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Development of Genomic GMACE

Development of Genomic GMACE. Pete Sullivan, CDN & Paul VanRaden*, USDA. Introduction. Genomics is here now National EBVs are being replaced by GEBVs GEBVs may not be strictly national Countries may share data, or use MACE

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Development of Genomic GMACE

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  1. Development of GenomicGMACE Pete Sullivan, CDN & Paul VanRaden*, USDA

  2. Introduction • Genomics is here now • National EBVs are being replaced by GEBVs • GEBVs may not be strictly national • Countries may share data, or use MACE • Genetic tests may be repeated (DGAT), shared or sold among countries

  3. Introduction • MACE assumption: • National data sets are independent, i.e. separate recorded cow populations • Sire with EBV from 100 daughters in 3 countries has 300 daughters in total • Genomics: • Sire can have GEBV from 100 daughters in 3 countries with only 100 daughters in total • MACE must evolve to GMACE

  4. Objectives • Overall objectives: • Update MACE model for genomic input data from participating countries • Create GMACE software for application as early as 2010 • Today: • Introduce GMACE with some examples • Preliminary results from software tests • Reactions and feedback from members

  5. Methods De-regression MACE

  6. Methods De-regression MACE GMACE

  7. ( = %EDC from genomics) Methods • D is a diagonal matrix • Residual variances of de-regressed proofs • E = D plus genomic covariances from shared data % common (shared) data Max correlation between genomic predictions Genomic portion of variance

  8. Single Sire Example • GMACE for a single sire • GEBV in 3 countries (A, B, C) • EDC from genomics = 20 • No data in 4th country D • All rg = .90, h2 = .33, • as a young bull (EDC=0+20, ɣ=100%) • with 1st crop proofs in A, B and C (EDC=100+20, ɣ=17%)

  9. Single Sire Example- Daughter equivalents

  10. Single Sire Example- GEBV ( )

  11. Brown Swiss Data- 9 countries • Used Pedigree and EBV from April 2009 Interbull files to simulate bull genotypes • 50,000 SNP and 10,000 QTL • VanRaden (2009) • 8073 proven bulls • 120 young bulls sampled in U.S.A. used to test genomic predictions

  12. Brown Swiss DataYoung USA Bull Reliability GMACE

  13. Brown Swiss DataYoung USA Bull Reliability • GMACE reliability very close to global genomic evaluation model • If “c” is correct • No shared data for GEBV in this study (i.e. c=0.0) • Some error in “c” seems O.K. (c=0.5 still good) • How robust is GMACE? • c=? %EDC from genomics ( =?)

  14. GMACE – next steps • Need to test GMACE when data are shared among countries for national GEBVs (c>0) • Need to extend MACE / MT-MACE reliability approximation for GMACE • Needs multi-country progeny absorption if c>0 • May need MT-GMACE (e.g. for fertility traits) • Are national cow EBVs useful for GMACE? • Question also for MACE, but • More important for young genotyped bulls in GMACE

  15. Conclusions • Software to extend from national to international GEBVs nearing completion • Application by Interbull in 2010? 2011? • A few developments and validation tests are still needed for the model and software • Enhancements may be added after the initial application • Extension to MT-GMACE • Model refinements

  16. Acknowledgements • Interbull genomics task force • Georgios Banos • Mario Calus • Vincent Ducrocq • João Dϋrr • Hossein Jorjani • Esa Mäntysaari • Zengting Liu

  17. Thank You!

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