Competing risks analysis of lamb mortality in a terminal sire composite population

Mortality records from birth to weaning of 8,301 lambs from a composite population at the U.S. Meat Animal Research Center were analyzed using a competing risks model. The advantage of the competing risks model over traditional survival analyses is that different hazards of mortality can be assigned to different causes, such as disease, dystocia, and starvation. In this study, specific causes of mortality were grouped into dam-related (DAMR; e.g., dystocia and starvation), pneumonia (PNEU), disease (DIS; excluding pneumonia), and other (OTHER) categories. The hazard of mortality was analyzed using a competing risk approach, where each mortality category was assumed to be independent. Continuous- and discrete-time survival analyses were implemented using sire, animal, and maternal effects mixed models. The continuous-time survival analysis used the Weibull model to describe the hazard of mortality for each category of mortality. Under the discrete-time survival analysis, a complementary log-log link function was used to analyze animal-time data sets using weekly intervals for each category of mortality. Explanatory variables were sex, type of birth, contemporary group, and age of dam. The significant influences of type of birth and age of dam effects were consistent across category of mortality, and the sex effect was significant for all categories except the OTHER category. Estimates of variance components indicated strong maternal effects for all categories except for PNEU. Estimates of additive genetic heritabilities from the discrete maternal effects models were 0.08+/-0.04, 0.09+/-0.18, 0.16+/-0.12, 0.19+/-0.09, and 0.14+/-0.10 for OVERALL (all causes combined), DIS, DAMR, PNEU, and OTHER categories, respectively. Ignoring the cause of the defining event in mortality and longevity studies may hide important genetic differences. Therefore, the effectiveness of breeding programs relying on models that ignore multiple causes of an event in time-to-event data, such as mortality and longevity, could be affected.