Benefits from Cooperation in Genomics

1. Benefits from Cooperation in Genomics

2. Topics • Genomic cooperation • Simulation of very large population • Proposals for genotype sharing • Country border issues and North American experience • Genomic MACE equations • USA update • Actual HOL, JER, and BSW results • Database and implementation

3. 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?

4. Sequencing of Genomes

5. Human DNA Data Sharing "The highest priority of the International Human Genome Sequencing Consortium is ensuring that sequencing data from the human genome is available to the world's scientists rapidly, freely and without restriction." National Human Genome Research Institute, 2008 "The principle of rapid pre-publication release should apply to other types of data from other large-scale production centers." Wellcome Trust, 2003

6. Simulation ResultsWorld Holstein Population • 40,360 older bulls to predict 9,850 younger 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

7. 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?

8. 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

9. 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

10. 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, 19,464 animals • USDA, Missouri, Canada, and Illumina • Oct 2007- Dec 2008

11. 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)

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

13. USA Update • 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)

14. 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

15. Genotyped Animals (n=19,464)As of December 2008

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

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

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

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

20. Genomic MACEGenomics Task Force, Pete Sullivan • 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

21. Conclusions • Reliability for young animals • 30-38% for traditional parent averages • 60-70% genomic REL for USA HOL traits • 81% using 40,360 simulated bulls • 83% using 100K instead of 50K markers • High reliability requires large numbers of genotyped animals • Gains much smaller for USA JER and BSW breeds • Trading, sharing, profit is needed • Revised MACE may include genomics

22. 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)

23. 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)