1 / 17

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

dalila
Télécharger la présentation

Development of Genomic GMACE

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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!

More Related