1. Session 4 – Part 1 Status update on genomically enhanced genetic evaluation by breeds Dr. Matt Spangler, University of Nebraska-Lincoln

2. Past Status=Confusion CEBWWWYWMCEMMMWW Adj. 907001320 Ratio 101107 EPD 9254931123 Acc.29.37.30.27.18.19.23 YGMarbBFREA Adj. 4.65%.2312.5 Ratio 10610095 EPD.21.44.05-.39 Acc.32.31.33.34 REATENDMARB 768

3. u American Simmental Association ãTenderness u American Angus Association ã“Correlated Trait Approach”—Kachman (2008) ãMade widely known by MacNeil (2010) Merger of All Information

4. Adoption of Genomic Predictions u Efficacy of this technology is not binary u The adoption of this must be centered on the gain in EPD accuracy ãThis is related to the proportion of genetic variation explained by a Molecular Breeding Values (MBV; Result of DNA Test) % GV = squared genetic correlation

5. u Simplification u Conceptually appealing u Fit current NCE u Information benefits more than just the genotyped animal u Multiple companies increases complexity u Assumes MBV predicts all animals equally as well Tremendous First Step

6. u Existing projects helped as a starting platform ã2,000 Bull Project ãUniversity of Missouri ãWeight Trait Project Other Breeds Made An Investment

7. u Other breeds sought help from the NBCEC (Garrick) in training u Process now largely moved back to companies/breed associations Paradigm SHIFT

8. u All different themes on the Two-Step Approach u Why? ãMethod of inclusion dependent on NCE provider (and software) ãInitially, genotypes were unavailable ãFear of computational (and storage) demands Current Methods

9. Current Status BreedNo. AnimLD ImputeProviderMethod Angus160,000YGS, ZCORR Red Angus13,700YGS, ZBLEND Hereford15,780YGSBLEND-Ext. Simmental10.800YGSBLEND Limousin5,660YGSBLEND Gelbvieh3,880YGSBLEND Charolais2,136NGSCORR Santa Gertrudis 3,160NGSSS-GBLUP Brangus3,909YGS, ZSS-GBLUP

10. Correlated Trait u Similar to what has been done before, but now we include MBV MacNeil et al., 2010

11. u American Simmental Association attempted this u Logical philosophy u Relationship differences between target animals and training animals Variable Accuracy

12. u Post evaluation indexing of EPD and MBV u Benefits only genotyped animals u Simplicity u Fits within framework of NCE u Must “blend” correctly… “Blending”

13. Blending u Assumes base is set zero u If not, need to adjust both EBV and MBV for differences in base

14. Threshold Traits Are Different Kachman and Spangler, 2013

15. Future Status BreedNo. AnimLD ImputeProviderMethod Angus160,000YGS, ZSS Red Angus13,700YGS, ZSS Hereford15,780YGSSS Simmental10.800YGSSS Limousin5,660YGSSS Gelbvieh3,880YGSSS Charolais2,136NGSSS Santa Gertrudis 3,160NGSSS Brangus3,909YGS, ZSS

16. Single Step (s) u Two fundamental camps ãUGA, ssGBLUP—Misztal ãTheta Solutions/ISU, ss“Hybrid”— Fernando, Garrick, Golden u One difference is in the weighting of individual markers Lourenco et al.,2015 JAS Fernando et al., 2014, GSE

17. u Training sets represent a selected subset ãProblem erodes overtime with more genotyped animals and dynamic training When N > p ãProblems likely most notable for traits that are hallmarks of the breed Unaccounted for bias

18. u We have made tremendous progress u More work to do u Evolutions in: ãMethodology ãNCE platforms Cannot continue to make genomics “fit” Opportunity to rebuild instead of patch Continuous NCE Moving forward

19. u Collection of phenotypes ãRetraining efforts ãIncrease in accuracy above what genomics can do Understanding

20. u Training will occur on phenotypes (not deregressed EBV) u Expense of data collection could reasonably lead to highly selected training sets u Inference becomes challenging u Bias could become a real issue u Multiple trait models could help Moving Forward-novel traits

21. Summary u Phenotypes are still critical to collect u Methods for lower cost genotyping are evolving u Breeds must build training populations to capitalize u Genomic information has the potential to increase accuracy ãProportional to %GV ãImpacts inversely related to prior EPD accuracy