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Potential and Pitfalls for Genomic
            Selection
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
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
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
Potential and Pitfalls for Genomic Selection- Chad Dechow
Association of SNP with Fat Yield
Association of SNP with Final Score
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
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
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
Realized Reliabilities
80%

70%

60%

50%
                                                          Holstein
40%
                                                          Jersey
30%                                                       Brown Swiss
20%

10%

0%
      Milk yield   Daughter Preg Rate   Productive Life
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
$




          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
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
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
Beyond Sire Selection
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
Potential and Pitfalls for Genomic Selection- Chad Dechow
Can we Improve Her?

            23 gallons/day for a year
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
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
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?
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
NY-PA Replacement Rates
NY-PA Cull Rates
Maintaining Herd Size

• More replacements than needed
   Increase cull rate?
     • Fewer problem cows
     • Less “mature milk”


   Sell heifers?
     • Lower feed costs
     • Heifer market sustainable?
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
$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
$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
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
What to Sell

• Lots of heifers = limited marketing potential
   Save on feed costs
• Beef sires
   Male sexed semen
   Gaining traction
   Helpful with Jerseys
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?
Will Genomics Impact Inbreeding Rates?
Close Inbreeding
           (F=14.7%): Double
           Grandson of Aerostar

                           Aerostar
                Megabuck


Megastar

                           Aerostar
                   Digne
Chromosome 24




                              VanRaden, 2008
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
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?
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)
Marker Effects
Thank you and are there any questions?

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Potential and Pitfalls for Genomic Selection- Chad Dechow

  • 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
  • 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
  • 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)
  • 39. Thank you and are there any questions?