sfa3 status on gensap for assessment and optimisation · sfa3 status on gensap for assessment and...

CENTER FOR QUANTITATIVE

GENETICS AND GENOMICS Q AARHUS

UNIVERSITY

SFA3 Status on GenSAP for

assessment and optimisation

UNIVERSITY

Huiming Liu, Tobias Okeno, Kristian Meier, Mark Henryon, Jørn Thomasen, Hadi Esfandyari, Christian Sørensen

UNIVERSITY

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

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

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

100:0 75:25 50:50 25:75 0:100

Male:female phenotyping ratio

ΔG, phenotypic standard deviation

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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• 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

Supervisors: Elise Norberg Peer Berg Christian Sørensen Theo Meuwissen

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How to maintain the genetic diversity?

0.0 0.2 0.4 0.6 0.8 1.0

Low allele freq. ~ High allele freq.

• Putting more weight on the rare favourable alleles (WGEBV)

0.00 0.01 0.02 0.03 0.04 0.05 0.06

genetic g

• Optimum contribution selection (OCS)

Rate of inbreeding

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Selection design

criterion

Truncation

selection

Optimum Contribution

Selection

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Genetic gain

criterion

Truncation

selection

Optimum Contribution

Selection

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Conclusion

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

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)

sfa3 status on gensap for assessment and optimisation · sfa3 status on gensap for assessment and...

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