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Extension of Bayesian procedures to integrate and to blend multiple external information into genetic evaluations J. Vandenplas 1,2, N. Gengler 1 1 University.

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Presentation on theme: "Extension of Bayesian procedures to integrate and to blend multiple external information into genetic evaluations J. Vandenplas 1,2, N. Gengler 1 1 University."— Presentation transcript:

1 Extension of Bayesian procedures to integrate and to blend multiple external information into genetic evaluations J. Vandenplas 1,2, N. Gengler 1 1 University of Liège, Gembloux Agro-Bio Tech, Belgium 2 National Fund for Scientific Research, Brussels, Belgium 2012 ADSA-ASAS Joint Annual Meeting Phoenix, AZ, July 15-19

2 Introduction Most reliable EBV if estimated from all available sources Most situations –Multiple sources (e.g., dairy breeds) Traditional genetic evaluations International second step (Interbull)  Animals with few (or no) local data: low accuracy –Development of genomic selection  New genomic information sources  Strategies for integration / blending of those multiple external evaluations

3 External information Most situations –EBV and REL from other genetic evaluations –Information not taken into account by a local BLUP  No double-counting between local and external evaluations –Only available for some animals Having, or not, phenotypic information in the local BLUP Present in the pedigree of the local BLUP Special case: MACE-EBV

4 Aim To integrate/blend multiple a priori known external information into a local evaluation  Using a Bayesian approach –Based on Legarra et al. (2007) Quaas and Zhang (2006) Vandenplas and Gengler (2012)

5 Mixed model equations – : Inverse of genetic (co)variances matrix – : vector of local observations – : vector of estimated local fixed effects – : vector of estimated local EBV – Regular BLUP

6 Methods Assumption: A priori known information of –n sources of external information: n vector of external EBV n prediction error (co)variances matrices –Issue: only available for some animals  and : (partially) unknown –EBV and REL from another genetic evaluation –Information not taken into account in a local ssGBLUP Not from an external genomic evaluation  No double-counting between local and external evaluations –Only available for some animals Having, or not, phenotypic or genomic information in the local ssGBLUP Present in the pedigree of the local ssGBLUP

7 Methods For each source i: Estimation of –Available: External EBV of external animals ( ) –Local animals: prediction of external EBV ( )   Correct propagation of external information Predicted external EBV Available external EBV

8 Methods For each source i: Estimation of : Inverse of genetic (co)variances matrix of

9 Methods Integration of n external information Sum of n least square parts of LHS of n hypothetical BLUP of n sources of external EBV Sum of n RHS of n hypothetical BLUP of n sources of external EBV

10 Methods Blending of n external information –Assumption: no local records in

11 Methods Issue: double-counting of information among external animals  Estimation of contributions due to relationships and due to own records  All only depend on contributions due to own records

12 Simulation: blending 100 replicates 2 populations –±1000 animals/population –5 generations –Random matings / cullings –Observations (Van Vleck, 1994) Milk yield for the first lactation Heritability : 0.25 –Fixed effect Random herd effect within population

13 Simulation: blending Performed evaluations InformationExternal BLUP Local BLUP Blending BLUP Joint BLUP Pedigree External population Local population + 50 external sires used locally Phenotypes External observations Local observations External information External EBV and REL (50 external sires) Local EBV and REL (all population)

14 Simulation: blending Performed evaluations InformationExternal BLUP Local BLUP Blending BLUP Joint BLUP Pedigree External population Local population + 50 external sires used locally Phenotypes External observations Local observations External information External EBV and REL (50 external sires) Local EBV and REL (all population)

15 Simulation: blending Performed evaluations InformationExternal BLUP Local BLUP Blending BLUP Joint BLUP Pedigree External population Local population + 50 external sires used locally Phenotypes External observations Local observations External information External EBV and REL (50 external sires) Local EBV and REL (all population)

16 Simulation: blending Performed evaluations InformationExternal BLUP Local BLUP Blending BLUP Joint BLUP Pedigree External population Local population + 50 external sires used locally Phenotypes External observations Local observations External information External EBV and REL (50 external sires) Local EBV and REL (all population)

17 Comparison with joint BLUP Rank correlations (r+SD)  Rankings more similar to those of the joint BLUP EvaluationLocal animalsExternal sires Without external information Local BLUP0.95±0.020.54±0.11 Only external information Blending BLUP With double-counting0.99±0.0040.97±0.01 Without double-counting>0.99±0.000>0.99±0.001

18 Comparison with joint BLUP Mean squared errors (MSE+SD) - Expressed as a percentage of the local MSE  Importance of double-counting EvaluationLocal animalsExternal sires Without external information Local BLUP100.00±26.7100.00±24.5 Only external information Blending BLUP With double-counting21.20±6.26.83±1.9 Without double-counting0.48±0.20.23±0.1

19 Conclusion Bayesian Mixed Model Equations  Rankings most similar to those of a joint BLUP –Importance of double-counting among animals

20 Conclusion Bayesian Mixed Model Equations  Rankings most similar to those of a joint BLUP –Importance of double-counting among animals  Bayesian procedure –Reliable integration/blending of multiple external information –Simple modifications of current programs –Applicable to multi-traits models

21 Special case: MACE Integration of MACE-EBV –Genetic evaluations –Single-step genomic evaluations (cf. oral presentation at this meeting)  Issue: included local information  Estimation of external information free of local information:

22 Special case: MACE Integration of MACE-EBV

23 Special case: MACE Integration of MACE-EBV Inverse of (combined genomic -) pedigree based (co)variances matrix

24 Special case: MACE Integration of MACE-EBV Inverse of (combined genomic -) pedigree based (co)variances matrix Inverse of prediction error (co)variances matrix of MACE-EBV

25 Special case: MACE Integration of MACE-EBV Inverse of (combined genomic -) pedigree based (co)variances matrix Inverse of prediction error (co)variances matrix of MACE-EBV Inverse of prediction error (co)variances matrix of local EBV

26 Special case: MACE Integration of MACE-EBV Inverse of (combined genomic -) pedigree based (co)variances matrix Inverse of prediction error (co)variances matrix of MACE-EBV Inverse of prediction error (co)variances matrix of local EBV RHS of an hypothetical BLUP of MACE-EBV

27 Special case: MACE Integration of MACE-EBV Inverse of (combined genomic -) pedigree based (co)variances matrix Inverse of prediction error (co)variances matrix of MACE-EBV Inverse of prediction error (co)variances matrix of local EBV RHS of an hypothetical BLUP of MACE-EBV RHS of an hypothetical BLUP of local EBV

28 Acknowledgements Animal and Dairy Science (ADS) Department, University of Georgia Athens (UGA), USA Animal Breeding and Genetics Group of Animal Science Unit, Gembloux Agro-Bio Tech - University of Liège (ULg – GxABT), Belgium National Fund for Scientific Research (FNRS), Belgium

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