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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

What’s coming next in genomics?

Ben Hayes, Department of Primary Industries, Victoria, Australia


Outline
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

Selection candidates

Genotypes

Phenotypes

Genotypes

Prediction equation

Genomic Breeding Value = w1x1+w2x2+w3x3……

Selected Breeders

Estimated breeding values


Increasing reliabilities
Increasing reliabilities

  • Add more animals to the reference population


Deterministic prediction vs holstein data

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


Increasing reliabilities1
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 technology1
Sequencing technology

Cost of sequencing a single base

- 2000 $1

- 2011 $0.00000015


Holstein key ancestors
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


Imputing sequence

ATTCTGGGGGCCTTACTCCC

ATTGTGGGGGCCATACGCCC

ATTCTGGGGGCCTTACGCCC

ATTGTGGGGGCCATACTCCC


Imputing sequence

ATTCTGGGGGCCTTACTCCC

ATTGTGGGGGCCATACGCCC

ATTCTGGGGGCCTTACGCCC

ATTGTGGGGGCCATACTCCC

C T

G G

G T


Imputing sequence

ATTCTGGGGGCCTTACTCCC

ATTGTGGGGGCCATACGCCC

ATTCTGGGGGCCTTACGCCC

ATTGTGGGGGCCATACTCCC

ATTCTGGGGGCCTTACTCCC

ATTGTGGGGGCCATACGCCC

ATTGTGGGGGCCATACTCCC


Outline1
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
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 project1
1000 Bull genomes project

  • 236 Bulls and 2 cows sequenced

  • 130 Holsteins, 48 Angus, 15 Jerseys, 42 Fleckvieh


1000 Bull genomes project

  • 25.2 million filtered variants

  • 23.5 million SNP

X


1000 bull genomes project2
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 project3
    1000 Bull genomes project

    • Better understanding effect of selection?


    Outline2
    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
    Selection in Australian dairy cattle

    • Current selection index does not capture variation in maintenance requirements


    Reference Population

    Selection candidates

    Genotypes

    Phenotypes

    Genotypes

    Prediction equation

    Genomic Breeding Value = w1x1+w2x2+w3x3……

    Selected Breeders

    Estimated breeding values


    Collaboration with nz
    Collaboration with NZ

    • 2000 heifers too expensive to measure

    • Collaboration Livestock Improvement Corporation and Dairy NZ

    • 1000 heifers each



    Results
    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


    Genomic predictions
    Genomic predictions

    • DNA from all heifers, genotyped for 800,000 markers


    Results accuracy of genomic predictions

    Trial

    Accuracy

    Trial 1

    0.40

    Trial 2

    0.42

    Trial 3

    0.40

    Average

    0.41

    ±

    0.01

    Results: Accuracy of genomic predictions


    Feed conversion efficiency
    Feed conversion efficiency

    • Major international effort to increase reference

    • Led by Roel Veerkamp, (University of Wageningen)

    • Reliable genomic breeding values for feed efficiency


    Outline3
    Outline

    • SNP chips to whole genome sequencing

    • The 1000 bull genomes project

    • New traits -> feed conversion efficiency

    • The other 96% -> rumen micro-biomes


    Conclusion
    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
    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)



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