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Accounting for differences in rate of maturity in yield evaluations

Accounting for differences in rate of maturity in yield evaluations. Objectives. Estimate correlations among first five parities Develop random regression model to represent genetic differences by parity Assess effectiveness in predicting future daughter performance.

matthewrogers
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Accounting for differences in rate of maturity in yield evaluations

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  1. Accounting for differences in rate of maturity in yield evaluations

  2. Objectives • Estimate correlations among first five parities • Develop random regression model to represent genetic differences by parity • Assess effectiveness in predicting future daughter performance

  3. Variance Component Estimation • Gibbs sampling used to estimate (co)variance matrices • Each of 5 parities a correlated trait • Herd-year random within fixed herd-2 year period • CA Jersey set ~150,000 animals • WI Holstein set ~180,000 animals

  4. Jersey CorrelationsMilk

  5. Holstein CorrelationsMilk

  6. HeritabilitiesMilk

  7. Correlations in Other Countries

  8. Random Regression on Parity • Represent change in genetic merit across parity with 1 parameter • Relative size of genetic differences by parity derived from genetic correlations • Difference between first and second set to 1

  9. Approximation of Jersey Correlations • Parity distances P = [-0.9 0.1 0.4 0.6 0.7] • Mean absolute difference between actual and estimated correlations .016 • Maximum difference .034

  10. Random Regression Model • Random regression on parity distances added to repeatability model • Same (co)variance matrix as animal effect • Portion of total variance 4% • Animal portion 40% (for Jersey)

  11. Data for Model Comparisons • 2.17 million lactations 1-5 for 920,000 Jersey cows with first lactation • Calvings 1985 and later and included in Feb 04 evaluation • Truncated set (calvings  1998)

  12. Truncation Study • 4 Evaluation runs • All – 5 traits • All – Regression on P • Trunc – 5 traits • Trunc – Regression on P • Calculate correlation of Parent Avg. from Trunc with EBV from All • Correlations between 2 All runs

  13. Truncation Study Results • Bulls born  1990, Rel  60% • Correlations .01 - .02 higher for PA from slope with EBV from first lactation • 8% of variance assigned to slope • Correlations were not higher with EBV from all lactations

  14. Conclusions • Random regression on vector of parity distances can represent correlations among parities < 1 • Regression effect can use same relationship matrix as animal effect. • Little benefit over current model

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