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Potential and Pitfalls for Genomic Selection- Chad Dechow

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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.

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Potential and Pitfalls for Genomic Selection- Chad Dechow

  1. 1. Potential and Pitfalls for Genomic Selection
  2. 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. 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. 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
  5. 5. Association of SNP with Fat Yield
  6. 6. Association of SNP with Final Score
  7. 7. Genetic Progress• How does this speed genetic progress? reliability * SelectionIntensity * GeneticVariance G / Year GenerationInterval1.Lower generation interval Sire of Sire2.Higher accuracy for females Sire Dam of Sire3.Selection Intensity Calf Sire of Dam Dam Dam of Dam
  8. 8. 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
  9. 9. Comparison of Jan 2009 to Dec 2012 Daughters Deviations517 bulls0 daughters in 2009 and ≥100 daughters currentlyMilk Yield Productive Life R² = 0.340 R² = 0.5462012 Dau Yield Deviation 2012 Dau Deviation 2009 PTAM 2009 PTAPL
  10. 10. Realized Reliabilities80%70%60%50% Holstein40% Jersey30% Brown Swiss20%10%0% Milk yield Daughter Preg Rate Productive Life
  11. 11. Top 25 Young Sires and Proven Bulls in 2009900800700600500 Genomic YS400 Proven300200100 0 Average 2009 Average 2012 Top 2012
  12. 12. $ 100 200 300 500 700 800 900 1000 0 400 600Aug-08Nov-08Feb-09May-09Aug-09Nov-09Feb-10May-10Aug-10Nov-10Feb-11May-11Aug-11Nov-11 Net Merit ChangesFeb-12May-12Aug-12 Freddie Cassino Sholton Atwood
  13. 13. 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
  14. 14. 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
  15. 15. Beyond Sire Selection
  16. 16. 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
  17. 17. Can we Improve Her? 23 gallons/day for a year
  18. 18. Haplotype Projections: Milk 90000 80000Selection 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
  19. 19. Haplotype Projections: DPR 160 140 120Selection Limit DPR 100 80 60 40 20 0 Brown Swiss Holstein Jersey Largest DGV Lower Bound Upper Bound Cole et al., 2011
  20. 20. 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?
  21. 21. 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
  22. 22. NY-PA Replacement Rates
  23. 23. NY-PA Cull Rates
  24. 24. Maintaining Herd Size• More replacements than needed  Increase cull rate? • Fewer problem cows • Less “mature milk”  Sell heifers? • Lower feed costs • Heifer market sustainable?
  25. 25. 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
  26. 26. $Net Merit of Remaining Calves 250 200$ 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
  27. 27. $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
  28. 28. 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
  29. 29. What to Sell• Lots of heifers = limited marketing potential  Save on feed costs• Beef sires  Male sexed semen  Gaining traction  Helpful with Jerseys
  30. 30. 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?
  31. 31. Will Genomics Impact Inbreeding Rates?
  32. 32. Close Inbreeding (F=14.7%): Double Grandson of Aerostar Aerostar MegabuckMegastar Aerostar DigneChromosome 24 VanRaden, 2008
  33. 33. 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
  34. 34. 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?
  35. 35. 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)
  36. 36. Marker Effects
  37. 37. Thank you and are there any questions?

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