1. Matt Spangler Beef Genetics Specialist University of Nebraska-Lincoln

2. Few markers and seemingly easy to interpret results Stars—Not all stars are equal The number of markers has grown Evolution of 50k Results have changed Molecular Breeding Values (MBVs) Some have a metric of accuracy MBV is not equal to an EPD Current accuracy metrics not equal to BIF accuracy

3. Advertising for seedstock X star bull for trait y 10 bull for tenderness Selection decisions Limited--Confusion

4. Results were not appropriate for educated selection decisions Not included in NCE Disjoined sources of information Good test result and poor EPD

5. Bull on the left has the most desirable genetic test value Bull on the left has the most desirable genetic test value Bull on the right has the least desirable genetic test value but favorable EPD Bull on the right has the least desirable genetic test value but favorable EPD Bull on the left could be undesirable for an infinite number of other markers while the bull on the right could be desirable for an infinite number of unknown markers Bull on the left could be undesirable for an infinite number of other markers while the bull on the right could be desirable for an infinite number of unknown markers

6. Marker panels have grown Format of results has changed MBVs Inclusion in NCE is happening Angus Novel traits are being collected Health Healthfulness

7. Multiple companies Multiple panels Differing sizes Differing traits 50k is getting a big brother Minimal inclusion into NCE

8. Who to test and with what panel Should I test Ultrasound vs maker panel Information flow—How Where will training of panels occur US MARC must play a key role Enough cattle?

9. Economically there is benefit to only testing some animals WGS not currently feasible for wide-spread use Reduced panels more logical for non-AI sires Most influential? i.e. large diagonal element of A -1 Novel search algorithms to eliminate testing of multiple sibs Those who might be influential in the future? Prior knowledge of parents diagonal element and other metrics

11. Closer the target population is to the training population the better they will work If robust diagnostics are the goal then non-informative SNPs are a concern (IBD) i.e. using an Angus panel to predict in Charolais

12. Fitting a single causative mutation Fitting Marker Scores and Genomic Relationships Marker scores represent additional phenotypes correlated to the trait of interest Genomic relationships represent realized relationship as opposed to expected relationships

13. Increased accuracy of prediction Earlier selection Increased array of traits

14. Why the low correlations (accuracy) for yearling bulls? Uncertainty surrounding what alleles were received from parents We begin to understand this when an animal has a record Becomes more clear as we see what it is passing on to its offspring Commercial producers do not have this luxury

15. Pedigree estimate Pedigree estimate + individual record (if available) Full pedigree + individual record (if available) Full pedigree + individual record (if available) + progeny records Take Home: Collection can take a long time. Records of relatives benefit an individual.

16. Pedigree estimate+ Molecular Score More information earlier=higher accuracy earlier Insight into Mendelian sampling Take Home: Information is available earlier.

17. Rate of genetic change is determined by: Accuracy, genetic variation, selection intensity, and length of the generation interval Generation interval is approximately 6 years More young sires as parents?

18. Two yearling bulls with a +40 weaning weight EPD One low accuracy and one high accuracy Are they really the same?

19. Difference in EPD Accuracy Acc = 0.30, Possible Change = 8.1 Acc = 0.8, Possible Change = 2.3

20. Two yearling bulls with a +5 CED EPD with accuracy of 0.2. Possible change of 6 With the addition of more information their EPDs change One favorably and the other unfavorably More information earlier allows you to choose animals more accurately

21. Bull ABull B +5+5 Add molecular scores as additional information Bull ABull B -1+11 In this extreme case risk was 10% more calving difficulties Average is still +5*

22. Lowly heritable Hard or expensive to measure Measured late in life Sex specific

23. Producers (seedstock and commercial) Researchers (university and USDA) DNA companies Breed Associations Outreach

24. “Marker Panel”  “Correlated Trait” Value of the molecular score depends on: Validation results-Yes or No Proportion of variation explained Benefit in accuracy is increased as the proportion of variation explained increases Cost of technology must pay for itself

25. Inclusion of DNA information has the potential to: Mitigate risk Decrease generation interval Benefits will only be realized through cooperation Resources need to be pooled Collaboration is critical