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Use of Genomics for Selecting Sires

Use of Genomics for Selecting Sires. Genomic Methods. Direct genomic evaluation Sum of effects for 38,416 genetic markers Not published Combined genomic evaluation Include phenotypes of non-genotyped ancestors Selection index includes 3 PTAs per animal

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Use of Genomics for Selecting Sires

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  1. Use of Genomics for Selecting Sires

  2. Genomic Methods • Direct genomic evaluation • Sum of effects for 38,416 genetic markers • Not published • Combined genomic evaluation • Include phenotypes of non-genotyped ancestors • Selection index includes 3 PTAs per animal • Traditional, direct genomic, and subset PTA • Transferred genomic evaluation • Propagate from genotyped animals to non-genotyped descendants by selection index • Propagation to ancestors being developed

  3. January Evaluation • HO, JE genomic PTAs official in Jan. • Genomic from Dec 1, domestic Dec 18 • Traditional PTAs sent to Interbull • MACE used if foreign daughters included • Genomic PTA used for most bulls (80%) • Traditional used if many new daughters • Genomic PTA transferred to descendants (to ancestors in future) • Brown Swiss not yet official

  4. February Evaluation • Official only for new genotypes • Animals genotyped during Dec and Jan • Active bulls not updated officially • Unofficial PTAs provided in March for proven bulls • March evaluation • Added 96 bulls accidentally left out of Feb • Tested fast reliability approximation • Brown Swiss now have >800 genotyped • Traded with Switzerland in March 2009

  5. April Evaluation (Plans) • Genomic PTAs all official • Compute domestic, then genomic • Redo last step after MACE arrives: • Selection index recalculation • Replace previous with current MACE • SNP effects and subset PTA same • Similar to young bull calving ease • Suggested by CDN researchers

  6. August Evaluation (Plans) • Interbull converts genomic PTAs • Young bulls only • Proven bulls next year (2010) • AIPL must compute domestic and genomic earlier to meet deadline • Decrease yield heritability to make PAs and cow PTAs less biased

  7. Genomic Tested BullsAvailable Jan 2009

  8. Net Merit of Top 20 Bulls from 2009 data based on selection in 2004

  9. Changes in Net Merit means for top 20 bulls (2009 – 2004)

  10. Average regressions across all traits Predict 2009 from 2004 data, expected = 1.00

  11. Net Merit regressions Predict 2009 from 2004 data, expected = 1.00

  12. Genomic vs. traditional – protein PTA

  13. Genomic vs. traditional – net merit

  14. Genomic vs. trad. – protein reliability

  15. Genomic vs. trad. – net merit reliability

  16. Expected Change in Net Merit • SD = 163 * √(RELG – RELT ) • = $91 for young bulls (.66 - .35) • = $33 for proven bulls (.88 - .84) • Daughter equivalents for NM$ • 10 from parent average • 25 from genomics • 35 total for young animals

  17. Net Merit by ChromosomeFreddie - highest Net Merit bull

  18. Net Merit by ChromosomeO Man – Sire of Freddie

  19. Net Merit by ChromosomeDie-Hard - maternal grandsire

  20. Net Merit by ChromosomePlanet – high Net Merit bull

  21. Adoption of Genomic TestingUS young bulls purchased by AI companies * 2007-2008 counts are incomplete

  22. Genetic Progress • Assume 60% REL for net merit • Sires mostly 1-3 instead of 6 years old • Dams of sons mostly heifers with 60% REL instead of cows with phenotype and genotype (66% REL) • Progress could increase by >50% • 0.37 vs. 0.23 genetic SD per year • Reduce generation interval more than accuracy

  23. Worldwide Dairy Genotypingas of January 2009 1Using a customized Illumina 50K chip (different markers)

  24. Foreign DNA in North American DataProven bulls, Young bulls, and Females

  25. Country Borders • Most phenotypic data collected and stored within country • Genomic data allows simple, accurate prediction across borders • Need traditional EBV or PA for foreign animals, but not available for young bulls, cows, or heifers • May need full foreign pedigrees • Genomic evaluations official on USA scale for many foreign animals (not just CAN)

  26. Simulation ResultsWorld Holstein Population • 40,360 older bulls to predict 9,850younger bulls in Interbull file • 50,000 or 100,000 SNP; 5,000 QTL • Reliability vs. parent average REL • Genomic REL = corr2 (EBV, true BV) • 81% vs 30% observed using 50K • 83% vs 30% observed using 100K

  27. International Evaluation • Traditional genetic evaluations • MACE instead of merging phenotypes • Small benefits expected from data merger • Proven bulls only, not cows or young bulls • Parentage testing, genetic recessives, pedigrees done by breed associations • Genomics: what role for Interbull? • Benefits of sharing genotypes are large

  28. Genotype Exchange Options • Give away for free (not likely) • Genotype own bulls, then trade? • Trade an equal number or all bulls? • Country A has 5000 and B has 1000 • Proportional to population size? • Trade among organization pairs or create central genomic database? • Service fee for young animals to pay for ancestor genotyping?

  29. Share Young Bull, Cow Genotypes? • May be marketed in >1 country • Exchange of young animals and females more important as their REL increases with genomics • Helps to synchronize databases • Could lead to joint evaluation

  30. Problems of Not Sharing • Genetic progress not as fast as with full access to genotypes • Severe limits on researcher access to genotypes (secrecy) • Genomics may lead to natural monopoly, similar to railroads • Small companies / countries can’t afford to buy sufficient genotypes

  31. Genomic MACEInterbull Genomics Task Force • Residuals correlated across countries • Repeated tests of the same major gene, or • SNP effects estimated from common bulls • Let cij = proportion of common bulls • Let gi = DEgen / (DEdau + DEgen) • Corr(ei, ej) = cij * Corr(ai, aj) * √(gi * gj) • Avoids double counting genomic information from multiple countries i, j • New deregression formulas needed

  32. Low Density SNP Chip • Choose 384 marker subset • SNP that best predict net merit • Parentage markers to be shared • Use for initial screening of cows • 40% benefit of full set for 10% cost • Could get larger benefits using haplotyping (Habier et al., 2008)

  33. Marker Effects for Net Merit

  34. Genomic vs. Expected Future Inbreeding

  35. Genomic vs. PedigreeInbreeding Correlation = .68

  36. Pedigree Relationship Matrix1HO9167 O-Style

  37. Genomic Relationship Matrix 1HO9167 O-Style

  38. Difference (Genomic – Pedigree) 1HO9167 O-Style

  39. Genotyped Animals (n=22,344)In North America as of February 2009

  40. Experimental Design - UpdateHolstein, Jersey, and Brown Swiss breeds Data from 2004 used to predict independent data from 2009

  41. Reliability Gain1 by BreedYield traits and NM$ of young bulls 1Gain above parent average reliability ~35%

  42. Reliability Gain by BreedHealth and type traits of young bulls

  43. Value of Genotyping More AnimalsActual and predicted gains for 27 traits and for Net Merit Cows: 947 1916

  44. Conclusions - 1 • High accuracy requires very many genotypes and phenotypes • 100X more markers allows MAS across rather than within families • 5X more bulls allows estimation of much smaller QTL effects (HO) • Most traits are very quantitative (few major genes)

  45. Conclusions - 2 • Reliability increases by tracing actual genes inherited instead of expected average from parents • Genomic reliability > traditional • 30-40% with traditional parent average • 60-70% using 8,100 genotyped Holsteins • 81-83% from 40,000 simulated bulls • Gains smaller for USA JER and BSW breeds • Trading, sharing, profit is needed

  46. Acknowledgments • Genotyping and DNA extraction: • USDA Bovine Functional Genomics Lab, U. Missouri, U. Alberta, GeneSeek, Genetics & IVF Institute, Genetic Visions, and Illumina • Computing: • AIPL staff (Mel Tooker, Leigh Walton, Jay Megonigal) • Funding: • National Research Initiative grants • 2006-35205-16888, 2006-35205-16701 • Agriculture Research Service • Holstein and Jersey breed associations • Contributors to Cooperative Dairy DNA Repository (CDDR)

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