A model with heterogeneous thresholds for subjective traits: fat cover and conformation score in the Pirenaica beef cattle.
Current selection schemes for livestock improvement use a wide variety of phenotypic traits. Some of them, such as sensory, type, or carcass traits, obtain their records from subjective grading performed by trained technicians. Data from this subjective evaluation usually involve classification under a categorical and arbitrary predefined scale, whose output may lead to strong departures from the Gaussian distribution. In addition, the scale of grading may be different according to different technicians. To study this phenomenon, we have analyzed subjective conformation (CON) and fat cover (FAT) scores in the Pirenaica beef cattle breed from data provided by 12 different slaughterhouses. Three statistical models were used: 1) a Gaussian linear model; 2) an ordered category threshold model; and 3) a specific slaughterhouse ordered category threshold model. These models were analyzed through a Bayesian analysis via a Gibbs sampler with a data augmentation step. Posterior mean estimates of heritability ranged from 0.23 to 0.26 for CON, and from 0.13 to 0.16 for FAT. Statistical models were compared by the deviance information criteria, and the slaughterhouse-specific ordered category threshold model was selected as the most plausible. This result was confirmed by the fact that the threshold estimates differed noticeably between slaughterhouses. Finally, the proposed model for genetic evaluation increased the expected selection response by up to 7.6% for CON and 11.2% for FAT.
Varona
L
L
Unidad de Genética Cuantitativa y Mejora Animal, Departamento de Anatomía, Embriología y Genética Animal, Universidad de Zaragoza, Miguel Servet 177, 50013, Zaragoza, Spain. lvarona@unizar.es
Moreno
C
C
Altarriba
J
J
D000273:Adipose Tissue / Q000502:physiology*; D000818:Animals; D001947:Breeding; D002417:Cattle / Q000235:genetics / Q000502:physiology*; D005260:Female; D008297:Male; D008460:Meat / Q000592:standards*; D008957:Models, Genetic; D012641:Selection, Genetic