditions to metabolize glucose to lactate (aerobic glycolysis) is known as the Warburg effect. Warburg made the observation that tumor slices consume glucose and secrete lactate at a higher rate than normal tissues.
This pdf is about the Schizophrenia.
For more details visit on YouTube; @SELF-EXPLANATORY;
https://www.youtube.com/channel/UCAiarMZDNhe1A3Rnpr_WkzA/videos
Thanks...!
Introduction:
RNA interference (RNAi) or Post-Transcriptional Gene Silencing (PTGS) is an important biological process for modulating eukaryotic gene expression.
It is highly conserved process of posttranscriptional gene silencing by which double stranded RNA (dsRNA) causes sequence-specific degradation of mRNA sequences.
dsRNA-induced gene silencing (RNAi) is reported in a wide range of eukaryotes ranging from worms, insects, mammals and plants.
This process mediates resistance to both endogenous parasitic and exogenous pathogenic nucleic acids, and regulates the expression of protein-coding genes.
What are small ncRNAs?
micro RNA (miRNA)
short interfering RNA (siRNA)
Properties of small non-coding RNA:
Involved in silencing mRNA transcripts.
Called “small” because they are usually only about 21-24 nucleotides long.
Synthesized by first cutting up longer precursor sequences (like the 61nt one that Lee discovered).
Silence an mRNA by base pairing with some sequence on the mRNA.
Discovery of siRNA?
The first small RNA:
In 1993 Rosalind Lee (Victor Ambros lab) was studying a non- coding gene in C. elegans, lin-4, that was involved in silencing of another gene, lin-14, at the appropriate time in the
development of the worm C. elegans.
Two small transcripts of lin-4 (22nt and 61nt) were found to be complementary to a sequence in the 3' UTR of lin-14.
Because lin-4 encoded no protein, she deduced that it must be these transcripts that are causing the silencing by RNA-RNA interactions.
Types of RNAi ( non coding RNA)
MiRNA
Length (23-25 nt)
Trans acting
Binds with target MRNA in mismatch
Translation inhibition
Si RNA
Length 21 nt.
Cis acting
Bind with target Mrna in perfect complementary sequence
Piwi-RNA
Length ; 25 to 36 nt.
Expressed in Germ Cells
Regulates trnasposomes activity
MECHANISM OF RNAI:
First the double-stranded RNA teams up with a protein complex named Dicer, which cuts the long RNA into short pieces.
Then another protein complex called RISC (RNA-induced silencing complex) discards one of the two RNA strands.
The RISC-docked, single-stranded RNA then pairs with the homologous mRNA and destroys it.
THE RISC COMPLEX:
RISC is large(>500kD) RNA multi- protein Binding complex which triggers MRNA degradation in response to MRNA
Unwinding of double stranded Si RNA by ATP independent Helicase
Active component of RISC is Ago proteins( ENDONUCLEASE) which cleave target MRNA.
DICER: endonuclease (RNase Family III)
Argonaute: Central Component of the RNA-Induced Silencing Complex (RISC)
One strand of the dsRNA produced by Dicer is retained in the RISC complex in association with Argonaute
ARGONAUTE PROTEIN :
1.PAZ(PIWI/Argonaute/ Zwille)- Recognition of target MRNA
2.PIWI (p-element induced wimpy Testis)- breaks Phosphodiester bond of mRNA.)RNAse H activity.
MiRNA:
The Double-stranded RNAs are naturally produced in eukaryotic cells during development, and they have a key role in regulating gene expression .
Richard's entangled aventures in wonderlandRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
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/
Slide 1: Title Slide
Extrachromosomal Inheritance
Slide 2: Introduction to Extrachromosomal Inheritance
Definition: Extrachromosomal inheritance refers to the transmission of genetic material that is not found within the nucleus.
Key Components: Involves genes located in mitochondria, chloroplasts, and plasmids.
Slide 3: Mitochondrial Inheritance
Mitochondria: Organelles responsible for energy production.
Mitochondrial DNA (mtDNA): Circular DNA molecule found in mitochondria.
Inheritance Pattern: Maternally inherited, meaning it is passed from mothers to all their offspring.
Diseases: Examples include Leber’s hereditary optic neuropathy (LHON) and mitochondrial myopathy.
Slide 4: Chloroplast Inheritance
Chloroplasts: Organelles responsible for photosynthesis in plants.
Chloroplast DNA (cpDNA): Circular DNA molecule found in chloroplasts.
Inheritance Pattern: Often maternally inherited in most plants, but can vary in some species.
Examples: Variegation in plants, where leaf color patterns are determined by chloroplast DNA.
Slide 5: Plasmid Inheritance
Plasmids: Small, circular DNA molecules found in bacteria and some eukaryotes.
Features: Can carry antibiotic resistance genes and can be transferred between cells through processes like conjugation.
Significance: Important in biotechnology for gene cloning and genetic engineering.
Slide 6: Mechanisms of Extrachromosomal Inheritance
Non-Mendelian Patterns: Do not follow Mendel’s laws of inheritance.
Cytoplasmic Segregation: During cell division, organelles like mitochondria and chloroplasts are randomly distributed to daughter cells.
Heteroplasmy: Presence of more than one type of organellar genome within a cell, leading to variation in expression.
Slide 7: Examples of Extrachromosomal Inheritance
Four O’clock Plant (Mirabilis jalapa): Shows variegated leaves due to different cpDNA in leaf cells.
Petite Mutants in Yeast: Result from mutations in mitochondrial DNA affecting respiration.
Slide 8: Importance of Extrachromosomal Inheritance
Evolution: Provides insight into the evolution of eukaryotic cells.
Medicine: Understanding mitochondrial inheritance helps in diagnosing and treating mitochondrial diseases.
Agriculture: Chloroplast inheritance can be used in plant breeding and genetic modification.
Slide 9: Recent Research and Advances
Gene Editing: Techniques like CRISPR-Cas9 are being used to edit mitochondrial and chloroplast DNA.
Therapies: Development of mitochondrial replacement therapy (MRT) for preventing mitochondrial diseases.
Slide 10: Conclusion
Summary: Extrachromosomal inheritance involves the transmission of genetic material outside the nucleus and plays a crucial role in genetics, medicine, and biotechnology.
Future Directions: Continued research and technological advancements hold promise for new treatments and applications.
Slide 11: Questions and Discussion
Invite Audience: Open the floor for any questions or further discussion on the topic.
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.
MAGIC population and its application in crop improvement
1. TOPIC- “MAGIC population its
development and application”
SUBMITTED TO :
Dr. Amar Sakure
Associate professor
Course Teacher (MBB501)
Department of plant biotechnology
Anand agricultural university
SUBMITTED BY :
Boddu Sangavi
Reg : 2010120093
M.sc agriculture 1st semester
Genetics and plant breeding
2. CONTENTS:
MAGIC –meaning and purpose
Advanced inter cross lines
Why magic?
Steps in development of magic lines
Genetic analysis of magic population
Applications Of Magic Lines In Breeding Programs
Future line of work
Advantages and limitations
Institutes Involved In Development Of Magic Populations
Case study
3. MAGIC POPULATION- A NEW RESOURCE FOR PLANT GENETICS
M-multi parent
A -advanced
G -generation
I-inter
C-cross
• It is a second generation mapping resource for discovery , characterization and
deployment of genes responsible for complex traits.
PURPOSE :
• Genetic dissection of complex traits
• It identifies genes that contribute to quantitative variation
• Most traits of biological and economic interest are quantitative in nature
• They display continuous variation (polygenic control)
• Identification of gene-trait associations for complex traits is difficult
4. MAGIC POPULATION
• Magic population is an extension of AILs
• It is a second generation mapping resource for discovery , characterization and
deployment of genes responsible for complex traits.
• Magic population is heterogenous stock
• Magic population is 1st introduced by Mott et.al 2000 in mice using 8 inbred
strains as an improvement over the AIC
• Term magic population is coined by Mackay and Powell 2008
• In plants magic population was 1st developed and described in Arabidopsis by
taking 19 founders Kover et al in 2009
• Intercrossed mapping population is created from multiple founder lines
• The intercrossed recombination and diversity of MAGIC gives greater precision
in QTL location and greater oppurtunity to detect more QTL
• Each generation reduces the extend of linkage disequilibrium thus allowing QTL
to be mapped more accurately
• Lines derived from early generations can be used for QTL detection and coarse
mapping
Cavanagh et al.,2008
6. ADVANCED INTERCROSS LINES (AIL)
• It is an extension of RILs
• Proposed by Darvasi and Soller in 1995
• Consists of repetitively intermated f2 population followed by selfing to derive AILs
• The additional rounds of intermating reduce the level of LD and increase the
precision of QTL location
• Higher accuracy than RILs
7. What is the difference between RIL and AIL ?
Cavanagh et al.,2008
8. Why MAGIC ?
• For both coarse and fine mapping
• Incorporation of multiple parents ensures the segregation of population for multiple
QTL for multiple traits
• Negligible impact from population structure
• Increased mapping resolution by taking the advantages of both historical and
synthetic recombination
• To overcome the demerits of linkage analysis and association mapping like
Lower power to detect QTL ,Sensitive to population structure
QTLs are located with very large confidence interval (10-30cM)
The narrow genetic base
DEVELOPMENT OF MAGIC LINES
1. Founder Selection
2. Mixing
3. Advanced Intercrossing
4. Inbreeding
9. FOUNDER SELECTION
• Based on genetic and phenotypic diversity
• Founder should be elite cultivars
• Founder material should be of more diverse origins like world wide germplasms
collections , distant relatives
• Founder parents generally will be 8 in number and each of them should be carefully
selected as a donor for at least one major trait in an improve background
Founder lines
World wide germplasm
collections
Huang et al.,2015
10. MIXING
• Multiple parents are intercrossed to form a broad genetic base
• Mixing of parents together in predefined patterns and intermated
• The inbred founders are paired off and inter mated , known as funnel
• The results of this stage is a set of lines whose genomes comprised contributions
from each of the founders
Funnel-1
Huang et al.,2015
11. ADVANCED INTERCROSSING
• Mixed lines from different funnels are randomly and sequentially intercrossed as in the
advanced intercross
• The main goal is to increase the number of recombination in the populations
• Selection is based on phenotype for further reducing frequency of the deleterious allele from
the donor
AIL-1 AIL-2
• Yamamoto et al 2014 concluded that at least 6 cycles of intercrossing is required for large
improvements in QTL mapping power
Funnels
Huang et al.,2015
12. INBREEDING
• Development of homozygous individuals
• RILs in plant can be created by single seed descent method
• The multiple generation of selfing will introduce additional
recombination but less than during the mixing and advanced
intercrossing stages
MAGIC effective population size :
1000 magic individuals are adequate to map a single additive locus that accounts for
5% of the phenotypic variation to within 0.96cM distance
- Valder etal.,2006
500 lines ,sufficient resolution may be obtained even in presence of high epistasis
13. GENETIC ANALYSIS OF MAGIC POPULATION
1.LINKAGE MAP CONSTRUCTION:
• The large number of polymorphic markers across all founders and accumulation of
recombination events through many generations of the magic pedigree can be used
to achieve dense and high resolution mapping of the genome
• The 1st linkage map from a magic population was constructed in wheat
( Huang et al.2012)
2.QTL MAPPING APPROACH
• The use of heterogenous stock improves the power to detect and localize QTL
• The large number of parental accession increases the allelic and phenotypic
diversity
• The large number of accumulated recombination events increase the mapping
accuracy of the detected QTL compared to an f2 cross
14. APPLICATIONS OF MAGIC LINES IN BREEDING PROGRAMS
• Magic population can be used directly as source materials for the extraction
and development of breeding lines and varieties
• Development of variety with several agronomically beneficial traits
• Can provide solutions to a range of production constraints particularly stress
tolerance
• An assessment and understanding of the potential of enhanced
recombination in generating novel diversity
FUTURE LINE OF WORK :
• Development of magic population in more important crops
• Detection of QTLs which is responsible for stress resistance , yield and other
important traits
• Development of varieties with novel combinations
15. ADVANTAGES OF MAGIC POPULATION:
• More targeted traits
• Increased precision and resolution with which the QTLs can be detected due to
increased level of recombination
• Shuffling of genes across different parents enabling novel rearrangements of alleles
• Greater genetic variability
• Chances to get best combination of desirable genes
• Phenotypic selection in advance generation reduce the frequency of deleterious or
undesirable alleles from donors
• Magic population will be a permanent mapping population for precise QTL mapping
LIMITATIONS :
• Intensive labour of crossing
• Large population size is required for recovery of recombinants with all the desirable
traits
• More time is required to develop the resource population
• Large scale phenotyping is required for a particular trait
• Requires more inputs
16. INSTITUTES INVOLVED IN DEVELOPMENT OF MAGIC POPULATIONS
CROP INSTITUTE
BREAD WHEAT NIAB
DURUM WHEAT University of Bologna ,ITALY
RICE IRRI
OATS IBERS
BARLEY SAC
SORGHUM ICRISAT
COWPEA IITA
18. The global population includes eight indica and
eight japonica founder lines, which carry QTLs conferring
tolerance of biotic and abiotic stresses. A, B, C, D, E, F, G
and H – represent the 8 indica parents; I, J, K, L, M, N, O
and P – represent the 8 japonica parents
Development of the indica
MAGIC population
19. Result of the study
Each population is comprised of 8 founder lines and were phenotyped for multiple traits :
Blast disease:
• Detected 3 contributing QTLs on chromosome 2,6 and 9
• Out of 8 founders ,6 were resistant to blast disease under natural conditions in the blast
nursery at IRRI
Bacterial blight :
• The subset of indica magic lines and the 8 founders were screened at maximum
tillering stage in the screen house at IRRI ,results indicate that majority of 200 lines
carried the Xa4 gene which is resistant for bacterial blight
Salt tolerance:
• Presence of good level of tolerance lines , presumably from the salt tolerant parents
IR4630-22-2-5-1-3 or IR45427-2B-2-2B-1-1
Submergence tolerance:
• Detected 54 associated markers out of which 49 were detected on chromosome 9 in the
region of sub1 locus
Grain quality:
• GWAS detected known major effect QTLs along with several potentially novel QTLs
for grain quality and grain shape