Genetic analysis of male and female fertility using longitudinal binary data
Averill, Travis Alan
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A permanent effect and two random regression models were used to analyze insemination events. In all three cases, longitudinal threshold models were implemented. In the first study, outcomes of insemination events in the first 250 d after calving were analyzed using a permanent effect model. The posterior mean (SD) of additive, service sire and permanent effect variance was 0.034 (0.006), 0.009 (0.001) and 0.171 (0.013), respectively. The PM (SD) of the estimated heritability was 0.028 (0.005). In the second study, all insemination events were used and a quadratic function and Ali -Schaeffer model were employed to model the additive genetic effects. With random regression models, genetic variance and heritability for insemination success increased with time. Genetic correlations between successive inseminations were positive and high and decreased with the increase of the interval between inseminations. Model comparison based on Bayes factor showed a slight superiority of the quadratic regression model.