Dr. Chad Dechow presented this information for DAIReXNET on Monday, January 14, 2013. For more information, please see our archived webinars page at www.extension.org/pages/15830/archived-dairy-cattle-webinars.

1. Potential and Pitfalls for Genomic
Selection

2. Topics
• Review of genomic technology and implementation
 4-path model
• Comparisons of early genomic predictions to actual
daughter proofs
 Traits to be careful
 Who should be using genomics, who not?
 Spread risk
• Genomics as a herd management tool
• Inbreeding
• Beyond SNPs

3. From Phenotype to Genotype:
diacylglycerol acyltransferase 1
• Enzyme involved in triglyceride synthesis
 Chromosome 14
 Knockout mice: complete absence of milk production
• Bi-nucleotide substitution: lysine to alanine
 +300 lbs milk
 +5 lbs protein
 +.17% fat
 -13 lbs fat
 Fatty acid profiles altered
• Terrific – but…
Grisart et al., 2002

4. Whole Genome Approach
• Single nucleotide polymorphisms
 10 – 50 million present in genome
 Not inherited independently of each other
• Tests
 Bovine SNP 50
• Cost: $125
 Low density
• 9,000 currently (replaces 6K, which replaced 3K)
• Used to “impute” 50K
• Cost: $45
 High density
• ~777,000
• Early research has not been exciting
• Cost: $250

6. Association of SNP with Fat Yield

7. Association of SNP with Final Score

8. Genetic Progress
• How does this speed genetic progress?
reliability * SelectionIntensity * GeneticVariance
G / Year
GenerationInterval
1.Lower generation interval Sire of Sire
2.Higher accuracy for females Sire
Dam of Sire
3.Selection Intensity
Calf
Sire of Dam
Dam
Dam of Dam

9. Implementation
• First official proofs in January of 2009
• Quickly adopted
Young sire matings
 Sires of sons – vast majority
50
• Marketing differs by 40
bull stud
Percent
30
 Mixed lineup 20
 separate lineups 10
0
2008 2011
Holstein Jersey

10. Comparison of Jan 2009 to Dec 2012
Daughters Deviations
517 bulls
0 daughters in 2009 and ≥100 daughters currently
Milk Yield Productive Life R² = 0.340
R² = 0.546
2012 Dau Yield Deviation
2012 Dau Deviation
2009 PTAM 2009 PTAPL

11. Realized Reliabilities
80%
70%
60%
50%
Holstein
40%
Jersey
30% Brown Swiss
20%
10%
0%
Milk yield Daughter Preg Rate Productive Life

12. Top 25 Young Sires and Proven Bulls in
2009
900
800
700
600
500
Genomic YS
400
Proven
300
200
100
0
Average 2009 Average 2012 Top 2012

13. $
100
200
300
500
700
800
900
1000
0
400
600
Aug-08
Nov-08
Feb-09
May-09
Aug-09
Nov-09
Feb-10
May-10
Aug-10
Nov-10
Feb-11
May-11
Aug-11
Nov-11
Net Merit Changes
Feb-12
May-12
Aug-12
Freddie
Cassino
Sholton
Atwood

14. Traits to watch
• Productive Life Productive Life Genetic
 Must wait for cows to die Correlations
 Predictors to help 0.8
• Calving related traits 0.6
0.4
0.2
0
-0.2
-0.4
-0.6
Body Udder Feet & DPR SCS
Size Legs
Previous Current

15. Who Should Use Genomic Young Sires?
Use Do not use
• Involved with marketing • Not marketing
 Will have hits and misses • You want to minimize
 Goes with the territory calving issues
• Not marketing • Willing to miss out on the
 Watching calving traits on best for 3 years
virgin heifers
 Average may not be
 Spreading risk by using a different, but top will be
selection lower
 Willing to accept some
misses

16. Beyond Sire Selection

17. DNA Level Mating Decisions
• Replacement for visual
appraisal mating programs?
• Chromosome level mating
 http://aipl.arsusda.gov/CF-
queries/Bull_Chromosomal_EBV/bull_chromosomal_ebv.cfm
 Use 17 digit ID style (HOUSA000000000000)
 Cows entered on same page as bulls

19. Can we Improve Her?
23 gallons/day for a year

20. Haplotype Projections: Milk
90000
80000
Selection Limit Milk (lbs)
70000
60000
50000
40000
30000
20000
10000
0
Brown Swiss Holstein Jersey
Largest DGV Lower Bound Upper Bound
Cole et al., 2011

21. Haplotype Projections: DPR
160
140
120
Selection Limit DPR
100
80
60
40
20
0
Brown Swiss Holstein Jersey
Largest DGV Lower Bound Upper Bound
Cole et al., 2011

22. Opportunity 2013
• Only bull studs can genotype males
 6 Studs
• Contributed $ and DNA
 License agreement
• Newer chips detect Y chromosome genes
• Agreement ends in 2013
• If you have a good bull, do you sell him?
 Market your own bull?
 What will it cost?

23. Genomics as a Herd Management Tool
• Premise: Genomics can play a role for
commercial milk producers with excess
heifers
• Helpful link
http://edis.ifas.ufl.edu/pdffiles/AN/AN27000.pdf

24. NY-PA Replacement Rates

25. NY-PA Cull Rates

26. Maintaining Herd Size
• More replacements than needed
 Increase cull rate?
• Fewer problem cows
• Less “mature milk”
 Sell heifers?
• Lower feed costs
• Heifer market sustainable?

27. Selling Heifers
• Value of testing
• Herd improvement by culling the bottom end
 70%, 80%, or 90% of calves kept
 What happens to the value of my remaining
calves if I genomically test first?
 What is the $ Net Present Value of testing?
**First culling threshold: sick/diseased calves

28. $Net Merit of Remaining Calves
250
200
$ Net Merit
150
100
50
0
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
% of calves tested
90% kept 80% kept 70% kept

29. $Value = $NM – Test Cost
140
120
100
$ Net Merit
80
60
40
20
0
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
% Tested
90% Kept 80% Kept 70% Kept

30. Net Present Value
• We don’t need to test every calf
 Top sires will rarely have offspring you want to
cull
• Net Present Value compared with parent
average selection

31. What to Sell
• Lots of heifers = limited marketing potential
 Save on feed costs
• Beef sires
 Male sexed semen
 Gaining traction
 Helpful with Jerseys

32. Individualized Cow Management?
• Should we alter management to
accommodate genetic potential?
 High dairy form = high early lactation BCS loss
risk
• Calving BCS should be LOW
 Lower yield potential
• Breed back more quickly?
• Group cows by genetic potential?

33. Will Genomics Impact Inbreeding Rates?

34. Close Inbreeding
(F=14.7%): Double
Grandson of Aerostar
Aerostar
Megabuck
Megastar
Aerostar
Digne
Chromosome 24
VanRaden, 2008

35. Inbreeding
• Likely to accelerate with genomics
 Shorter generation interval
 Technology is “pattern recognition”
• Unusual genetic make-up = unrecognized pattern
• Line development
Aerostar
Megabuck
Identical by descent
= inbred
Megastar
Aerostar
Digne
Chromosome 24

36. If we know the DNA code
• Why are genomic tests 100% accurate?
 Markers are random & may have nothing to do
with performance themselves
 Copy number variation
 Not accounting for dominance/gene interactions
 “Epigenetic” effects
• Alter gene expression independently of DNA code
• High milk yield during gestation = lower milk yield
daughter?

37. The more we learn, the less we know
• Intelligent design cannot explain the presence of a
nonfunctional pseudogene … the designer made serious
errors, wasting millions of bases of DNA … junk …
Evolution, however, can explain them easily … they persist in
the genome as evolutionary remnants of the past history
(Miller, 1994)

38. Marker Effects

39. Thank you and are there any questions?