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Journal of Animal Science Abstract - Animal Genetics

A comparison between different survival and threshold models with an application to piglet preweaning survival in a dry-cured ham-producing crossbred line1

 

This article in JAS

  1. Vol. 88 No. 6, p. 1990-1998
     
    Received: Sept 08, 2009
    Accepted: Feb 11, 2010
    Published: December 4, 2014


    2 Corresponding author(s): alessio.cecchinato@unipd.it
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doi:10.2527/jas.2009-2460
  1. A. Cecchinato 2,
  2. O. González-Recio,
  3. E. López de Maturana,
  4. L. Gallo* and
  5. P. Carnier*
  1. Department of Animal Science, University of Padova, Viale dell’Università 16, 35020 Legnaro (PD), Italy; and
    Departamento Mejora Genética Animal, Instituto Nacional de Investigaciones Agrarias (INIA), Ctra La Coruña, km 7.5. 28040 Madrid, Spain

ABSTRACT

ABSTRACT

Different approaches for predicting genetic merit of piglet preweaning survival were compared using proportional hazard, threshold (TM), and sequential threshold (STM) models. Data were from 13,924 crossbred piglets (1,347 litters), born from 2000 to 2006, and originated by mating 189 Large White C21 Gorzagri boars to 328 Large White-derived crossbred sows. A frailty proportional hazard model was fitted assuming 2 different baseline hazard functions (Cox and Weibull time-dependent model) and including sire and nursed litter as random effects. The TM and STM included the same effects as considered in the proportional hazard model. Model fitting was evaluated in terms of goodness of fit and predictive ability. The goodness-of-fit was evaluated using the local weighted regression and the mean squared error, whereas the predictive ability was assessed by using a cross-validation procedure. Estimated sire variances for piglet preweaning mortality were low, and heritability ranged from 0.04 to 0.06. All 4 models led to similar ranking of sires. Results suggest that STM may be preferred to the other models for genetic evaluation of piglet preweaning survival, both for its better predictive ability and its easier interpretation. Further, STM is computationally less demanding than survival models and allows for estimating different variance components from birth up to weaning.

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