@entity1271 in @entity291 can be defined as a binary trait, reflecting presence or absence of @entity922 ( @entity922 ), or as a count variable, number of @entity1271 cases (NCM), within a defined time interval. Many different models have been proposed for genetic analyses of @entity1271 , and the objective of this study was to evaluate the predictive ability and sire predictions of a set of models for genetic evaluation of @entity922 or NCM. Linear- and threshold liability models for @entity922 , and linear, censored ordinal threshold, and zero-inflated Poisson (ZIP) models for NCM were compared in a cross-validation study. To assess the ability of these models to predict future data, records from 620492 first-lactation Norwegian Red @entity291 , which were daughters of 3064 sires, were evaluated in a fourfold cross-validation scheme. The mean squared error of prediction was used for model comparison. All models but ordinal threshold model equally performed when comparing the overall predictive ability. This result was on average, across sick and healthy @entity291 ; however, the models behaved differently for each category of animals. For example, healthy @entity291 were predicted better by the threshold and linear models for binary data and ZIP model, whereas for mastitic @entity291 , the ordinal threshold model was by far the best model. Predicted sire effects and rankings of sires were highly correlated across all models. For practical purposes, the linear models are very competitive with the nonlinear models.

Predictive ability of alternative models for genetic analysis of XXXX .

@entity291 :: ('9913', 'Species') :: ['cows'] @entity922 :: ('MESH:D013568', 'Disease') :: ['CM', 'clinical mastitis'] @entity1271 :: ('MESH:D008413', 'Disease') :: ['Mastitis', 'mastitis']
@entity922 :: (MESH:D013568,Disease) :: ['clinical mastitis']
