Believing in MAGIC: Validation of a novel experimental breeding design - PowerPoint PPT Presentation

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Believing in MAGIC: Validation of a novel experimental breeding design

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Title: Do you believe in MAGIC? Proof of concept for a new mapping population Author: Huang, Emma (CMIS, St. Lucia) Last modified by: Huang, Emma (CMIS, St. Lucia) – PowerPoint PPT presentation

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Title: Believing in MAGIC: Validation of a novel experimental breeding design


1
Believing in MAGIC Validation of a novel
experimental breeding design
  • Emma Huang, Ph.D.
  • Biometrics on the Lake
  • December 2, 2009

2
Multiparent Advanced Generation Inter Cross
3
Experimental crosses
  • 2 parents
  • BC
  • F2
  • ...

A
B
B
AB
AB
AB
4
4 Parents
A
C
B
D
AB
CD
ABCD
6 generations of selfing
Inbred individuals
5
Final Result
6
In theory...
  • No genotyping for users after setup is complete
  • Effective design for G x E
  • Resource for large community - many traits
  • Large population of RILs
  • Large genetic (allelic) and phenotypic diversity
  • Ability to map epistatic interactions
  • High recombination ? high resolution

RIL final lines
Size
Diversity
7
Vital resource for linkage mapping
  • No physical map/sequence for wheat (yet)
  • Previous maps developed for specific population
  • Limited polymorphisms
  • Would have to join maps across populations
  • Possibly inconsistent estimates across maps
  • Many markers have not been mapped
  • MAGIC map is potentially
  • More complete due to greater genetic diversity
  • More accurate due to larger population size
  • More precise due to many generations of
    recombination

8
But... nontrivial
  • Complex inheritance from founders
  • Limited genotyping
  • Population size/ markers computational burden
  • Marker issues dominant markers, polyploidy,
    etc.

9
Theory vs. Reality
10
Linkage Map Construction
  • Basic Strategy
  • Filter and preprocess marker data
  • Estimate pairwise recombination
  • Group and order loci
  • Refinement

11
Step 2 Estimating Recombination Distance
  • In standard designs, counting numbers of
    different genotypes
  • This doesnt work for 4/8 way crosses
  • Many generations of recombination
  • No intermediate genotyping
  • Ambiguous inheritance of alleles
  • Instead maximize the likelihood

12
Step 4 Refinement
  • Start with framework map
  • Position markers relative to fixed locations
  • Maximize likelihood over grid of positions
  • Compared to initial ordering
  • Iterative and time-consuming
  • Less sensitive to missing values
  • Additional information about marker relationships

13
MAGIC bag of tricks
  • R package mpMap
  • Simulate data, filter/process, generate linkage
    map, visual quality checks

14
In a perfect world
  • Nice data
  • Fully informative markers
  • No missing data
  • No genotyping errors

Chr 6
Recombination fractions below diagonal scaled
LOD scores above
Chr 5
Chr 4
Chr 3
Chr 2
Chr 1
15
Something closer to reality
  • Typical data
  • Biallelic markers
  • 10 missing data
  • 10 genotyping errors
  • gt datbad lt- mp.sim(map, simped, seed1,
    error.prob.1, missing.prob.1)
  • --------------------------------------------------
    -----
  • Summary of mpcross object
  • --------------------------------------------------
    -----
  • 0 markers were removed with missing values in
    founders
  • 0 markers were removed with non-polymorphic
    founder genotypes
  • --------------------------------------------------
    -----
  • 195 markers were biallelic.
  • 0 markers were multiallelic.
  • --------------------------------------------------
    -----
  • 195 markers had gt5 missing data.
  • 99 markers had gt10 missing data.
  • 0 markers had gt20 missing data.
  • --------------------------------------------------
    -----
  • 49 markers had lt1e-5 p-value for segregation
    distortion
  • 2 markers had lt1e-10 p-value for segregation
    distortion
  • 0 markers had lt1e-15 p-value for segregation
    distortion

16
4-parent MAGIC
  • DArT SNP markers
  • Constructed map
  • 871 progeny
  • 1148 markers
  • 20/21 chromosomes
  • 2010 5000 lines from 8-way cross

17
Chromosome 6B
Genetic Map
Heat Map
18
Looking to the future
  • Improve the current map
  • Starting from the least informative set of
    markers
  • Further genotyping to fill in gaps
  • QTL Mapping
  • Testing different approaches
  • Field trials
  • Association Mapping
  • Using constructed map for complex analysis

19
CSIRO Mathematical and Information Sciences Emma
Huang Research Scientist Phone 61 7 3214
2953 Email Emma.Huang_at_csiro.au
Thanks to Andrew George Colin Cavanagh Matthew
Morell
Thank you
Contact UsPhone 1300 363 400 or 61 3 9545
2176Email Enquiries_at_csiro.au Web www.csiro.au
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