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Whats coming next in genomics? Ben Hayes, Department of Primary Industries, Victoria, Australia

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

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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

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Increasing reliabilities Add more animals to the reference population

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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

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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

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Sequencing technology

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Cost of sequencing a single base - 2000 $1 - 2011 $0.00000015

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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

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ATTCTGGGGGCCTTACTCCC ATTGTGGGGGCCATACGCCC ATTCTGGGGGCCTTACGCCC ATTGTGGGGGCCATACTCCC Imputing sequence

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ATTCTGGGGGCCTTACTCCC ATTGTGGGGGCCATACGCCC ATTCTGGGGGCCTTACGCCC ATTGTGGGGGCCATACTCCC C T G G G T

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Imputing sequence ATTCTGGGGGCCTTACTCCC ATTGTGGGGGCCATACGCCC ATTCTGGGGGCCTTACGCCC ATTGTGGGGGCCATACTCCC ATTCTGGGGGCCTTACTCCCATTGTGGGGGCCATACGCCC ATTGTGGGGGCCATACTCCC

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

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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

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

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25.2 million filtered variants 23.5 million SNP X 1000 Bull genomes project

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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

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1000 Bull genomes project Better understanding effect of selection?

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

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

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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

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

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Trials conducted at Rutherglen

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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 0.280.15 Results

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

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Results: Accuracy of genomic predictions Trial Accuracy Trial 1 0.40 Trial 2 0.42 Trial 3 0.40 Average 0.410.01

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

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

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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

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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 Universitt Mnchen, 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)

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Conclusions