International Dairy Sire Proofs

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Genetic trends in dairy cattle over the next 25
years where are we headed and how will we
get there


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National Dairy Genetic Evaluation Program
PDCA
DHI
NAAB
AIPL
CDCB
Universities
AIPL Animal Improvement Programs Lab.,
USDA CDCB Council on Dairy Cattle
Breeding DHI Dairy Herd Improvement (milk
recording organizations) NAAB National
Association of Animal Breeders (AI) PDCA Purebred
Dairy Cattle Association (breed registries)
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DHI statistics (2007)

• 4.4 million cows
• 98 fat recorded
• 95 protein recorded
• 94 somatic cell count recorded
• 23,500 herds
• 184 cows per herd
• 23,560 pounds milk per cow
• 3.69 fat
• 3.09 (true) protein

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Traits evaluated

• Yield (milk, fat, protein volume component
  percentages)
• Type/conformation
• Productive life/longevity
• Somatic cell score (SCS)/mastitis resistance
• Fertility
• Daughter pregnancy rate (DPR cow)
• Estimated relative conception rate (bull)
• Calving ease/dystocia (service sire, daughter)

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Evaluation methods

• Animal model (linear) Heritability
• Yield (milk, fat, protein) 2540
• Type (Ayrshire, Brown Swiss, 754
• Guernsey, Jersey)
• Productive life 8.5
• SCS 12
• DPR 4
• Sire-maternal grandsire model (threshold)
• Service sire calving ease 8.6
• Daughter calving ease 3.6

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Dairy cattle breeding

• Long generation interval 5 years
• High value of individuals
• 2,000 per cow
• Intensive management
• milking 23 times per day
• Bull semen suitable for dilution
• 500 doses per collection day)

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U.S. progeny-test bulls (2006)

• Major and marketing-only AI organizations plus
  breeder proven
• Breeds
• Ayrshire 13
• Brown Swiss 30
• Guernsey 12
• Holstein 1,493
• Jersey 151
• Milking Shorthorn 8
• 260 new bulls returned to service per year

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Genetic-economic indexes
Trait Relative value () Relative value () Relative value ()
Trait Cheese merit Net merit Fluid merit
Protein (lb) 36 33 9
Fat (lb) 18 22 22
Milk (lb) 10 0 24
Productive life (mo) 9 11 11
SCS (log base 2) 7 9 9
Udder composite 6 7 7
Feet/legs composite 3 4 4
Body size composite 2 3 3
DPR () 5 7 7
Service sire calving difficulty () 2 2 2
Daughter calving difficulty () 2 2 2
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Index changes
PTA traits included Relative emphasis on traits in index () Relative emphasis on traits in index () Relative emphasis on traits in index () Relative emphasis on traits in index () Relative emphasis on traits in index () Relative emphasis on traits in index ()
PTA traits included PD 1971 MFP 1976 CY 1984 NM 1994 NM 2000 NM 2003
Milk (lb) 52 27 2 6 5 0
Fat (lb) 48 46 45 25 21 22
Protein (lb) 27 53 43 36 33
Productive life 20 14 11
SCS 6 9 9
Udder composite 7 7
Feet/legs composite 4 4
Body size composite 4 3
DPR 7
Service sire calving difficulty 2
Daughter calving difficulty 2
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International reach

• Semen and embryos marketed internationally
• Interbull Evaluation Centre (Sweden) ranks all
  bulls for each participating country
• Correlations between countries of lt1 accommodated
• Some foreign bulls used as sires of sons
• U.S. and Canadian semen used widely in South
  America
• Red breeds more popular in Europe than in North
  America

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PTA milk prediction
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Net merit prediction
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PTA DPR prediction (curvilinear)
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PTA DPR prediction (linear)
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Holstein milk yield
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Goals beyond increased yield

• Improve fertility
• Increase herdlife
• Improve disease resistance
• Reduce calving difficulty
• Improve efficiency

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Options for increasing progress

• Crossbreeding
• Increased selection intensity
• Adoption of new technologies

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Crossbreds

• Increasing interest
• Way to increase fertility
• Scandinavian Red breeds proposed
• Hybrid vigor observed

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All-breed animal model

• Purebreds and crossbreds together
• Unknown parents grouped by breed
• Variance adjustments by breed
• Age adjusted to 36 months, not maturity

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Genomics

• Genotype calves
• Calculate genomic evaluation
• Select intensively
• Reduce cost of finding top bulls
• Increase rate of genetic progress

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Getting started

• Select animals to genotype
• Assign identification to animals
• Collect tissue samples
• Extract DNA
• Check DNA quality and standardize concentration
• Begin 3-day genotyping process

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Genomic evaluation workflow

• Check genotypes for inheritance errors
• Calculate genomic relationships
• Infer missing genotypes
• Estimate single-nucleotide polymorphism (SNP)
  effects

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Evaluation workflow cont.

• Combine genomic information with parent average
• Based on gain from genomics over parent average
  for animals with genotypes
• Apply to all traits
• Distribute results

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First genomic evaluation

• 750 animals nominated for genotyping
• Over 5,285 predictor bulls from United States and
  Canada
• Embryo flushes
• AI organization that arranged for genotyping have
  first choice
• More information at http//aipl.arsusda.gov/refere
  nce/changes/eval0804.html

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Reliabilities and squared correlations
Squared correlation 100 Squared correlation 100 Reliability () Reliability () Reliability ()
Squared correlation 100 Squared correlation 100 Tradi-tional Genomic Genomic
Trait PA Genomic PA Realized Gain
Net merit 11 28 30 53 23
Milk (lb) 28 49 35 58 23
Fat (lb) 15 44 35 68 33
Protein (lb) 27 47 35 57 22
Fat () 25 63 35 78 43
Protein () 28 58 35 69 34
Productive life 17 27 27 45 18
SCS 23 38 30 51 21
DPR 20 29 25 41 16
Service sire calving ease 27 29 28 31 3
Daughter calving ease 14 22 25 40 15
Final score 23 36 24 42 18
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Marker effects for net merit
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SNP density comparison
PA reliability () Genomic reliability () Genomic reliability () Genomic reliability ()
Trait PA reliability () 10K 20K 40K
Net merit 30 48 50 53
Milk (lb) 35 53 56 58
Fat (lb) 35 64 66 68
Protein (lb) 35 54 56 57
Productive life 27 38 41 45
SCS 30 45 47 51
DPR 25 37 39 41
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Conclusions

• Genomic predictions significantly better than
  parent average (P lt .0001) for all 26 traits
  tested
• Gains in reliability equivalent on average to 11
  daughters with records
• Analysis used 3,576 historical bulls
• Current data includes 5,285 proven bulls
• Larger populations require more SNPs

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Current status

• Field test results distributed for 750 nominated
  animals
• Extension to Jersey and Brown Swiss in progress
• Transition to commercial genotyping labs
• Extension to cows planned for June

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SNP project outcomes

• Genome-wide selection
• Parentage verification and traceability panels
• Enhanced mapping for quantitative trait loci and
  gene discovery

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Future plans

• Evaluations of animals not genotyped updated
  using genomic information (3 times per year)
• Genomic evaluations calculated and released more
  frequently (monthly? weekly?)
• Bull evaluations made public when bull enrolled
  with NAAB
• Cow evaluations made public immediately at USDA
  web site
• January 2009 target for public release

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Genomic selection (New Zealand)

• Identify top 30,000 bull calves annually based on
  parent average
• Genotype by 6 days old with 768 SNP
• Genotype top 500 bull calves with 50K SNP chip
• Keep top 100 bull calves

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Genomic selection (NZ) cont.

• At 1 year, limited progeny test to check for
  undesirable recessives
• At 2 years, market as part of DNA team
• When progeny tested, graduate best to
  progeny-proven team

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Research topics

• Differential inclusion of X-chromosome effects to
  predict bulls versus cows
• Contribution of cows to accuracy of genomic
  prediction
• Benefit of genotyping more predictor bulls
• Optimum methods for combining genomic and current
  evaluation

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Research topics cont.

• Practicality of screening and parentage
  verification with low-cost, low-SNP number assay
• Potential of freely sharing enough SNP for
  accurate parentage discovery
• Computational methods to improve accuracy, such
  as haplotyping

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Summary

• Genomic prediction has great promise
• Extensive changes in bull acquisition and
  marketing and in cow selection expected
• Routine genotyping and validation will become
  industry rather than research responsibilities

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Where do we go from here

• Economic indexes adjusted as conditions change
• Traits added as their collection becomes feasible
  and value demonstrated
• Dairies increase in size and technological
  sophistication
• Selection adapts the cow to meet human needs

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Senior research staff