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What’s coming next in genomics?

What’s coming next in genomics?. Ben Hayes, Department of Primary Industries, Victoria, Australia. Outline. SNP chips to whole genome sequencing The 1000 bull genomes project New traits -> feed conversion efficiency The other 96% -> rumen micro-biomes. Reference Population.

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What’s coming next in genomics?

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  1. What’s coming next in genomics? Ben Hayes, Department of Primary Industries, Victoria, Australia

  2. Outline • SNP chips to whole genome sequencing • The 1000 bull genomes project • New traits -> feed conversion efficiency • The other 96% -> rumen micro-biomes

  3. Reference Population Selection candidates Genotypes Phenotypes Genotypes Prediction equation Genomic Breeding Value = w1x1+w2x2+w3x3…… Selected Breeders Estimated breeding values

  4. Increasing reliabilities • Add more animals to the reference population

  5. 1 0.9 0.8 0.7 0.6 Accuracy of genomic breeding value 0.5 0.4 0.3 0.2 Predicted Daetwyler et al. (2008) US Holstein data 0.1 0 0 1000 2000 3000 4000 5000 6000 7000 Number of bulls in reference population Deterministic prediction vs. Holstein data

  6. Increasing reliabilities • Better DNA markers? • Maximum reliability -> proportion genetic variance explained by DNA markers • For 50K SNP chip, 60% for fertility, 90% for milk production

  7. Sequencing technology

  8. Sequencing technology Cost of sequencing a single base - 2000 $1 - 2011 $0.00000015

  9. Holstein Key ancestors Year of Birth Relationship TO-MAR BLACKSTAR-ET 1983 7.9 ROUND OAK RAG APPLE ELEVATION 1965 7.6 PAWNEE FARM ARLINDA CHIEF 1962 7.2 MJR BLACKSTAR EMORY-ET 1989 7.1 WA-DEL RC MATT-ET 1989 7.0 KED JUROR-ET 1990 7.0 S-W-D VALIANT 1973 6.8 CAL-CLARK BOARD CHAIRMAN 1976 6.8 RICECREST EMERSON-ET 1994 6.8 Carol Prelude Mtoto ET 1993 6.7 WALKWAY CHIEF MARK 1978 6.7 MARGENE BLACKSTAR FRED 1991 6.7 HANOVERHILL STARBUCK 1979 6.6

  10. Imputing sequence ATTCTGGGGGCCTTACTCCC ATTGTGGGGGCCATACGCCC ATTCTGGGGGCCTTACGCCC ATTGTGGGGGCCATACTCCC

  11. Imputing sequence ATTCTGGGGGCCTTACTCCC ATTGTGGGGGCCATACGCCC ATTCTGGGGGCCTTACGCCC ATTGTGGGGGCCATACTCCC C T G G G T

  12. Imputing sequence ATTCTGGGGGCCTTACTCCC ATTGTGGGGGCCATACGCCC ATTCTGGGGGCCTTACGCCC ATTGTGGGGGCCATACTCCC ATTCTGGGGGCCTTACTCCC ATTGTGGGGGCCATACGCCC ATTGTGGGGGCCATACTCCC

  13. Outline • SNP chips to whole genome sequencing • The 1000 bull genomes project • New traits -> feed conversion efficiency • The other 96% -> rumen micro-biomes

  14. 1000 Bull genomes project • Provide a database of genotypes from sequenced key ancestor bulls • Global effort! – groups sequencing can get involved • Receive genotypes for all individuals sequenced

  15. 1000 Bull genomes project • 236 Bulls and 2 cows sequenced • 130 Holsteins, 48 Angus, 15 Jerseys, 42 Fleckvieh

  16. 1000 Bull genomes project • 25.2 million filtered variants • 23.5 million SNP X

  17. 1000 Bull genomes project • DNA variants affecting traits in data • Higher reliability genomic breeding values -> 100% genetic variance explained • small effect production, larger fertility? • Better reliability of genomic breeding values across generations • Genomic sires as sire of sons, JIVET, etc

  18. 1000 Bull genomes project • Better understanding effect of selection?

  19. Outline • SNP chips to whole genome sequencing • The 1000 bull genomes project • New traits -> feed conversion efficiency • The other 96% -> rumen micro-biomes

  20. Selection in Australian dairy cattle • Current selection index does not capture variation in maintenance requirements

  21. Reference Population Selection candidates Genotypes Phenotypes Genotypes Prediction equation Genomic Breeding Value = w1x1+w2x2+w3x3…… Selected Breeders Estimated breeding values

  22. Collaboration with NZ • 2000 heifers too expensive to measure • Collaboration Livestock Improvement Corporation and Dairy NZ • 1000 heifers each

  23. Trials conducted at Rutherglen

  24. Results • Difference between most efficient and least efficient 10% of heifers 1.5kg intake/day for same growth • But selection only on genetic component • Heritability was 0.28±0.15

  25. Genomic predictions • DNA from all heifers, genotyped for 800,000 markers

  26. Trial Accuracy Trial 1 0.40 Trial 2 0.42 Trial 3 0.40 Average 0.41 ± 0.01 Results: Accuracy of genomic predictions

  27. Feed conversion efficiency • Major international effort to increase reference • Led by Roel Veerkamp, (University of Wageningen) • Reliable genomic breeding values for feed efficiency

  28. Outline • SNP chips to whole genome sequencing • The 1000 bull genomes project • New traits -> feed conversion efficiency • The other 96% -> rumen micro-biomes

  29. Conclusion • Whole genome sequence data • improved reliabilities of genomic breeding values (esp fertility?) • better persistence across generations? • Genomic breeding values for new traits • feed conversion efficiency • Rumen micro-biome profiles to predict phenotypes? • Feed conversion efficiency • Methane emissions levels

  30. With thanks • Workers • Hans Daetwyler, Jennie Pryce, Elizabeth Ross • Partners/Funders • Dairy Futures CRC, Gardiner Foundation, Holstein Australia • Steering committee 1000 bull genomes • Ruedi Fries (Technische Universität München, Germany) • Mogens Lund/Bernt Guldbrandtsent (Aarhus University, Denmark) • Didier Boichard (INRA, France) • Paul Stothard (University of Alberta, Canada) • Roel Veerkamp (Wageningen UR, Netherlands) • Ben Hayes/Mike Goddard (DPI) • Curt Van Tassell (United States Department of Agriculture)

  31. Conclusions

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