1. 2007 Paul VanRaden, George Wiggans, Jeff O’Connell, John Cole, Animal Improvement Programs Laboratory Tad Sonstegard, and Curt Van Tassell Bovine Functional Genomics Laboratory USDA Agricultural Research Service, Beltsville, MD, USA Paul.VanRaden@ars.usda.gov Paul.VanRaden@ars.usda.gov 2009 National and International Genomic Evaluation Methods

2. Council on Dairy Cattle Breeding, April 2009 (2)Paul VanRaden 2009 Topics  Evaluation schedules, data, reliability  International genomic evaluation Conversions for young bulls Genomic MACE for old bulls Interbull Brown Swiss genomic proposal  Reduction of yield trait heritability to reduce elite cow PTA, young bull PA bias  Low density, low cost genotyping

3. Council on Dairy Cattle Breeding, April 2009 (3)Paul VanRaden 2009 Traditional, Genomic, MACE Schedule  Review of Dec/Jan, Feb, and Apr evaluations  Recompute genomic PTAs after new MACE PTAs arrive  Aug evaluation must start earlier to provide genomic PTAs of young bulls to Interbull  Interbull conversions to begin in Aug, MACE for proven bulls in 2010  Genotype, cow PTA, pedigree exchange with Canada, Switzerland, etc.

4. Council on Dairy Cattle Breeding, April 2009 (4)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 Include phenotypes of non-genotyped ancestors 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

5. Council on Dairy Cattle Breeding, April 2009 (5)Paul VanRaden 2009 January Evaluation  HO, JE genomic PTAs official in Jan. Genomic from Dec 1, domestic Dec 18 Traditional PTAs sent to Interbull MACE used if foreign daughters included Genomic PTA used for most bulls (80%) Traditional used if many new daughters Genomic PTA transferred to descendants (to ancestors in future)

6. Council on Dairy Cattle Breeding, April 2009 (6)Paul VanRaden 2009 February Evaluation  Interim, official only for new genotypes Animals genotyped during Dec and Jan Active bulls not updated officially Unofficial PTAs provided in March for proven bulls  March evaluation (interim interim) Added 96 bulls accidentally left out of Feb Tested fast reliability approximation  Brown Swiss now have 719 genotyped Traded with Switzerland in March 2009

7. Council on Dairy Cattle Breeding, April 2009 (7)Paul VanRaden 2009 April Evaluation  Compute domestic, then genomic January type used by mistake Reliability approximate, not exact  Selection index calculation Replace previous with current MACE SNP effects and subset PTA same Similar to young bull calving ease Suggested by CDN researchers

8. Council on Dairy Cattle Breeding, April 2009 (8)Paul VanRaden 2009 June Evaluation (Plans)  Net Merit as sum instead of trait Evaluate traits, then sum, instead of sum traits, then evaluate NM as trait Large differences for CAN cows – Individual traits were converted to US scale, but not NM Small changes for bulls and US cows – Nearly all changes < $50 – Corr (NM as sum, NM as trait) >.996

9. Council on Dairy Cattle Breeding, April 2009 (9)Paul VanRaden 2009 August Evaluation (Plans)  Interbull converts genomic PTAs Young bulls only EU requires 50% REL for marketing Proven bulls next year (2010) AIPL must compute domestic and genomic earlier to meet deadline  Decrease yield heritability to make PAs and cow PTAs less biased

10. Council on Dairy Cattle Breeding, April 2009 (10)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 needed

11. Council on Dairy Cattle Breeding, April 2009 (11)Paul VanRaden 2009 Worldwide Dairy Genotyping as of January 2009 CountriesAnimals United States and Canada22,344 France8,500 Netherlands, New Zealand 1 6,000 New Zealand and Ireland4,500 Germany3,000 Australia2,000 Denmark, Finland, Sweden2,000 1 Using a customized Illumina 50K chip (different markers)

12. Council on Dairy Cattle Breeding, April 2009 (12)Paul VanRaden 2009 Foreign DNA in North American Data Proven bulls, Young bulls, and Females CtryoldyngfemCtryoldyngfem CHE6100GBR8111 DEU4148118DNK1181 NLD3220099CZE6150 FRA149013LUX0010 ITA30228BEL320 AUS33320NZL400 HUN8345FIN100

13. Council on Dairy Cattle Breeding, April 2009 (13)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 official on USA scale for many foreign animals (not just CAN)

14. Council on Dairy Cattle Breeding, April 2009 (14)Paul VanRaden 2009 International Evaluation  Traditional genetic evaluations MACE instead of merging phenotypes Small benefits expected from data merger Proven bulls only, not cows or young bulls  Parentage testing, genetic recessives, pedigrees done by breed associations  Genomics: what role for Interbull? Benefits of sharing genotypes are large Brown Swiss genotype sharing proposal

15. Council on Dairy Cattle Breeding, April 2009 (15)Paul VanRaden 2009 Genotype Exchange Options  Give away for free (not likely)  Genotype own bulls, then trade? Trade an equal number or all bulls? Country A has 5000 and B has 1000 Proportional to population size?  Trade among organization pairs or create central genomic database?  Service fee for young animals to pay for ancestor genotyping?

16. Council on Dairy Cattle Breeding, April 2009 (16)Paul VanRaden 2009 Share Young Bull, Cow Genotypes? USA – CAN exchange  May be marketed in >1 country  Exchange of young animals and females more important as their REL increases with genomics  Helps to synchronize databases  Could lead to joint evaluation

17. Council on Dairy Cattle Breeding, April 2009 (17)Paul VanRaden 2009 Problems of Not Sharing  Genetic progress not as fast as with full access to genotypes  Limits on research access to genotypes (secrecy)  Genomics may lead to natural monopoly Small companies / countries can’t afford to buy sufficient genotypes

18. Council on Dairy Cattle Breeding, April 2009 (18)Paul VanRaden 2009 Simulation Results World Holstein Population  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

19. Council on Dairy Cattle Breeding, April 2009 (19)Paul VanRaden 2009 Genotyped Animals (n=25,393) In North America as of April 2009

20. Council on Dairy Cattle Breeding, April 2009 (20)Paul VanRaden 2009 Experimental Design - Update Holstein, Jersey, and Brown Swiss breeds HOLJERBSW Predictor: Bulls born <20004,4221,149472 Cows with data94721240 Total5,3691,361512 Predicted: Bulls born >20002,035388150 Data from 2004 used to predict independent data from 2009

21. Council on Dairy Cattle Breeding, April 2009 (21)Paul VanRaden 2009 Reliability Gain 1 by Breed Yield traits and NM$ of young bulls TraitHOJEBS Net merit2489 Milk26617 Fat321110 Protein24214 Fat %50368 Protein %382910 1 Gain above parent average reliability ~35%

22. Council on Dairy Cattle Breeding, April 2009 (22)Paul VanRaden 2009 Reliability Gain by Breed Health and type traits of young bulls TraitHOJEBS Productive life32712 Somatic cell score23317 Dtr pregnancy rate28718 Final score2025 Udder depth37208 Foot angle2511 Trait average29128

23. Council on Dairy Cattle Breeding, April 2009 (23)Paul VanRaden 2009 Genomic Daughter Equivalents from April 2009 published reliabilities TraitHOJEBS Net Merit30159 Milk2485 Prod Life583017 SCS492414 DPR1095734 Type18154

24. Council on Dairy Cattle Breeding, April 2009 (24)Paul VanRaden 2009 Expected Change in Net Merit Holstein – April 2009  SD = 163 * √(REL G – REL T ) = $95 for young bulls (.69 -.35) = $23 for proven bulls (.86 -.84)  Daughter equivalents for NM$ 10 from parent average 30 from genomics 40 total for young animals

25. Council on Dairy Cattle Breeding, April 2009 (25)Paul VanRaden 2009 Value of Genotyping More Animals Actual and predicted gains for 27 traits and for Net Merit BullsReliability Gain PredictorPredictedNM$27 trait avg 21302611317 3576175923 442220352429 760096903433 Cows: 947 2711

26. Council on Dairy Cattle Breeding, April 2009 (26)Paul VanRaden 2009 Do Cows and Old Bulls Help? Research by Marcos da Silva, BFGL, using Nov 2004 cutoff REL GainNo CowsNo Bulls < 85 TraitHOJEHOJEHOJE NM$248239248 Milk266277244 PL327316326 SCS333326 3 Type202 1191

27. Council on Dairy Cattle Breeding, April 2009 (27)Paul VanRaden 2009 Yield Trait Heritability  30% used Aug 1997-present for HO 35% used Nov 2000-present for JE and BS Van Tassell et al., 1999 JDS 82:2231 Deviations limited to 4 SD since 1997 Herd variance adjustment since 1991  25% from 1989-1997  20% from 1974-1988  19% from 1966-1973  31% from 1962-1965

28. Council on Dairy Cattle Breeding, April 2009 (28)Paul VanRaden 2009 Heritability Test  Change in PTA protein of elite cows and bulls Top 100 cows in each breed Top 10 bulls in each breed  Predict son’s current DYD from dam’s 2005 PTA 30% h 2 for HO, GU, AY; 35% JE, BS 25% h 2 for HO, GU, AY; 29% JE, BS 20% h 2 for HO, GU, AY; 23% JE, BS 15% h 2 for HO, GU, AY; 18% JE, BS

29. Council on Dairy Cattle Breeding, April 2009 (29)Paul VanRaden 2009 Effect of h 2 on Top PTAs for Protein Change in cow and bull means compared to current h 2 Top 100 CowsTop 10 Bulls.30.25.20.15.25.20.15 HO-6.4-11.0-14.8-1.8-3.8-6.4 JE 1 -4.1-5.4-6.2-2.0-4.0-6.8 BS 1 -3.5-4.8-5.8-3.5-5.4-8.1 GU-1.5-1.1-.7-2.8-4.1-6.0 1 JE and BS heritability set to HO h 2 * (.35 / 30)

30. Council on Dairy Cattle Breeding, April 2009 (30)Paul VanRaden 2009 Effect of h 2 on Corr(dam, son) Dam PTA 2005 and son DYD protein 2009 Breed.30.25.20.15 HO.437.449.459.466 JE 1.553.559.562.567 BS 1.083.071.070.039 GU.425.423.415.394 AY.554.549.545 1 JE and BS heritability set to HO h 2 * (.35 / 30)

31. Council on Dairy Cattle Breeding, April 2009 (31)Paul VanRaden 2009 Low Density SNP Chip  Choose 384 marker subset SNP that best predict net merit Parentage markers to be shared  Use for initial screening of cows 40% benefit of full set for 10% cost Could get larger benefits using haplotyping (Habier et al., 2008)

32. Council on Dairy Cattle Breeding, April 2009 (32)Paul VanRaden 2009 Marker Effects for Net Merit

33. Council on Dairy Cattle Breeding, April 2009 (33)Paul VanRaden 2009 Gains by Trait for 384 SNPs selected for Holstein Net Merit TraitCorr(NM)38,416384Ratio Net Merit1.0024%9%.38 Milk.5426%5%.20 Fat.6632%12%.39 Protein.6224%9%.39 Prod. Life.6732%0%.0 SCS-.3723%0%.0 Preg Rate.2728%0%.0

34. Council on Dairy Cattle Breeding, April 2009 (34)Paul VanRaden 2009 Conclusions  Genomic reliability > traditional 30-40% with traditional parent average 60-70% using 8,100 genotyped Holsteins 81-83% from 40,000 simulated bulls Gains for US Jersey and Brown Swiss breeds smaller, but improving  Young bull conversions, reduced yield heritability in May Interbull test Due April 28

35. Council on Dairy Cattle Breeding, April 2009 (35)Paul VanRaden 2009 Acknowledgments  Genotyping and DNA extraction: USDA Bovine Functional Genomics Lab, U. Missouri, U. Alberta, GeneSeek, Genetics & IVF Institute, Genetic Visions, and Illumina  Computing: AIPL staff (Mel Tooker, Leigh Walton, Jay Megonigal)  Funding: National Research Initiative grants – 2006-35205-16888, 2006-35205-16701 Agriculture Research Service Holstein, Jersey & Brown Swiss breed associations Contributors to Cooperative Dairy DNA Repository (CDDR)