Prediction of Breed Composition & Multibreed Genomic Evaluations

1. Prediction of Breed Composition & Multibreed Genomic Evaluations K. M. Olson and P. M. VanRaden

2. Background - Prediction of Breed • 200 Breed specific SNP were used to verify an animal received the correct breed code in the quality control data step • Several animals had fewer breed-specific SNPs and lower genomic relationships and inbreeding • Wanted to investigate a more precise way to look at breed composition

3. Materials & Methods – Prediction of Breed • Y- Variable was breed of animal • Used both females and males • 3 different sizes of SNP sets were used for the genomic evaluation • The Full 43,385 SNP set • The proposed 3 K SNP set • The 600 breed specific set • Each breed has ~ 200 – used for the basic check currently not a genomic evaluation

4. Materials & Methods – Prediction of Breed • Training data set – animal reliability set to 99% and parent average reliability set to 50% • Proven as of July 2009 • Total of 14,039 animals across all breeds • Validation data set – reliabilities set to 0% • Unproven as of July 2009 • 15,809 animals across all breeds

5. Results – Prediction of Breed • All three tests were able to determine a Holstein that was by pedigree 1/8 (12.5%) Jersey • 43 K test predicted her as 85.9% Holstein and 13.3% Jersey • 3 K predicted she was 84.4% Holstein and 15.5% Jersey • 600 SNP set she was 83.0% Holstein and 16.6% Jersey

6. Results – Prediction of Breed Means and standard deviations for given breed of the validation data set

7. Conclusions – Prediction of Breed • The 43 K chip was the most accurate at prediction of breed composition • The 3 K chip could identify individuals that had large amounts (> 13%) of foreign DNA

8. Obstacles – Prediction of Breed • There is a patent • Located at http://www.patentstorm.us/patents/7511127/fulltext.html • May not be accurate for animals from different populations • foreign animals • older animals

9. Background - Multibreed • Multibreed methods are currently used in traditional methods • Only within breed methods are used for genomics evaluations • Previous research has shown little improvement in accuracy from using all breeds with the 50K SNP chip however, little research has been done using multi-trait methodology

10. Objectives – Multibreed genomic evaluations • To investigate three different methods of multibreed genomic evaluations using Holsteins, Jerseys, and Brown Swiss genotypes

11. Materials & Methods – Multibreed (Animals) • 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 June 2009 • Holsteins – 2,477 • Jerseys – 410 • Brown Swiss - 182

12. Material & Methods – Multibreed (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 multitrait method ( as explained by VanRaden and Sullivan, 2010)

13. Results – P – Values for Protein Yield

14. Results – P-values for protein yield

15. Results – P-Values for protein yield The traditional GPTA was not included in these analyses

16. Conclusions – Multibreed Genomic Evaluation • Method 1 did not help the estimates for genomic evaluations • Method 2 increased the predictive ability, however the traditional GPTA accounted for more variation than the across-breed GPTA • Method 3 increased the predictive ability and the multi-breed GPTA accounted for more variation than the traditional GPTA

17. Implications • The multibreed genomic evaluations do slightly increase the accuracy of the evaluations, but may not warrant the increased computational demands • A higher density SNP chip would most likely increase the gains in accuracy for multibreed genomic evaluations • Multibreed would be needed for genomic selection in crossbred herds • Not much demand for that yet