2007 Paul VanRaden Animal Improvement Programs Lab, USDA, Beltsville, MD, USA Pete Sullivan Canadian Dairy Network, Guelph, ON, Canada 2009 National and International Genomic Evaluations for Dairy Cattle

ADSA / ASAS annual meeting, July 2009 (2)Paul VanRaden 2009 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

ADSA / ASAS annual meeting, July 2009 (3)Paul VanRaden 2009 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

ADSA / ASAS annual meeting, July 2009 (4)Paul VanRaden 2009 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

ADSA / ASAS annual meeting, July 2009 (5)Paul VanRaden 2009 Bulls and Cows as Predictors Holstein, Jersey, and Brown Swiss breeds HOLJERBSW Historic test: 1 Bulls born <20004,4221, Cows with data June predictions: Proven bulls7,8831, Cows with data3, Data from 2004 used to predict independent data from 2009

ADSA / ASAS annual meeting, July 2009 (6)Paul VanRaden 2009 Do Cows and Old Bulls Help? Research by Marcos da Silva, BFGL REL GainAdd CowsAdd Bulls <85 TraitHOJEHOJEHOJE NM$ Milk PL SCS Type2020+1

ADSA / ASAS annual meeting, July 2009 (7)Paul VanRaden 2009 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 – w 1 PA – w 3 ∑(2PTA prog – PTA mate )/DE prog ] / w 2 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 -1 k) a^

ADSA / ASAS annual meeting, July 2009 (8)Paul VanRaden 2009 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

ADSA / ASAS annual meeting, July 2009 (9)Paul VanRaden 2009 Calculation of Reliability for 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 N e of breed or for number of close relatives Used in April 2009 to beat deadline

ADSA / ASAS annual meeting, July 2009 (10)Paul VanRaden 2009 Genomic Daughter Equivalents Holstein bulls, June 2009 TraitAgeInverseDiscount 1 Approx NM$Yng Old YieldYng Old FertilityYng Old DE from inverse *.6

ADSA / ASAS annual meeting, July 2009 (11)Paul VanRaden 2009 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

ADSA / ASAS annual meeting, July 2009 (12)Paul VanRaden 2009 R 2 with Polygenic Effect Trait Net Merit $ Milk yield Productive Life SCS Fertility

ADSA / ASAS annual meeting, July 2009 (13)Paul VanRaden 2009 Regressions with Polygenic Effect Trait Net Merit $ Milk yield Productive Life SCS Fertility

ADSA / ASAS annual meeting, July 2009 (14)Paul VanRaden 2009 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

ADSA / ASAS annual meeting, July 2009 (15)Paul VanRaden 2009 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

ADSA / ASAS annual meeting, July 2009 (16)Paul VanRaden 2009 Genomic MACE Interbull Genomics Task Force  Residuals correlated across countries Repeated tests of the same major gene, or SNP effects estimated from common bulls Let c ij = proportion of common bulls Let g i = DE gen / (DE dau + DE gen ) Corr(e i, e j ) = c ij * Corr(a i, a j ) * √(g i * g j )  Avoids double counting genomic information from multiple countries i, j  New deregression formulas tested

ADSA / ASAS annual meeting, July 2009 (17)Paul VanRaden 2009 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

ADSA / ASAS annual meeting, July 2009 (18)Paul VanRaden 2009 Multi-Country Genotype Model X’R 1 X 0 X’R 1 Z 0 X’R X’R 2 X 0 X’R 2 Z 0 X’R 2 Z’R 1 X 0 Z’R 1 Z+Ik 11 Ik 12 Z’R Z’R 2 X Ik 21 Z’R 2 Z+Ik 22 0 Z’R 2 R 1 ’X 0 R 1 Z 0 R 1 +A -1 λ 11 A -1 λ 12 0 R 2 ’X 0 R 2 Z A -1 λ 21 R 2 +A -1 λ 22 b1b2g1g2a1a2b1b2g1g2a1a2 = X’R 1 y X’R 2 y Z’R 1 y Z’R 2 y R 1 y R 2 y Trait genetic covariance matrix = T, and Var -1 (error) = R Marker variance ratio k ij = (T -1 ) ij / [∑ 2p(1-p) * w] Polygenic variance ratio λ ij = (T -1 ) ij / (1 – w)

ADSA / ASAS annual meeting, July 2009 (19)Paul VanRaden 2009 Multi-Country Computation with 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

ADSA / ASAS annual meeting, July 2009 (20)Paul VanRaden 2009 Proven Bull Reliability Simulated BS bulls on home country scale TraditionalGenomic CountryNat’lMACENat’lMulti-trait USA CAN CHE91 92 NZL

ADSA / ASAS annual meeting, July 2009 (21)Paul VanRaden 2009 Young Bull Reliability 120 simulated BS bulls sampled in USA TraditionalGenomic CountryNat’lMACENat’lMulti-trait USA CAN CHE NZL11126

ADSA / ASAS annual meeting, July 2009 (22)Paul VanRaden 2009 Holstein Simulation Results World population, single-trait methods  40,360 older bulls to predict 9,850 younger bulls in Interbull file  50,000 or 100,000 SNP; 5,000 QTL  Reliability vs. parent average REL Genomic REL = corr 2 (EBV, true BV) 81% vs 30% observed using 50K 83% vs 30% observed using 100K

ADSA / ASAS annual meeting, July 2009 (23)Paul VanRaden 2009 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

ADSA / ASAS annual meeting, July 2009 (24)Paul VanRaden 2009 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

ADSA / ASAS annual meeting, July 2009 (25)Paul VanRaden 2009 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

ADSA / ASAS annual meeting, July 2009 (26)Paul VanRaden 2009 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