1. Genetic correlations between first and later parity calving ease in a sire-maternal grandsire model G. R. Wiggans*, C. P. Van Tassell, J. B. Cole, and L. L. M. Thornton Animal Improvement Programs Laboratory, Agricultural Research Service, USDA, Beltsville, MD 20705-2350 INTRODUCTION First parity cows have higher incidence of calving difficulty than later parity cows In US calving ease evaluations, all parity calvings assumed to be single trait Genetic differences may exist between calving difficulty in first and later parities OBJECTIVES Determine genetic correlation between calving ease in first and later lactations for sire and MGS effects Examine if differences warrant multi-trait analysis DATA & METHODS Five samples of 250,000 from 13,000,000 Holstein calvings since 1980 Limited to 2,600 most commonly occurring bulls as sire or MGS. Scores recorded on 1 to 5 scale (easy to hard) Herd-years required to have at least 20 calvings Number of herds ranged 721 – 860 Compared threshold and linear model For linear model, scores transformed to linear scale  Value was the standard normal deviate at midpoint of probability for category  Probabilities calculated separately by sex of calf and for first and later parity Model Fixed  Year-season of calving (two per year starting in April and October)  Sex of calf within parity (1, 2, 3+)  Birth year for sire and MGS effects Random  Herd-year  Sire  MGS  Error Variance component estimation AIREML on transformed scores and Gibbs sampling on original scores RESULTS Computing: AIREML converged quickly Gibbs Sampling took >20,000 samples for threshold stabilization Estimates: Bayesian and AIREML produced comparable genetic correlations between parity effects Considerable between sample variation for sire correlations (Table 1) Table 1. Genetic correlations between first and later parity calving ease effects MGS estimates were lower and more stable Avg correlation for MGS of 0.8 suggests substantial genetic differences by parity Correlation between traits is moderate and stable between traits (Table 2) Table 2. Correlations between sire and MGS effects by parity (Co)variance component estimates were slightly different from those currently used in evaluations (Table 3) EffectSample Estimation Procedure BayesianAIREML Sire 10.9580.969 20.8810.840 30.8450.842 40.9920.993 50.9920.941 Mean0.9340.917 MGS 10.8070.822 20.7930.815 30.7670.788 40.8350.828 50.7730.778 Mean0.7950.806 Parity MGS 12+ Sire 10.5510.518 2+0.5130.547 RESULTS (cont.) Table 3. Variances relative to parity 1 residual Thresholds lower for later parity which is consistent with lower variance (Table 4) Differences in variance by parity combined with correlation of approximately 0.8 indicate value in modeling MGS CE effect separately by parity Table 4. Mean thresholds across 5 samples Parity Threshold 34 12.42 ± 0.103.71 ± 0.18 2+2.27 ± 0.093.24 ± 0.12 CONCLUSIONS Correlations between first and later parity were high for sire effects, but around 0.8 for MGS Variances for later parity groups were less than first parity, especially for MGS Evaluations can be improved by treating first and later parities as correlated traits, particularly MGS Additional research is needed before implementation of separate genetic effects for first and later parities A bivariate threshold model should be considered for national evaluation A non-Markov Chain Monte Carlo implementation would be complicated A linear model on the transformed scale is possible, but approximations required for linear model may be unacceptable 0.0180.3082+ 0.0120.3461 Herd-year 0.0680.9212+Residual 0.0020.0162+ 0.0030.0331 MGS 0.0010.0212+ 0.0040.0261 Sire SDVarianceParityEffect