Transcript Do you believe in MAGIC? Proof of concept for a new

Believing in MAGIC:
Validation of a novel experimental breeding design
Emma Huang, Ph.D.
Biometrics on the Lake
December 2, 2009
Multiparent
Advanced
Generation
Inter
Cross
Experimental crosses
2 parents
• BC
• F2
• ...
A
B
B
AB
AB
AB
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4 Parents
A
C
B
AB
CD
ABCD
6 generations of selfing
Inbred individuals
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D
Final Result
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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
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RIL final lines
Size
Diversity
Vital resource for linkage mapping
• No physical map/sequence for wheat (yet)
• Previous maps developed for specific population
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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
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But... nontrivial
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Complex inheritance from founders
Limited genotyping
Population size/# markers – computational burden
Marker issues – dominant markers, polyploidy, etc.
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Theory vs. Reality
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Linkage Map Construction
Basic Strategy
1.
2.
3.
4.
Filter and preprocess marker data
Estimate pairwise recombination
Group and order loci
Refinement
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Step 2: Estimating Recombination Distance
• In standard designs, counting numbers of different genotypes
• This doesn’t work for 4/8 way crosses
• Many generations of recombination
• No intermediate genotyping
• Ambiguous inheritance of alleles
Instead maximize the likelihood:
Pr(Y; r )   Pr(Y | G) Pr(G; r )
G
N
N
i 1
i 1
Pr(Y | G)  Pr(Y j | G  j ) Pr(Yk | G k )   Pr(yij | g ij ) Pr(yik | g ik )
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Step 4: Refinement
A
X X XX
X X X X XX X X X
C
X X X X
B
• 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
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MAGIC bag of tricks
R package mpMap
• Simulate data, filter/process, generate linkage map, visual
quality checks
Simulations
Input Variables:
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Mixing structure
Inbreeding structure
Sample size
True linkage map
Marker quality
Real Data
• 4-parent cross
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Output Questions:
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Precision of estimation
Reliability of grouping
Accuracy of ordering
Usefulness of 3-pt vs. 2-pt
Resolution of data
In a perfect world
“Nice” data –
• Fully informative markers
• No missing data
• No genotyping errors
Chr 6
Chr 5
Chr 4
Chr 3
Chr 2
Chr 1
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Recombination
fractions below
diagonal; scaled
LOD scores
above
Something closer to reality
“Typical” data –
• Biallelic markers
• 10% missing data
• 10% genotyping errors
> datbad <- mp.sim(map, simped, seed=1, 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 >5% missing data.
99 markers had >10% missing data.
0 markers had >20% missing data.
------------------------------------------------------49 markers had <1e-5 p-value for segregation distortion
2 markers had <1e-10 p-value for segregation distortion
0 markers had <1e-15 p-value for segregation distortion
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4-parent MAGIC
• DArT & SNP markers
• Constructed map
• 871 progeny
• 1148 markers
• 20/21 chromosomes
• 2010: 5000 lines from 8-way cross
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Chromosome 6B
Genetic Map
Heat Map
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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
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Emma Huang
Research Scientist
Phone: +61 7 3214 2953
Email: [email protected]
Thanks to:
Andrew George
Colin Cavanagh
Matthew Morell
Thank you
Contact Us
Phone: 1300 363 400 or +61 3 9545 2176
Email: [email protected] Web: www.csiro.au
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