Association Mapping versus Genomic Selection Association Mapping • To discover genes and genetic variants that control a trait • Knowledge can be applied understand mechanism, genetic architecture, design pathways with diversity, ideas for transgenic improvement Genomic Selection • To identify germplasm with the best breeding values and performance • Can identify complementary varieties that should be crossed for future improvement. 1
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Association Mapping versus Genomic Selection Association Mapping To discover genes and genetic variants that control a trait Knowledge can be applied understand.
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Association Mappingversus Genomic Selection
Association Mapping• To discover genes and
genetic variants that control a trait
• Knowledge can be applied understand mechanism, genetic architecture, design pathways with diversity, ideas for transgenic improvement
Genomic Selection• To identify germplasm
with the best breeding values and performance
• Can identify complementary varieties that should be crossed for future improvement.
• Ridge regression is not affected by the number of QTL / the QTL effect size
• BayesB performs better with large marker-associated effects
• Co-linearity is more detrimental to BayesB• High marker density and training pop. size?
Yes: BayesB No: RR-BLUP
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VanRaden et al. 2009
• VanRaden, P.M. et al. 2009. Invited Review: Reliability of genomic predictions for North American Holstein bulls. J. Dairy Sci. 92:16-24.
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VanRaden et al. 2009
• Some traits have major genes, others do not
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VanRaden et al. 2009
• The larger the training population, the better. Where diminishing returns will begin is not in sight.
Predictor
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Take Homes
• Training population requirements very large• BayesB did not help• == no large marker-associated effects ==• Like the “Case of the missing heritability” in
human GWAS studies– Are many quantitative traits driven by very low
frequency variants?– RR would capture this case better than BayesB
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Empirical data on crops: TP size
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Empirical data on crops: Marker No.
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Empirical data on Humans: Marker No.
Yang et al. 2010. Nat. Genet. 10.1038/ng.608
Out of 295K SNP
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Long-term genomic selection
• Marker data from elite six-row barley program• 880 Markers• 100 hidden as additive-effect QTL• Evaluate 200 progeny, select 20• Phenotypic compared to genomic selection
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Breeding / model update cycles
Evaluation is possible every other season. Candidates from every other cycle can be evaluated. There is still a lag: Parents of C2 are selected based on evaluation of C0.
Season 1 Season 2 Season 3 Season 4 Season 5 Season 6
Phenotypic Selection
Cross &Inbreed
Evaluate& Select
Cross &Inbreed
Evaluate& Select
Cross &Inbreed
Evaluate& Select
Cross &Inbreed
Evaluate& Select
Cross, Inb.& Select
Cross, Inb.& Select
Cross, Inb.& Select
Cross, Inb.& Select
Evaluate EvaluateGenomic Selection
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Response in genotypic value
Phenotypic Breeding Cycle
Mea
n G
enot
ypic
Val
ue
Genomic; Small Training PopGenomic; Large Training Pop
Phenotypic Selection
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Accuracy
Phenotypic Breeding Cycle
Mea
n Re
alize
d Ac
cura
cy
Genomic; Small Training PopGenomic; Large Training Pop
Phenotypic Selection
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Genetic variance
Phenotypic Breeding Cycle
Mea
n G
enot
ypic
Sta
ndar
d D
evia
tion
Genomic; Small Training PopGenomic; Large Training Pop
Phenotypic Selection
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Lost favorable alleles
Phenotypic Breeding Cycle
Mea
n N
umbe
r Los
t Fav
orab
le A
lllel
es
Genomic; Small Training PopGenomic; Large Training Pop
Phenotypic Selection
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Goddard 2008; Hayes et al. 2009
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Response in genotypic value
Phenotypic Breeding Cycle
Mea
n G
enot
ypic
Val
ue
Genomic; Small Training PopGenomic; Large Training Pop
Phenotypic Selection
Phenotypic Breeding Cycle
Unweighted Weighted
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Genetic variance
Phenotypic Breeding Cycle
Mea
n G
enot
ypic
Sta
ndar
d D
evia
tion
Genomic; Small Training PopGenomic; Large Training Pop
Phenotypic Selection
Phenotypic Breeding Cycle
Unweighted Weighted
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Lost favorable alleles
Phenotypic Breeding Cycle
Mea
n N
umbe
r Los
t Fav
orab
le A
llele
s
Genomic; Small Training PopGenomic; Large Training Pop
Phenotypic Selection
Phenotypic Breeding Cycle
Unweighted Weighted
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Long term genomic selection
• The acceleration of the breeding cycle is key• Some favorable alleles will be lost– Likely those not in LD with any marker
• Managing diversity / favorable alleles appears a good idea
• This can be done using the same data as used for genomic prediction
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Introgressing diversity
• GS relies on marker–QTL allele association• An “exotic” line comes from a sub-population
divergent from the breeding population• After sub-populations separate– Drift moves allele frequencies independently– Drift & recombination shift associations
independently• Will the GS prediction model identify valuable
segments from the exotic?
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Three approaches
• Create a bi-parental family with the exotic (Bernardo 2009)– Develop a mini-training population for that family– Improve the family – Bring it into the main breeding population
• Develop a separate training population for the exotic sub-population (Ødegård et al. 2009)
• Develop a single multi-subpopulation (species-wide?) training population (Goddard 2006)
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Need higher marker density
Ancestral LD
• Tightly–linked: ancestral LD• Loosely–linked: sub-population specific LD
sub-population specific LD
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0 cM recombination distance 5 cM recombination distance
Genetic Distance
Corr
elati
on o
f rConsistency of association across barley
subpopulations
0.8
0.0
0.2
0.4
0.6
1.0
0.0 0.5
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Example: Dairy cattle breeds
TP = Hols. TP = Jers. Hols. + Jers.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
VP = HolsteinVP = Jersey
Pred
ictio
n A
ccur
acy
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G1 G2 G3
N=136 N=149 N=161
Oat sub-populations (UOPN)
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Combined sub-population TP(β-Glucan)
G1 G2 and G3
0.11
TPVP
G3G1 and G2
0.50
G1 G3G2
0.39
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Introgressing diversity using GS
• Need higher marker density• Analysis of consistency of r may indicate
whether current density is sufficient– Not sure we have it for barley
• If you have the density, a multi-subpopulation training population seems like a good idea– Focuses the model on tighter ancestral LD rather