1 / 31

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:

malachi-hood
Download Presentation

Dairy Cattle Breeders Have Adopted Genomic Selection

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related