The document discusses the advantages and challenges of using multiparent advanced generation intercross (MAGIC) populations for genetic research in crops, particularly in quantitative trait loci (QTL) mapping. MAGIC populations enhance genetic diversity and facilitate more accurate QTL detection due to increased recombination, though they require significant resources and time. The ongoing research aims to improve understanding and application of these methods for crop trait improvement.

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

17. (Rakshit et al., 2012)

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)

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

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.

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.