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 Genetic tests may be repeated (DGAT), shared or sold among countries

Introduction MACE assumption: Genomics: 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

Objectives Overall objectives: Today: 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

Methods De-regression MACE

Methods De-regression MACE GMACE

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 ( = %EDC from genomics) Genomic portion of variance

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%)

Single Sire Example - Daughter equivalents Young Bull (ɣ=100%) 1st Crop Proofs (ɣ=17%) Countries A,B,C D National 20 120 MACE 42 30 172 63 GMACE c=0.0 c=0.5 25 19 163 61 c=1.0 14 155 60

Single Sire Example - GEBV ( ) Young Bull (ɣ=100%) 1st Crop Proofs (ɣ=17%) Countries A,B,C D National 3.0 - 3.5 - 3.0 – 3.5 MACE 4.3 – 4.4 4.2 3.3 – 3.6 3.3 GMACE c=0.0 c=0.5 3.6 – 3.8 3.6 c=1.0 3.1

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

Brown Swiss Data Young USA Bull Reliability GMACE

Brown Swiss Data Young 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 ( =?)

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

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

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

Thank You!