Multibreed Genomic Evaluations in Purebred Dairy Cattle

1. Multibreed Genomic Evaluations in Purebred Dairy Cattle K. M. Olson1 and P. M. VanRaden2 1National Association of Animal Breeders 2AIPL, ARS, USDA Beltsville, MD katie.olson@ars.usda.gov

2. Background • Multibreed methods are currently used in traditional evaluations • Only within breed methods are used for genomic evaluations • 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. Background • Smaller breeds are interested in genomic evaluations • Genomic evaluations on crossbreds • 1999 2,236 1st lactation crossbreds, 2009 there were 23,209 • With the 3k might be more demand • Currently, system not set up to handle crossbred data

4. Objectives • To investigate different methods of multibreed genomic evaluations using purebred Holsteins, Jerseys, and Brown Swiss genotypes

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

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

7. Method 1 (breed SNP effects) • Estimated SNP effects within breed • Applied those SNP effects to the other breeds • Multiple regressions were used to test the GPTA using other breeds SNP effects along with PA

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

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

10. Results – prediction of protein yield P-Values

11. 0.8 0.7 0.6 HO SNP 0.5 JE SNP 2 0.4 R BS SNP 0.3 PA Only 0.2 0.1 0 Holstein Jersey Brown Swiss R2 adjusted for Method 1

12. Correlation GPTAs and other Breeds’ GPTAs

13. Results – prediction of protein yield P-Values

14. Results – R2 for protein yield

15. Correlation with traditional GPTA

16. R2 of different correlation levels for multi-breed 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. Conclusions • Using another breeds SNP estimates did not help • Across-breed method increased the predictive ability, however the traditional GPTA accounted for more variation than the across-breed GPTA • Multi-breed increased the predictive ability and the multi-breed GPTA accounted for more variation than the traditional GPTA

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

19. Questions