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Journal articles
Assessment of Poisson, probit and linear models for genetic analysis of presence and number of black spots in Corriedale sheep.
Abstract : Black skin spots are associated with pigmented fibres in wool, an important quality fault. Our objective was to assess alternative models for genetic analysis of presence (BINBS) and number (NUMBS) of black spots in Corriedale sheep. During 2002-08, 5624 records from 2839 animals in two flocks, aged 1 through 6 years, were taken at shearing. Four models were considered: linear and probit for BINBS and linear and Poisson for NUMBS. All models included flock-year and age as fixed effects and animal and permanent environmental as random effects. Models were fitted to the whole data set and were also compared based on their predictive ability in cross-validation. Estimates of heritability ranged from 0.154 to 0.230 for BINBS and 0.269 to 0.474 for NUMBS. For BINBS, the probit model fitted slightly better to the data than the linear model. Predictions of random effects from these models were highly correlated, and both models exhibited similar predictive ability. For NUMBS, the Poisson model, with a residual term to account for overdispersion, performed better than the linear model in goodness of fit and predictive ability. Predictions of random effects from the Poisson model were more strongly correlated with those from BINBS models than those from the linear model. Overall, the use of probit or linear models for BINBS and of a Poisson model with a residual for NUMBS seems a reasonable choice for genetic selection purposes in Corriedale sheep.
Cited literature [27 references]
https://hal-riip.archives-ouvertes.fr/pasteur-00606553
Contributor : Mariella Botta <>
Submitted on : Wednesday, July 6, 2011 - 8:23:38 PM
Last modification on : Thursday, April 4, 2019 - 11:36:01 AM
Document(s) archivé(s) le : Monday, December 5, 2016 - 12:08:56 AM
Contributor : Mariella Botta <>
Submitted on : Wednesday, July 6, 2011 - 8:23:38 PM
Last modification on : Thursday, April 4, 2019 - 11:36:01 AM
Document(s) archivé(s) le : Monday, December 5, 2016 - 12:08:56 AM
