sfa3 status on gensap for assessment and optimisation · sfa3 status on gensap for assessment and...
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CENTER FOR QUANTITATIVE
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CENTER FOR QUANTITATIVE
GENETICS AND GENOMICS Q AARHUS
UNIVERSITY
SFA3 Status on GenSAP for
assessment and optimisation
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Huiming Liu, Tobias Okeno, Kristian Meier, Mark Henryon, Jørn Thomasen, Hadi Esfandyari, Christian Sørensen
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What GenSAP can do for us
• Time for development of tools
• Time for uncovering and understanding mechanism underlying consequences of selection
• Both of these support the industry projects
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Diminishing marginal returns from
genomic selection as more selection
candidates are phenotyped
T. O. Okeno, M. Henryon and A. C. Sørensen
05-01-2015 3
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Hypotheses There is diminishing marginal return from genomic selection as
more candidates are phenotyped
Phenotyping candidates based on a priori information is beneficial
There is a best distribution of phenotypes on the sexes with respect to genetic gain and inbreeding rate
05-01-2015 4
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05-01-2015 5
Genetic gain
0,85
0,87
0,89
0,91
0,93
0,95
0,97
0,99
1,01
1,03
1,05
20 40 60 80 100Phenotyping proportion (%)
ΔG, genotypic standard deviation
Estimated breeding value
Random selection
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6 05-01-2015
Genetic gain
0,85
0,87
0,89
0,91
0,93
0,95
0,97
0,99
1,01
1,03
1,05
20 40 60 80 100
Phenotyping proportion (%)
ΔG, genotypic standard deviation
Estimated breeding value
Random selection
Sex differentiation
No sex differentiation
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05-01-2015 7
0,84
0,86
0,88
0,90
0,92
0,94
0,96
0,98
1,00
100:0 75:25 50:50 25:75 0:100
Male:female phenotyping ratio
ΔG, phenotypic standard deviation
50%
40%
30%
20%
Genetic gain for Male:Female ratio
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Diminishing return as more candidates are phenotyped
Use of a priori information to select animals to phenotype is beneficial
Mainly phenotyping sex with high selection intensity is beneficial at low
phenotyping proportions
Less intensively selected sex should also be considered at high
phenotyping proportions
05-01-2015 8
Conclusions
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Specific challenges adressed in GenSAP SFA3
• Extended tool box
• Non-additive genetic variation (Hadi)
• Selection across multiple environments
• Use of genomic information for handling inbreeding (Huiming)
• Proof-of-concept (Kristian)
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Maximizing crossbred performance through purebred genomic selection
Hadi Esfandyari
Christian Sørensen
Piter Bijma
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Aims
• To investigate the benefits of GS of purebreds for CP based on purebred information
• To compare separate pure line reference populations with a joint reference population
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Predicting crossbred performance
• Predicting additive and dominance effects of each marker
• Purebred breeding value of an animal depends on allele frequency in its own breed
• Crossbred breeding value of an animal depends on allele frequency in the other breed
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Performance of crossbreds
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Conclusions
• Selecting the pure lines for crossbred performance increases phenotypic performance of crossbreds
• Selecting for crossbred performance increases the expression of heterosis
• If the breeds are closely related a joint reference population is beneficial
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GENETICS AND GENOMICS Q
Huiming Liu
Maintenance of genetic diversity in genomic selection
AARHUS
UNIVERSITY
15
Supervisors: Elise Norberg Peer Berg Christian Sørensen Theo Meuwissen
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How to maintain the genetic diversity?
16
0.0 0.2 0.4 0.6 0.8 1.0
24
68
10
Low allele freq. ~ High allele freq.
We
igh
t (W
)
• Putting more weight on the rare favourable alleles (WGEBV)
0.00 0.01 0.02 0.03 0.04 0.05 0.06
0.1
10.1
20.1
30.1
40.1
50.1
60.1
7
Fped
genetic g
ain
-1
-5
-10
-25
-50
Ge
net
ic g
ain
• Optimum contribution selection (OCS)
Rate of inbreeding
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Selection design
17
rule
criterion
Truncation
selection
Optimum Contribution
Selection
A G
GEBV
GEBV
OCSA
OCSG
WGEBV
WGEBV
WOCSA
WOCSG
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Genetic gain
18
rule
criterion
Truncation
selection
Optimum Contribution
Selection
A G
GEBV
GEBV
0.435
OCSA
0.542
OCSG
0.587
WGEBV
WGEBV
0.495
WOCSA
0.590
WOCSG
0.604
13
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Conclusion
19
The strategy that combines WAF and OCS is very promising
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Genomic selection in mink (Neovison vison): A simulation study
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Kristian Meier Anders Christian Sørensen
Janne P. Thirstrup Mogens Sandø Lund
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Breeding plan without genomic information
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240 males 1200 females
6000 offspring
40% reused second year
Breeding value estimation
Selection
Weight, feed efficiency, pelt quality
(live animals)
Skin quality
Litter size, barren females
Pelting
Proces starts over
Challenge: No registration on selection candidates for: - Litter size, - Barren females - Skin quality
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• Do we need to make DNA analysis on all selection candidates?
Simulation design
• How high accuracy do we get with genomic selection in mink? – We don´t know?
– We simulate scenarios with low, medium and high accuracy
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Number of DNA analysis
Selection candidates Number
All 6000
Best 10% males 300
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Genomic selection
Total economic gain Dkr pr.
female pr. year Accuracy DNA analysis
High All 97
10 % best males 71
Low All 60
10 % best males 57
Breeding plan without genomic information 54
Monetary genetic gain
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Genetic gain in traits
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Genomic selection
Contribution (%) from five traits to the total economic gain
Litter size
Weight
Barren females
Pelt quality
Feed efficiency
Accuracy DNA analysis
High All 42 19 19 0 20
10 % best males
26 37 12 -5 30
Low
All 12 51 7 -8 39
10 % best males 10 54 4 -9 41
Breeding plan without genomic information 4 59 2 -10 45
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Conclusion
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• Genomic selection increases total economic gain
• Increased genetic gain for litter size, barren females and pelt quality
• Even at low accuracy and few DNA analyses genetic gain is increased
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Future work
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• Evaluate different economic weights
• Cost (DNA) benefit (economic gain) analysis
• Future infrastructure supporting genomic selection
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Wrap-up
• So far, we have just scratched the surface
• Recruitment allows us to speed up from January – PhD at NMBU started September 1st 2014
– Post Doc jointly with SFA1 starting January 1st 2015
– Post Doc assisting with ADAM programming starting January 1st 2015
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Specific challenges adressed in GenSAP SFA3
• Extended tool box (Huiming, Jørn, Mark, Beatriz)
• Non-additive genetic variation (Hadi)
• Selection across multiple environments
• Use of genomic information for handling inbreeding (Huiming, Gebreyohans, Tobias)
• Proof-of-concept (Kristian)