Submitted to: Journal of Animal Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/12/2004
Publication Date: 10/2/2004
Citation: Southey, B.R., Rodriguez-Zas, S.L., Leymaster, K.A. 2004. Competing risks analysis of lamb mortality in a terminal sire composite population. Journal of Animal Science. 82:2892-2899. Interpretive Summary: Lamb survival is an important factor that influences profitability of sheep production. Mortality can be due to a variety of causes such as disease, chilling, difficult births, starvation, and injury. These different causes of mortality are typically ignored, resulting in loss of information and limited opportunity to improve survival by selection. An alternative approach considers that individual causes of mortality compete among each other, so that the most limiting cause for a particular lamb is responsible for death of that lamb. Data collected on a terminal sire composite population were analyzed to evaluate this approach. Genetic variation for individual causes of mortality were detected; moreover, the heritabilities of individual causes were greater than the heritability of overall mortality. Therefore, selection for individual causes of mortality is likely to be more effective than selection solely on overall mortality. The competing risks approach can be used to increase lamb survival, leading to improved productivity, profitability, and welfare.
Technical Abstract: 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 (e.g., dystocia and starvation), Pneumonia, Disease (excluding pneumonia), and Other categories. The hazard of mortality was analyzed using a competing risk approach where each mortality category was assumed 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. Although type of birth and age of dam effects were non-significant across category of mortality, there was a significant sex effect for all categories except the Other category. Estimates of variance components indicated strong maternal effects for all categories except for Pneumonia. 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, Disease, Dam-related, Pneumonia, 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 jeopardized.