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MAGIC, Multiparent advanced generation intercross a
new genetic resource for multiple trait improvement and

QTL discovery in crops
   G.Kalidasan
    Quantitative traits
     Phenotype expression
     Natural variation

    Experimental System

     Selection or Natural Population

     Experimental Population

    Multiparent Advanced Generation Intercross Population

    Case Studies
Selection and Natural populations
Selection experiments


Marker allele frequency –
 Unrelated individuals


Many generations


Difference in phenotype


LD between QTL and marker


LD around a QTL
Domestication




Comparison of allele frequency

Without phenotypic information

Loci subject to selection
   Exploits LD in diverse population
   Human
   Crops
   Maize


Advantage
   Cheaper and high density markers


Disadvantage
   Spurious associations
   Greater precision but low power
Mutant population
   Spontaneous mutation
   Induced mutation
   Mutagenesis
   Large resources
   Poly ploidy
TILLING
   Phenotyping screen
   Knowledge on genes controlling trait
F2 and backcross (BC) populations
   Additive effects
   Few meioses


Recombinant inbred lines
   RILs are advanced homozygous
    lines
   Increased recombination events
    and improved map resolution
   Epistatic interactions
Near isogenic lines(NIL)

   Target trait is required for the

      generation of NILs.

   High-resolution mapping




Double haploids

   100% purity and genetic
    uniformity.

    Genetic studies
Randomly and sequentially
  intercrossed population.


Phenotypic selection to further
 reduce the frequency of deleterious
 alleles from the donor.

 Detect QTLs with epistatic effects

 Useful meiotic recombination
Linkage map
   DNA Markers
   Position and relative genetic distance
   For identifying chromosomal regions that contain genes
    controlling simple or complex traits using QTL analysis

   QTL mapping

   Advantage

     High detection power
     Few markers are required


   Disadvantage

     Large confidence interval of upto 5 to 30cM
     Limited resolution
     Only two alleles tested
Multiparent advanced generation intercross
   Animals. (Mott et al., 2000) and (Yalchin et al., 2005)

   Fine-mapping of multiple QTLs for multiple traits in the same
    population.

   Advanced intercrossed lines (AILs)

    Each generation reduces the extent of linkage disequilibrium
    (LD), thus allowing QTL to be mapped more accurately.

   Lines derived from early generations can be used for QTL
    detection and coarse mapping

   While those derived from later generations will only detect
    marker-trait associations if markers are located very close to
    the QTL.
   Extended to plants
        (Cavanagh et al., 2008)

   Diverse founder lines

   n/2 generations

   RILs

   Increased intercrossing
    cycles
Short generation period- Arabidopsis

Eight founder lines

G1

G2

G3

G4

G10-12

SNP genotyping platforms

SSR Markers
   Statistics tools

   Linear mixed effect model and Hierarchical Bayes QTL
    mapping - study the interrelationship between individuals MLs
    and founder lines and increases the precision to detect QTL

   HAPPY- a software package for Multipoint QTL Mapping in
    Genetically Heterogeneous Animals

   R/mp Map- A computational platform for the genetic analysis of
    multi-parent recombinant inbred lines
Advantages

   Shuffling the genes across different parents enable accurately
    ordering the genes

   Increased recombination - novel rearrangements of alleles and
    greater genetic diversity.

   Best combinations of genes for important traits development

   1000 Magic individuals

   Seeds retained - fine mapping

   Epistatic and G X E interactions

   Facilitate the discovery, identification and manipulation of new
    forms of allelic variability
Disadvantages

   Extensive segregation

   More time

   Large scale phenotyping
(Rakshit et al., 2012)
   In Arabidopsis fine mapping of QTL for germination and bolting
    time                                       (Kover et al. 2009)

   Studies in flowering time candidate genes
                        (Ehrenreich et al. 2009)

   Developed computational platform R/mp Map for Genetic
    analysis                             (Huang and George,
    2011)
   19 ‘‘founder’’ accessions
   Wide geographical distribution
   Staggered planting scheme
   Replanted families – randomly
    assigned crosses
   342 F4 outcrossed families

   Each F4 family derived up to 3 MLs
    followed by selfing 6 generations
   527 lines taken out of 1026
   Developmental quantitative traits

   Measured the heritability (h2)

    h2 L is the proportion of variation that is due to genetic
    differences between lines, using the phenotypic average of the
    replicates within each line

   h2 P is an estimate of the genetic variance if only one replicate
    per line were phenotyped

   h2L ≥ h2P

   h2L increases with the number of replicates

   Mean of each line is used for QTL mapping
Phenotypic variance
Diallelic population - 0.5
 Magic – 0.052
Average minor allele frequency in
 founder lines is 0.22
70% of SNP shared between any pair
 of founders
Increasing replication within line
 reduces non- genetic variance
Improves power of QTL
   A hidden Markov model (HMM) is used to make a multipoint
    probabilistic reconstruction of the genome of each ML as a mosaic of
    the founder haplotypes.

   Diallelic SNPs cannot distinguish between all founders so information
    from neighboring SNPs is used to compute the posterior probability
    Pis(L) is that at a given locus L, the ML i is descended from founder s.

   Locus is defined to be the interval between two adjacent genotyped
    SNPs, labeled by the name of the left-hand SNP.
   Used fixed-effects QTL models but to accommodate population
    structure, in different ways used multiple-QTL modeling or random
    effects to explain the correlations introduced in population structure

   Checked with hierarchical Bayesian random effects model

    All approaches model the mosaic structure of the MAGIC genomes
    as described in and implemented in the R package HAPPY

   Detected two QTLs on chromosomes 3 and 4 for the number of days
    to germination and bolting time.
   Constructed a linkage map from a four parent MAGIC population and
    validate it against a comprehensive DArT consensus map drawing
    together maps from over 100 biparental populations

   Incorporated the alien introgressions in to the linkage map

   Level of LD across the genome and compare it with previous
    estimates for LD from previous studies

   The power and precision of MAGIC for QTL mapping for plant height ,
    an important trait for yield potential
 Selected four elite wheat
  cultivar , A- Yitpi , B- Baxter,
  C- Chara, D- Westonia

Genetic diversity based on
 genetic survey of international
 wheat samples

Diverse geographical distribution

Phenotypic diversity for a range
 of traits
Genotyping

   Used 1285 DArT markers, 57 SSR markers and 1536 SNPs

   384 SNPs observed to be polymorphic among the parental lines
     were selected for genome wide coverage

Phenotyping

   1100 RILs – Plant height was recorded
   R package mpMap

   Filtered with monomorphic markers

   Estimated the recombination fraction all pairs of loci with function
    ‘mpestrf’

   Grouped the markers based on estimated recombination fractions
    and LOD scores with the function ‘mpgroup’

   For map resolution computed recombination events for all lines
    using the function ‘mpprob’

   Which calculates the multipoint probablity at each locus that the
    observed genotype is inherited from each of the four founder
   Both internal and external comparisons was done

   Examined a series of diagnostic plots to propose changes to
    ordering which were then tested through ‘ compare orders’ function
    in R/mpMap

   Used heatmaps based on both recombination fractions and LD
    using R/Ldheatmap

   The tool provided visualization of the relationships between all pairs
    of markers
External comparison

   Diagnostic checks are compared it to an external DArT consensus
    wheat map

   Each individual consensus map was based on genotyping involving
    the analyis of between 206 and 1525markers, with an average
    density of 582 markers
   Test the introgression in magic population

   Sr36 is an introgression from Triticum timopheevii for stem rust
    resistance – carried in variety Baxter on chromosome 2B cause
    segregation distortion

   Aimed to identify markers associated with it and identify lines
    containing it

   Computed the degree of segregation distortion for which i) the
    Baxter allele differed from all other founder alleles

   ii) mutual recombination of < 0.05 was observed were tagged as
    potential markers in the introgression

   Estimated the probablity of a line having inherited an allele from the
    founder Baxter for the identified markers(‘mpprob’)
Linkage disequilbrium

   Multipoint probablities were computed using the function ‘mpprob’ in
    R/mpMap and LD was computed using the function ‘mpcalcld’

QTL Mapping

   For all analyses used the ‘mplMmm’ function in the R package
    mpMap, which performs the interval mapping in the context of a
    linear mixed model

   Three QTL for plant height were detected near known genes on
    chromosomes 2D, 4B, 4D.
   (MAGIC) populations combine the advantages of linkage analysis and
    association studies.

   The increased recombination in MAGIC populations leads to novel
    rearrangements of alleles and greater genetic diversity

   Can facilitate the discovery, identification and manipulation of new
    forms of allelic variability

   They require longer time and more resource to be generated and they
    are likely to show extensive segregation for developmental traits.

   MAGIC populations are likely to bring paradigm shift towards QTL
    analysis in plant species
   The experimental method was underway since it has to be studied
    in many crops

   The tools used for QTL mapping are very complex, so simplified
    models has to be developed in near future for understanding.

   In near future the method will bring success in finding our economic
    interest of traits in plants.

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MAGIC :Multiparent advanced generation intercross and QTL discovery

  • 1. MAGIC, Multiparent advanced generation intercross a new genetic resource for multiple trait improvement and QTL discovery in crops G.Kalidasan
  • 2. Quantitative traits  Phenotype expression  Natural variation  Experimental System  Selection or Natural Population  Experimental Population  Multiparent Advanced Generation Intercross Population  Case Studies
  • 3. Selection and Natural populations Selection experiments Marker allele frequency – Unrelated individuals Many generations Difference in phenotype LD between QTL and marker LD around a QTL
  • 4. Domestication Comparison of allele frequency Without phenotypic information Loci subject to selection
  • 5. Exploits LD in diverse population  Human  Crops  Maize Advantage  Cheaper and high density markers Disadvantage  Spurious associations  Greater precision but low power
  • 6. Mutant population  Spontaneous mutation  Induced mutation  Mutagenesis  Large resources  Poly ploidy TILLING  Phenotyping screen  Knowledge on genes controlling trait
  • 7. F2 and backcross (BC) populations  Additive effects  Few meioses Recombinant inbred lines  RILs are advanced homozygous lines  Increased recombination events and improved map resolution  Epistatic interactions
  • 8. Near isogenic lines(NIL) Target trait is required for the generation of NILs. High-resolution mapping Double haploids 100% purity and genetic uniformity.  Genetic studies
  • 9. Randomly and sequentially intercrossed population. Phenotypic selection to further reduce the frequency of deleterious alleles from the donor.  Detect QTLs with epistatic effects  Useful meiotic recombination
  • 10. Linkage map  DNA Markers  Position and relative genetic distance  For identifying chromosomal regions that contain genes controlling simple or complex traits using QTL analysis  QTL mapping  Advantage  High detection power  Few markers are required   Disadvantage  Large confidence interval of upto 5 to 30cM  Limited resolution  Only two alleles tested
  • 11. Multiparent advanced generation intercross  Animals. (Mott et al., 2000) and (Yalchin et al., 2005)  Fine-mapping of multiple QTLs for multiple traits in the same population.  Advanced intercrossed lines (AILs)  Each generation reduces the extent of linkage disequilibrium (LD), thus allowing QTL to be mapped more accurately.  Lines derived from early generations can be used for QTL detection and coarse mapping  While those derived from later generations will only detect marker-trait associations if markers are located very close to the QTL.
  • 12. Extended to plants (Cavanagh et al., 2008)  Diverse founder lines  n/2 generations  RILs  Increased intercrossing cycles
  • 13. Short generation period- Arabidopsis Eight founder lines G1 G2 G3 G4 G10-12 SNP genotyping platforms SSR Markers
  • 14. Statistics tools  Linear mixed effect model and Hierarchical Bayes QTL mapping - study the interrelationship between individuals MLs and founder lines and increases the precision to detect QTL  HAPPY- a software package for Multipoint QTL Mapping in Genetically Heterogeneous Animals  R/mp Map- A computational platform for the genetic analysis of multi-parent recombinant inbred lines
  • 15. Advantages  Shuffling the genes across different parents enable accurately ordering the genes  Increased recombination - novel rearrangements of alleles and greater genetic diversity.  Best combinations of genes for important traits development  1000 Magic individuals  Seeds retained - fine mapping  Epistatic and G X E interactions  Facilitate the discovery, identification and manipulation of new forms of allelic variability
  • 16. Disadvantages  Extensive segregation  More time  Large scale phenotyping
  • 18. In Arabidopsis fine mapping of QTL for germination and bolting time (Kover et al. 2009)  Studies in flowering time candidate genes (Ehrenreich et al. 2009)  Developed computational platform R/mp Map for Genetic analysis (Huang and George, 2011)
  • 19.
  • 20. 19 ‘‘founder’’ accessions  Wide geographical distribution  Staggered planting scheme  Replanted families – randomly assigned crosses  342 F4 outcrossed families  Each F4 family derived up to 3 MLs followed by selfing 6 generations  527 lines taken out of 1026
  • 21.
  • 22. Developmental quantitative traits  Measured the heritability (h2)  h2 L is the proportion of variation that is due to genetic differences between lines, using the phenotypic average of the replicates within each line  h2 P is an estimate of the genetic variance if only one replicate per line were phenotyped  h2L ≥ h2P  h2L increases with the number of replicates  Mean of each line is used for QTL mapping
  • 23. Phenotypic variance Diallelic population - 0.5  Magic – 0.052 Average minor allele frequency in founder lines is 0.22 70% of SNP shared between any pair of founders Increasing replication within line reduces non- genetic variance Improves power of QTL
  • 24. A hidden Markov model (HMM) is used to make a multipoint probabilistic reconstruction of the genome of each ML as a mosaic of the founder haplotypes.  Diallelic SNPs cannot distinguish between all founders so information from neighboring SNPs is used to compute the posterior probability Pis(L) is that at a given locus L, the ML i is descended from founder s.  Locus is defined to be the interval between two adjacent genotyped SNPs, labeled by the name of the left-hand SNP.
  • 25. Used fixed-effects QTL models but to accommodate population structure, in different ways used multiple-QTL modeling or random effects to explain the correlations introduced in population structure  Checked with hierarchical Bayesian random effects model  All approaches model the mosaic structure of the MAGIC genomes as described in and implemented in the R package HAPPY  Detected two QTLs on chromosomes 3 and 4 for the number of days to germination and bolting time.
  • 26.
  • 27. Constructed a linkage map from a four parent MAGIC population and validate it against a comprehensive DArT consensus map drawing together maps from over 100 biparental populations  Incorporated the alien introgressions in to the linkage map  Level of LD across the genome and compare it with previous estimates for LD from previous studies  The power and precision of MAGIC for QTL mapping for plant height , an important trait for yield potential
  • 28.  Selected four elite wheat cultivar , A- Yitpi , B- Baxter, C- Chara, D- Westonia Genetic diversity based on genetic survey of international wheat samples Diverse geographical distribution Phenotypic diversity for a range of traits
  • 29. Genotyping  Used 1285 DArT markers, 57 SSR markers and 1536 SNPs  384 SNPs observed to be polymorphic among the parental lines were selected for genome wide coverage Phenotyping  1100 RILs – Plant height was recorded
  • 30. R package mpMap  Filtered with monomorphic markers  Estimated the recombination fraction all pairs of loci with function ‘mpestrf’  Grouped the markers based on estimated recombination fractions and LOD scores with the function ‘mpgroup’  For map resolution computed recombination events for all lines using the function ‘mpprob’  Which calculates the multipoint probablity at each locus that the observed genotype is inherited from each of the four founder
  • 31. Both internal and external comparisons was done  Examined a series of diagnostic plots to propose changes to ordering which were then tested through ‘ compare orders’ function in R/mpMap  Used heatmaps based on both recombination fractions and LD using R/Ldheatmap  The tool provided visualization of the relationships between all pairs of markers
  • 32. External comparison  Diagnostic checks are compared it to an external DArT consensus wheat map  Each individual consensus map was based on genotyping involving the analyis of between 206 and 1525markers, with an average density of 582 markers
  • 33. Test the introgression in magic population  Sr36 is an introgression from Triticum timopheevii for stem rust resistance – carried in variety Baxter on chromosome 2B cause segregation distortion  Aimed to identify markers associated with it and identify lines containing it  Computed the degree of segregation distortion for which i) the Baxter allele differed from all other founder alleles  ii) mutual recombination of < 0.05 was observed were tagged as potential markers in the introgression  Estimated the probablity of a line having inherited an allele from the founder Baxter for the identified markers(‘mpprob’)
  • 34. Linkage disequilbrium  Multipoint probablities were computed using the function ‘mpprob’ in R/mpMap and LD was computed using the function ‘mpcalcld’ QTL Mapping  For all analyses used the ‘mplMmm’ function in the R package mpMap, which performs the interval mapping in the context of a linear mixed model  Three QTL for plant height were detected near known genes on chromosomes 2D, 4B, 4D.
  • 35. (MAGIC) populations combine the advantages of linkage analysis and association studies.  The increased recombination in MAGIC populations leads to novel rearrangements of alleles and greater genetic diversity  Can facilitate the discovery, identification and manipulation of new forms of allelic variability  They require longer time and more resource to be generated and they are likely to show extensive segregation for developmental traits.  MAGIC populations are likely to bring paradigm shift towards QTL analysis in plant species
  • 36. The experimental method was underway since it has to be studied in many crops  The tools used for QTL mapping are very complex, so simplified models has to be developed in near future for understanding.  In near future the method will bring success in finding our economic interest of traits in plants.

Notes de l'éditeur

  1. By waiting until an advanced generation, one could allow for phenotypic selection to further reduce the frequency of deleterious or undesirable alleles from the donor.