Replacement Sire Selection and Genetic Evaluation Strategies for Large Commercial Ranches

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Replacement Sire Selection and Genetic Evaluation
Strategies for Large Commercial Ranches

• Robert L. Weaber
• Assistant Professor,
• State Extension Specialist-Beef Genetics
• University of Missouri-Columbia
• National Animal Breeding Seminar Series
• December 13, 2004

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Beef Cattle Selection in US

• Driven by seedstock segment
• Loose stratification of nucleus and multipliers
• Supply yearling bulls to commercial producers
• Perceived needs of commercial segment
• Commercial segment
• Buys bulls
• Minimal selection applied following purchase
• Almost no data collection, NO genetic evaluation

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Serial Selection at Commercial Level

• Can it work??
• What type of operation?
• What marketing structure?
• What traits?
• Performance and progeny test strategies?
• Genetic evaluation strategies?
• Pilot project
• Bell Ranch, New Mexico
• 4,500 cows, 300,000 acres
• Integrated seedstock unit, but very traditionally
  managed

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Bell Ranch Pilot Project
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Bell Ranch Pilot Project Goals

• Create additional selection opportunities
• Serial selection (performance and progeny
  testing)
• Overcome obstacles
• Animal identification
• Data collection
• Pedigree construction
• Genetic evaluation
• Demonstrate approach
• Investigate efficacy of selection strategy

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Research Objectives

• Simulate the serial selection strategy used in
  Bell Ranch Pilot Project to investigate the
  economic returns.
• Evaluate two genetic evaluation systems that
  incorporate information from DNA genotype derived
  pedigrees
• Assess the value of sorting commercial bulls into
  breeding groups that optimize the probability of
  single sire paternity assignments

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Serial Selection Spring and Fall
HerdsSensitivity Analysis Results
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Research Question

• Should large commercial ranches consider progeny
  testing of herd sire replacements as an
  alternative to performance testing?

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Proposed ProgenyTest Protocol

• Partition herd
• Commercial progeny test cows
• Progeny test cows all same age
• Large multi-sire breeding pasture
• Assign paternity via DNA genotype analysis
• Progeny test must minimize costs and operate with
  minimal management intrusion.

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Materials and Methods

• Simulation in Matlab
• Large commercial ranch with(out) fall herd
• Optimization of bulls progeny tested, selected
  and calves tested per sire
• Selection differentials for performance tested,
  progeny tested and selected bulls
• Monte Carlo simulation (200 replicates/scenario)
• True ERT breeding values for bulls
• Bull and progeny group phenotypes
• All records evaluated in RAM

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Materials and Methods

• Economics
• Lifetime production of selected progeny test bull
  vs. performance tested bull
• Costs of progeny test
• DNA genotyping, calf ID, data processing, etc.
• Interest charges
• Expected returns of progeny test system vs.
  performance test system
• Risk analysis of selected optimization

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Optimization Results

• Spring Herd
• Base Group 60
• Perf. Test Bulls 15
• Prog. Test Bulls 16
• Selected Bulls 12
• Calves per Sire 8
• System Comp -728.80
• Profit Frequency 45.8

• Spring Fall Herd
• Base Group 60
• Perf. Test Bulls 15
• Prog. Test Bulls 16
• Selected Bulls 12
• Calves per Sire 16
• System Comp 4,474.15
• Profit Frequency 62.3

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Sensitivity Analysis

• Test optimization for effect of changing one
  parameter.
• Tested
• Heritability
• Additive Genetic Variance
• Exposure Rate
• Progeny Test Costs per Calf
• Value of Unit of Production
• Exclusion Rate

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Serial Selection Simulation Results and
Conclusions

• Simulation of serial selection is a useful
  managerial decision aid given wide range inputs
  and variation of economic returns.
• Serial selection valuable tool for identifying
  superior genetics to be returned to seedstock
  unit to change genetic trend.

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Effect of performance and progeny testing on mean
genetic merit of selected replacement bulls.
Progeny Tested
Performance Tested
All Bulls
Mean Genetic Merit
Genetic Trend
Time
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Serial Selection Simulation Results and
Conclusions

• Herds likely to benefit from serial selection
  systems
• Use bulls for two breeding seasons/year
• Select for moderately heritable trait of economic
  importance
• Simulated weaning wt. observed on animal and
  progeny
• Follow up study carcass traits
• Can reduce test costs
• Genetic evaluation system that includes data on
  all progeny via paternity prob. from genotype
  analysis would be useful.

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Genetic Evaluation Strategies to Use Paternity
Probabilities and Effects of Sorting Sires to
Breeding Groups by Genotype
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Objectives

• Investigate the effect of the incorporation of
  paternity probabilities on the genetic evaluation
  of sires
• Metrics
• Correlation of true and predicted progeny
  differences
• Selection differentials of selected sires
• Does sorting bulls to breeding groups improve
  evaluation?

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Use of Paternity Probabilities

• Dr. Quaas methodology for computation
• Likelihood based approach
• Incorporation into Genetic Evaluation
• Adaptation of Hendersons Average Numerator
  relationship method
• Monte Carlo method

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Materials and Methods

• Simulation of Sire Genotypes and true progeny
  differences
• 200 replicates of 20 sires
• Divided into 2 breeding groups
• Assigned at random or sorted by genotype
• 6-15 progeny per sire
• 12 poly-allelic markers 2-6 alleles per marker
• Bi-allelic panel with 4-28 loci
• Simulate co-dominant inheritance of calf genotype
  and calf phenotypes (WW)
• Records evaluated in Sire Model
• Sires considered unrelated (AI)

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Data Structure

• Focused on structure of Z (incidence matrix
  relating calf records to sires)
• True Pedigree from Simulation
• Typical Z incidence matrix
• Average Z method
• Substitute matrix of probabilities for typical Z
• Monte Carlo Z method
• Generate typical incidence matrix Z in the
  proportions suggested by paternity probabilities
• EPD Sets True pedigree, Sorted, Random

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Correlation of true progeny difference and
estimated progeny difference (EPD)
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Progeny difference selection differentials (kg.)
for the best 5 of 20 sires selected
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Results and Conclusions

• Inclusion of information from DNA genotype
  derived pedigrees can produce useful genetic
  evaluations and reliable selection decisions.
• Sorting sires to breeding groups only marginally
  useful
• Better at low exclusion rates
• Maybe important when related sires considered
  (topic for follow-up research)
• Average Z methodology generally produced higher
  accuracies of prediction and larger selection
  differentials than did the Monte Carlo Z method
  of incorporating paternity probabilities.

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Results and Conclusions

• Pedigrees do not need to be fully resolved to be
  useful in genetic evaluation.
• Marker panels with exclusion rates of 0.90 and
  greater produced adequate pedigree resolution for
  useful genetic evaluations.
• Small panels of SNP markers may provide a
  low-cost genotyping method that will make serial
  selection more profitable and available to more
  commercial producers.

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Acknowledgements

• Dr. John Pollak
• Dr. Dick Quaas (SireProb)
• J. P. Pollak (SireSort)
• Keith and Bonnie Long,
• Bell Ranch, NM
• National Beef Cattle Evaluation Consortium