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National and International Genomic Evaluations for Dairy Cattle

National and International Genomic Evaluations for Dairy Cattle. CAN, USA Combined Phenotypes. Joint evaluations tested and reported at 1991 ADSA meeting Banos and Wiggans, Robinson and Wiggans, Powell et al, Wiggans et al Both countries used Cornell computer

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National and International Genomic Evaluations for Dairy Cattle

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  1. National and International Genomic Evaluations for Dairy Cattle

  2. CAN, USA Combined Phenotypes • Joint evaluations tested and reported at 1991 ADSA meeting • Banos and Wiggans, Robinson and Wiggans, Powell et al, Wiggans et al • Both countries used Cornell computer • Animal models applied to yield data of Jerseys and Ayrshires • Correlations .98 and .96 between combined vs. converted evaluation

  3. International Evaluation • Traditional genetic evaluations • Small benefits from merging phenotypes • MACE used instead to merge EBV files • Proven bulls only, not cows or young bulls • Genomics: what role for Interbull? • Large benefits from sharing genotypes • Brown Swiss genotype sharing project • Less benefits from combining only GEBV using G-MACE

  4. Topics • National genomic evaluation • Value of cows and old bulls as predictors • Deregression, blending, polygenic effects • Reliability approximation • International genomic evaluation • Simple conversion formulas • Exchange of genomic EBVs via G-MACE • Multi-country exchange of genotypes • Update on US and world analyses

  5. Bulls and Cows as PredictorsHolstein, Jersey, and Brown Swiss breeds 1Data from 2004used to predict independent data from 2009

  6. Do Cows and Old Bulls Help?Research by Marcos da Silva, BFGL

  7. Deregression Methods • Simple (remove parent average) • Method used in US = PA + (PTA – PA) / REL • Partial (remove parents and genotyped progeny) • Method includes cows without double-counting information from progeny • [PTA – w1 PA – w3∑(2PTAprog – PTAmate)/DEprog] / w2 • Predictions very similar to simple deregression • Decided not to implement at this time • Matrix (remove sire, MGS, and all sons) • Method used in Canada = D-1 (D + A-1k) a^

  8. Genomic Methods • Direct genomic value (DGV) • Sum of effects for 38,416 genetic markers • Now displayed for NM$ with chromosome query • Combined genomic evaluation (code 1) • Include phenotypes not used in estimating DGV • Selection index includes 3 PTAs per animal • Traditional, direct genomic, and subset PTA • Transferred genomic evaluation (code 2) • Propagate from genotyped animals to non-genotyped descendants by selection index • Propagation to ancestors being developed

  9. Calculation of Reliabilityfor individual animals • Inversion and discounting • Diagonals of (D + G-1 k)-1 and (D + A-1 k)-1 • Gain in daughter equivalents times .6 • Simple approximation used in USA • Gain in DE = ∑(REL – RELpa) k / 2000 for all genotyped animals • Could adjust for Ne of breed or for number of close relatives • Used in April 2009 to beat deadline

  10. Genomic Daughter EquivalentsHolstein bulls, June 2009 1DE from inverse * .6

  11. Include Polygenic Effect? • Markers explain <100% of genetic variance • y = Zg + a + e, and Var(u) = w G + (1-w) A • In simulation, w = .95 had highest accuracy, and regressions were close to 1 • Tested w = .60, .80, and .95 in real data • Nov 2004 Holstein data • Linear instead of nonlinear SNP estimates • Polygenic effect now in nonlinear program • More important with low-density SNP chip

  12. R2 with Polygenic Effect

  13. Regressions with Polygenic Effect

  14. Blending of Interbull PTAs • Order of national calculations • Phenotypic animal model evaluation • Direct genomic evaluation (DGV), using previous MACE for foreign bulls • Selection index combining DGV, animal model PTA, and subset PTA • Redo last step, using new MACE PTA • Plan to implement in August • Suggested by Brian Van Doormaal, CDN

  15. Interbull Evaluation (Plans) • Convert genomic EBVs • Young bulls from FRA, NLD, NZL • EU requires 50% REL for marketing • Combine using G-MACE (2010) • Proven bulls next year (2010) • Countries must compute domestic and genomic evaluations 1-2 weeks earlier to meet Interbull deadline • Currently genomics, MACE at same time

  16. Genomic MACEInterbull Genomics Task Force • Residuals correlated across countries • Repeated tests of the same major gene, or • SNP effects estimated from common bulls • Let cij = proportion of common bulls • Let gi = DEgen / (DEdau + DEgen) • Corr(ei, ej) = cij * Corr(ai, aj) * √(gi * gj) • Avoids double counting genomic information from multiple countries i, j • New deregression formulas tested

  17. Multi-Country Combined Genotypes • Evaluation methods • Foreign data included via MACE, then single-trait genomic evaluation • Domestic and foreign data evaluated using multi-country genomic model • Advantages of multi-trait model • Phenotypic and genomic both multi-trait • Domestic data weighted more than foreign • More accurate ranking than G-MACE

  18. Multi-Country Genotype Model b1 b2 g1 g2 a1 a2 X’R1y X’R2y Z’R1y Z’R2y R1y R2y X’R1X 0 X’R1Z 0 X’R1 0 0 X’R2X 0 X’R2Z 0 X’R2 Z’R1X 0 Z’R1Z+Ik11 Ik12 Z’R1 0 0 Z’R2X Ik21 Z’R2Z+Ik22 0 Z’R2 R1’X 0 R1Z 0 R1+A-1λ11 A-1λ12 0 R2’X 0 R2Z A-1λ21 R2+A-1λ22 = Trait genetic covariance matrix = T, and Var-1(error) = R Marker variance ratio kij = (T-1)ij/ [∑ 2p(1-p) * w] Polygenic variance ratio λij = (T-1)ij/ (1 – w)

  19. Multi-Country Computationwith shared genotype files • USA-CAN, 2 trait model • 10,129 HO with data, 11,815 without • Block-diagonal solver converged in 250 iterations (similar to single-trait) • 11 hours using 2 processors • Global Brown Swiss, 9 countries • All 8,073 proven bulls simulated • 30 hours using 9 processors

  20. Proven Bull ReliabilitySimulated BS bulls on home country scale

  21. Young Bull Reliability120 simulated BS bulls sampled in USA

  22. Holstein Simulation ResultsWorld population, single-trait methods • 40,360 older bulls to predict 9,850younger bulls in Interbull file • 50,000 or 100,000 SNP; 5,000 QTL • Reliability vs. parent average REL • Genomic REL = corr2 (EBV, true BV) • 81% vs 30% observed using 50K • 83% vs 30% observed using 100K

  23. Country Borders • Most phenotypic data collected and stored within country • Genomic data allows simple, accurate prediction across borders • Need traditional EBV or PA for foreign animals, but not available for young bulls, cows, or heifers • May need full foreign pedigrees • Genomic evaluations rapidly becoming international • DEU, FRA, NLD, DFS Holstein cooperation • World Brown Swiss cooperation • Accuracy requires very many genotypes

  24. Conclusions • National evaluation options • Include cows as predictors? • Include polygenic effect in model? • International evaluation options • Conversion formulas for young bulls • G-MACE to exchange GEBVs • Direct multi-country genomic evaluation works well

  25. Acknowledgments -1 • Genotyping and DNA extraction: • USDA Bovine Functional Genomics Lab, U. Missouri, U. Alberta, GeneSeek, Genetics & IVF Institute, Genetic Visions, DNA Landmarks, and Illumina • Computing: • AIPL, CDN, and U. Guelph staff

  26. Acknowledgments -2 • Interbull Genomics Task Force: • Georgios Banos, Esa Mantysaari, Mario Calus, Vincent Ducrocq, Zengting Liu, Hossein Jorjani, and João Dürr • Data subset research: • Marcos da Silva

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