1. Multibreed Genomic Evaluations in Purebred Dairy Cattle K. M. Olson 1 and P. M. VanRaden 2 1 National Association of Animal Breeders 2 AIPL, ARS, USDA Beltsville, MD katie.olson@ars.usda.gov

2. July 2010 ADSA (2) K. M. Olson Background l Multibreed methods are currently used in traditional evaluations l Only within breed methods are used for genomic evaluations l Previous research has shown little improvement in accuracy from combining breeds for genomic evaluations however, little research has been done using multi-trait methodology

3. July 2010 ADSA (3) K. M. Olson Background l Smaller breeds are interested in genomic evaluations l Genomic evaluations on crossbreds w 1999 2,236 1 st lactation crossbreds, 2009 there were 23,209 w With the 3k might be more demand − Currently, system not set up to handle crossbred data

4. July 2010 ADSA (4) K. M. Olson Objectives l To investigate different methods of multibreed genomic evaluations using purebred Holsteins, Jerseys, and Brown Swiss genotypes

5. July 2010 ADSA (5) K. M. Olson Materials & Methods – Animals l Animals genotype Illumina BovineSNP50 w 43,385 SNP l The training data set - animals were proven by Nov. 2004 w Holsteins – 5,331 w Jerseys – 1,361 w Brown Swiss – 506 l The validation data set - animals were unproven as of Nov. 2004 and proven by Aug. 2009 w Holsteins – 2,507 w Jerseys – 413 w Brown Swiss - 185

6. July 2010 ADSA (6) K. M. Olson Overview - Methods l Method 1 estimated SNP effects within breed then applied those effects to the other breeds l Method 2 (across-breed) used a common set of SNP effects from the combined breed genotypes and phenotypes l Method 3 (multi-breed) used a correlated SNP effects using a multi-trait method

7. July 2010 ADSA (7) K. M. Olson Method 1 (breed SNP effects) l Estimated SNP effects within breed l Applied those SNP effects to the other breeds l Multiple regressions were used to test the GPTA using other breeds SNP effects along with PA

8. July 2010 ADSA (8) K. M. Olson Method 2 - (across-breed) l All breeds were treated as one population w Base allele frequency assumed to be 0.33 for each breed l Breed PTAs were converted to the Holstein 2004 Base l Multiple regressions were used to test across breed GPTA along with PA

9. July 2010 ADSA (9) K. M. Olson Method 3 – (multi-breed) l Used a multi-trait genomic method as explained by VanRaden and Sullivan, 2010 w Breeds instead of countries w Animals were purebreds − Their information only used for their respective breed − Assumption of independent residuals l Three levels of correlation were tested w 0.20, 0.30, and 0.55 for Protein yield

10. July 2010 ADSA (10) K. M. Olson Results – prediction of protein yield P -Values HolsteinJerseyBrown Swiss Traditional PA< 0.001 0.061 GPTA< 0.001 0.086 Method 1 HO GPTA-0.6850.258 JE GPTA0.200-0.872 BS GPTA0.2970.829-

11. July 2010 ADSA (11) K. M. Olson R 2 adjusted for Method 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 HolsteinJersey Brown Swiss R 2 HO SNP JE SNP BS SNP PA Only

12. July 2010 ADSA (12) K. M. Olson Correlation GPTAs and other Breeds’ GPTAs

13. July 2010 ADSA (13) K. M. Olson Results – prediction of protein yield P -Values HolsteinJerseyBrown Swiss Across-breed PA<0.001 0.2016 ABGPTA< 0.001 0.0023 Multi-breed PA< 0.001 0.055 MBGPTA< 0.001 0.081

14. July 2010 ADSA (14) K. M. Olson Results – R 2 for protein yield HolsteinJerseyBrown Swiss PA only0.31420.43620.0933 Traditional0.50450.48740.1030 Across-breed0.47420.47310.1336 Multi-breed0.50600.49160.1067

15. July 2010 ADSA (15) K. M. Olson Correlation with traditional GPTA

16. July 2010 ADSA (16) K. M. Olson R 2 of different correlation levels for multi-breed Correlation Level/ Breed 0.200.30 0.55 Holstein0.50470.50600.5053 Jersey0.49120.49160.4912 Brown Swiss0.10300.10670.1030 The correlation yielding best results was 0.30 - results in 0.09 sharing between breeds Denser SNP panels would likely result in a higher correlation, therefore greater gains across breeds

17. July 2010 ADSA (17) K. M. Olson Conclusions l Using another breeds SNP estimates did not help l Across-breed method increased the predictive ability, however the traditional GPTA accounted for more variation than the across- breed GPTA l Multi-breed increased the predictive ability and the multi-breed GPTA accounted for more variation than the traditional GPTA

18. July 2010 ADSA (18) K. M. Olson Implications l The multi-breed does slightly increase the accuracy, but may not warrant the increased computational demands l Higher density SNP chips would most likely increase the gains in accuracy for multi-breed genomic evaluations l Across-breed or multi-breed would be needed for genomic selection in crossbred herds w Not much demand for that yet

19. July 2010 ADSA (19) K. M. Olson Questions