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

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Presentation transcript:

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 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

Increasing reliabilities Add more animals to the reference population

Deterministic prediction vs. Holstein data Number of bulls in reference population Accuracy of genomic breeding value Predicted Daetwyler et al. (2008) US Holstein data

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

Sequencing technology

Cost of sequencing a single base $ $

Holstein Key ancestors Year of Birth Relationship TO-MAR BLACKSTAR-ET ROUND OAK RAG APPLE ELEVATION PAWNEE FARM ARLINDA CHIEF MJR BLACKSTAR EMORY-ET WA-DEL RC MATT-ET KED JUROR-ET S-W-D VALIANT CAL-CLARK BOARD CHAIRMAN RICECREST EMERSON-ET Carol Prelude Mtoto ET WALKWAY CHIEF MARK MARGENE BLACKSTAR FRED HANOVERHILL STARBUCK

ATTCTGGGGGCCTTACTCCC ATTGTGGGGGCCATACGCCC ATTCTGGGGGCCTTACGCCC ATTGTGGGGGCCATACTCCC Imputing sequence

ATTCTGGGGGCCTTACTCCC ATTGTGGGGGCCATACGCCC ATTCTGGGGGCCTTACGCCC ATTGTGGGGGCCATACTCCC C T G G G T

Imputing sequence ATTCTGGGGGCCTTACTCCC ATTGTGGGGGCCATACGCCC ATTCTGGGGGCCTTACGCCC ATTGTGGGGGCCATACTCCC ATTCTGGGGGCCTTACTCCCATTGTGGGGGCCATACGCCC ATTGTGGGGGCCATACTCCC

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

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

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

25.2 million filtered variants 23.5 million SNP X 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

1000 Bull genomes project Better understanding effect of selection?

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

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

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

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

Trials conducted at Rutherglen

 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

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

Results: Accuracy of genomic predictions Trial Accuracy Trial Trial Trial Average 0.41±0.01

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

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

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

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)

Conclusions