1. 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

2. National Animal Breeding Seminar Series2 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

3. December 13, 2004National Animal Breeding Seminar Series3 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

4. December 13, 2004National Animal Breeding Seminar Series4 Bell Ranch Pilot Project

5. December 13, 2004National Animal Breeding Seminar Series5 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

6. December 13, 2004National Animal Breeding Seminar Series6 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

7. Serial Selection Spring and Fall Herds Sensitivity Analysis Results

8. December 13, 2004National Animal Breeding Seminar Series8 Research Question: Should large commercial ranches consider progeny testing of herd sire replacements as an alternative to performance testing?

9. December 13, 2004National Animal Breeding Seminar Series9 Proposed Progeny Test 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.

10. December 13, 2004National Animal Breeding Seminar Series10 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

11. December 13, 2004National Animal Breeding Seminar Series11 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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13. December 13, 2004National Animal Breeding Seminar Series13 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%

14. December 13, 2004National Animal Breeding Seminar Series14 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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19. December 13, 2004National Animal Breeding Seminar Series19 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.

20. December 13, 2004National Animal Breeding Seminar Series20 Effect of performance and progeny testing on mean genetic merit of selected replacement bulls. Time Mean Genetic Merit Genetic Trend Performance Tested Progeny Tested All Bulls

21. December 13, 2004National Animal Breeding Seminar Series21 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.

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

23. December 13, 2004National Animal Breeding Seminar Series23 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?

24. December 13, 2004National Animal Breeding Seminar Series24 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

25. December 13, 2004National Animal Breeding Seminar Series25 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 (A=I)

26. December 13, 2004National Animal Breeding Seminar Series26 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

27. December 13, 2004National Animal Breeding Seminar Series27 Correlation of true progeny difference and estimated progeny difference (EPD) Average Z methodologyMonte Carlo Z methodology Progeny per sire Correlation6156 2 allelesr(u,u^true)0.48690.68030.48690.6803 r(u,u^sorted)0.39020.56640.30360.4583 r(u,u^random)0.37430.56150.28910.4438 4 allelesr(u,u^true)0.49590.68580.49590.6858 r(u,u^sorted)0.49260.68320.48950.6801 r(u,u^random)0.49010.68030.48560.6758 6 allelesr(u,u^true)0.50530.66840.50530.6684 r(u,u^sorted)0.50520.66820.50510.6681 r(u,u^random)0.50470.66930.50450.6690

28. December 13, 2004National Animal Breeding Seminar Series28 Progeny difference selection differentials (kg.) for the best 5 of 20 sires selected Average Z methodologyMonte Carlo Z methodology Progeny per sire Rank Criterion6156 2 allelesTrue PD16.45 EPD true8.1011.738.1011.73 EPD sorted6.369.594.987.77 EPD rand6.249.454.787.47 4 allelesTrue PD16.45 EPD true8.4611.458.4611.45 EPD sorted8.4011.368.4111.37 EPD rand8.3911.618.4411.40 6 allelesTrue PD16.45 EPD true8.3211.058.3311.05 EPD sorted8.3511.038.3211.05 EPD rand8.4511.268.4411.22

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31. December 13, 2004National Animal Breeding Seminar Series31 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.

32. December 13, 2004National Animal Breeding Seminar Series32 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.

33. December 13, 2004National Animal Breeding Seminar Series33 Acknowledgements Dr. John Pollak Dr. Dick Quaas (SireProb) J. P. Pollak (SireSort) Keith and Bonnie Long, –Bell Ranch, NM National Beef Cattle Evaluation Consortium