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Dairy Cattle Breeders Have Adopted Genomic Selection

Dairy Cattle Breeders Have Adopted Genomic Selection . How’s Your Genome?. Acknowledgments. Genotyping and DNA extraction: USDA Bovine Functional Genomics Lab, U. Missouri, U. Alberta, GeneSeek, Genetics & IVF Institute, Genetic Visions, and Illumina Computing:

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Dairy Cattle Breeders Have Adopted Genomic Selection

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  1. Dairy Cattle Breeders Have Adopted Genomic Selection

  2. How’s Your Genome?

  3. 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)

  4. CDDR Contributors • National Association of Animal Breeders (NAAB, Columbia, MO) • ABS Global (DeForest, WI) • Accelerated Genetics (Baraboo, WI) • Alta (Balzac, AB, Canada) • Genex (Shawano, WI) • New Generation Genetics (Fort Atkinson, WI) • Select Sires (Plain City, OH) • Semex Alliance (Guelph, ON, Canada) • Taurus-Service (Mehoopany, PA)

  5. Genomics Timeline

  6. SNP Edits and Counts

  7. Repeatability of Genotypes • 2 laboratories genotyped the same 46 bulls • About 1% missing genotypes per lab • Mean of 98% SNP same (37,624 out of 38,416) • Range across animals of 20 to 2,244 SNP missing • Mean of 99.997% SNP concordance (conflict <0.003%) • Mean of 0.9 errors per 38,416 SNP • Range across animals of 0 to 7 SNP conflicts

  8. Old Genetic Terms • Predicted transmitting ability and parent average • PTA required progeny or own records • PA included only parent data • Genomics blurs the distinction • Reliability = Corr2(predicted, true TA) • Reliability of PA could not exceed 50% because of Mendelian sampling • Genomics can predict the other 50% • Reliability limit at birth theoretically 99%

  9. New Genetic Terms • Genomic vs. pedigree relationships • Expected genes in common (A) • Actual genes in common (G) • Several formulas to compute G • Wright’s (1922) correlation matrix or Henderson’s (1976) covariance matrix • Genomic vs. pedigree inbreeding • Correlated by 0.68 • Daughter merit vs. son merit (X vs. Y)

  10. Differences in G and AG = genomic and A = pedigree relationships • Detected clones, identical twins, and duplicate samples • Detected incorrect DNA samples • Detected incorrect pedigrees • Identified correct source of DNA by genomic relationships with other animals

  11. Genomic Evaluation Methods • Use Henderson’s mixed model • Replace A by G • Proposed by Nejati-Javaremi, Smith, Gibson, 1997 J. Anim Sci. 75:1738 • Nonlinear regression, haplotyping or only slightly more accurate

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

  13. Phenotypes • 26 traits plus the Net Merit index • The 6,184 bulls genotyped have >10 million phenotyped daughters (average 2,000 daughters per bull) • Most traits recorded uniformly across the world • Foreign data provided by Interbull

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

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

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

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

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

  19. 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

  20. Marker Effects for Net Merit

  21. Significance Tests are Stupid

  22. Insignificant SNP Effects • Traditional selection on PA • 50 : 50 chance of better chromosome • 1 SNP with tiny effect • 50.01 : 49.99 chance • 38,416 SNPs with tiny effects • 70 : 30 chance • Only test overall sum of effects!

  23. X, Y, Pseudo-autosomal SNPs 35 SNPs 35 SNPs 0 SNPs 487 SNPs

  24. Net Merit by Chromosome for O-ManTop bull, +$778 Lifetime Net Merit

  25. Progeny Tested BullO-Man • Semen sales ~200,000 units / year • Semen price $40 / unit • Income > $5 million / year • 40,144 daughters already milking • 29,811 in United States • 1,963 in France, 1,895 in Denmark, 1,716 in Italy, 839 in Holland, etc.

  26. O-Man Daughters vs. Average Cows

  27. Genomic Tested BullsAvailable Jan 2009

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

  29. 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

  30. 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)

  31. Conclusions • High accuracy requires very many genotypes and phenotypes • Most traits are very quantitative (few major genes) • Genomic reliability > traditional • 30-40% with traditional parent average • 60-70% using 8,100 genotyped Holsteins • 81-83% from 40,000 simulated bulls

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