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 Genotypes Phenotypes Prediction equation Genomic Breeding Value = w 1 x 1 +w 2 x 2 +w 3 x 3 …… Selection candidates Genotypes Selected Breeders Estimated breeding values

4. Increasing reliabilities Add more animals to the reference population

5. Deterministic prediction vs. Holstein data 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 01000200030004000500060007000 Number of bulls in reference population Accuracy of genomic breeding value Predicted Daetwyler et al. (2008) US 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. Cost of sequencing a single base - 2000 $1 - 2011 $0.00000015

9. Holstein Key ancestors Year of Birth Relationship TO-MAR BLACKSTAR-ET19837.9 ROUND OAK RAG APPLE ELEVATION19657.6 PAWNEE FARM ARLINDA CHIEF19627.2 MJR BLACKSTAR EMORY-ET19897.1 WA-DEL RC MATT-ET19897.0 KED JUROR-ET19907.0 S-W-D VALIANT19736.8 CAL-CLARK BOARD CHAIRMAN19766.8 RICECREST EMERSON-ET19946.8 Carol Prelude Mtoto ET19936.7 WALKWAY CHIEF MARK19786.7 MARGENE BLACKSTAR FRED19916.7 HANOVERHILL STARBUCK19796.6

10. ATTCTGGGGGCCTTACTCCC ATTGTGGGGGCCATACGCCC ATTCTGGGGGCCTTACGCCC ATTGTGGGGGCCATACTCCC Imputing sequence

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

12. Imputing sequence ATTCTGGGGGCCTTACTCCC ATTGTGGGGGCCATACGCCC ATTCTGGGGGCCTTACGCCC ATTGTGGGGGCCATACTCCC ATTCTGGGGGCCTTACTCCCATTGTGGGGGCCATACGCCC 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. 25.2 million filtered variants 23.5 million SNP X 1000 Bull genomes project

17. 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 Genotypes Phenotypes Prediction equation Genomic Breeding Value = w 1 x 1 +w 2 x 2 +w 3 x 3 …… Selection candidates Genotypes 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.  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 Results

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

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

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

32. 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

33. 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)

34. Conclusions