What s coming next in genomics
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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


What s coming next in genomics

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 technology

Sequencing technology


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-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


What s coming next in genomics

Imputing sequence

ATTCTGGGGGCCTTACTCCC

ATTGTGGGGGCCATACGCCC

ATTCTGGGGGCCTTACGCCC

ATTGTGGGGGCCATACTCCC


What s coming next in genomics

Imputing sequence

ATTCTGGGGGCCTTACTCCC

ATTGTGGGGGCCATACGCCC

ATTCTGGGGGCCTTACGCCC

ATTGTGGGGGCCATACTCCC

C T

G G

G T


What s coming next in genomics

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


What s coming next in genomics

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


    What s coming next in genomics

    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


    Trials conducted at rutherglen

    Trials conducted at Rutherglen


    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)


  • Conclusions

    Conclusions


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