1 / 40

Genomics for Emerging Markets

Genomics for Emerging Markets . Proven Bulls or Emerging Bulls?. Genetic Terms. Predicted transmitting ability and parent average PTA required progeny or own records PA included only parent data Genomics provides more information Reliability = Corr 2 (predicted, true TA)

lamont
Download Presentation

Genomics for Emerging Markets

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. Genomics for Emerging Markets

  2. Proven Bulls or Emerging Bulls?

  3. Genetic Terms • Predicted transmitting ability and parent average • PTA required progeny or own records • PA included only parent data • Genomics provides more information • 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%

  4. Reliability = 99% ? Not Yet !

  5. New Genetic Terms • Genomic vs. pedigree relationships • Expected genes in common (A) • Actual genes in common (G) • Several formulas to compute G • Genomic vs. pedigree inbreeding • Correlated by 0.68 in Holstein • Correlated by 0.80 in Angus • Daughter merit vs. son merit (X vs. Y)

  6. 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

  7. Genomic vs. PedigreeInbreeding Correlation = .68

  8. Genomic vs. Expected Future Inbreeding

  9. Phenotypic Data • 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

  10. Genomics Timeline

  11. 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

  12. Sequencing of Genomes

  13. Genomic Methods • Direct genomic evaluation • Sum of effects for 38,416 genetic markers • Not published • Combined genomic evaluation • Include phenotypes of non-genotyped ancestors by selection index • Transferred genomic evaluation • Propagate info from genotyped animals to non-genotyped relatives by selection index

  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. USA Evaluation • Genomic PTAs official in January • Traditional PTAs sent to Interbull • MACE used if foreign dtrs included • Genomic info used for most bulls • Genomic PTA transferred to descendants (to ancestors in future) • Jersey results also are official • More Brown Swiss needed (CHE)

  20. Genomic Tested BullsAvailable Jan 2009

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

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

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

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

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

  26. 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

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

  28. North American Cooperation • 174 markers, 1068 USA and CAN bulls • Illinois, Israel, and USDA researchers • 1991-1999 • 367 markers, 1415 USA and CAN bulls • USDA, Illinois, and Israel • 1995-2004 • 38,416 markers, 22,344 animals • USDA, Missouri, Canada, and Illumina • Oct 2007- Dec 2008

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

  30. 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)

  31. 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

  32. Cooperative International Projects • 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

  33. 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?

  34. 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

  35. 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)

  36. 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!

  37. 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)

  38. 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 much smaller for USA JER and BSW breeds • Trading, sharing, profit is needed

  39. 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)

  40. 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)

More Related