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