An application of change-point recursive models to the relationship between litter size and number of stillborns in pigs.
We developed and implemented change-point recursive models and compared them with a linear recursive model and a standard mixed model (SMM), in the scope of the relationship between litter size (LS) and number of stillborns (NSB) in pigs. The proposed approach allows us to estimate the point of change in multiple-segment modeling of a nonlinear relationship between phenotypes. We applied the procedure to a data set provided by a commercial Large White selection nucleus. The data file consisted of LS and NSB records of 4,462 parities. The results of the analysis clearly identified the location of the change points between different structural regression coefficients. The magnitude of these coefficients increased with LS, indicating an increasing incidence of LS on the NSB ratio. However, posterior distributions of correlations were similar across subpopulations (defined by the change points on LS), except for those between residuals. The heritability estimates of NSB did not present differences between recursive models. Nevertheless, these heritabilities were greater than those obtained for SMM (0.05) with a posterior probability of 85%. These results suggest a nonlinear relationship between LS and NSB, which supports the adequacy of a change-point recursive model for its analysis. Furthermore, the results from model comparisons support the use of recursive models. However, the adequacy of the different recursive models depended on the criteria used: the linear recursive model was preferred on account of its smallest deviance value, whereas nonlinear recursive models provided a better fit and predictive ability based on the cross-validation approach.
Ibáñez-Escriche
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Genètica i Millora Animal, IRTA-Lleida, Lleida, Spain. Noelia.ibanez@irta.es
López de Maturana
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Noguera
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Varona
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D000818:Animals; D001499:Bayes Theorem; D005260:Female; D008098:Litter Size / Q000502:physiology*; D008954:Models, Biological; D009010:Monte Carlo Method; D011247:Pregnancy; D050497:Stillbirth / Q000662:veterinary*; D013552:Swine / Q000502:physiology*