Modeling variability in immunocompetence and immunoresponsiveness

The purposes of this paper were to 1) develop a stochastic model that would reflect observed variation between animals and across ages in immunocompetence and responsiveness; and 2) illustrate consequences of this variability for the statistical power of genotype comparisons and selection. A stochastic model of immunocompetence development and responsiveness kinetics was developed. This model enabled variability in immunological variables to be taken into account in the evaluation of challenge and measurement strategies for selection. The characteristics of the variation in model output reflect those observed in the literature, to the extent that variation in the literature shows a consistent pattern; knowledge of true variation and patterns of variation in immunological variables is limited. The model created correlations between immunocompetence and immunoresponsiveness components, as well as correlations within each component across time. These correlations were generally in agreement with literature estimates, where available. The model enabled predictions of the effectiveness of selection for improved health through immunocompetence or immunoresponsiveness. It was predicted that effective selection for increased general immunocompetence to improve health should be done only when baseline immunity has matured. Further, the model implied that selection is unlikely to be successful if it is based only on a single measurement. Problems with low statistical power to detect differences between genotypes can be reduced by increasing challenge age in the experimental design, and one should ensure that the effects of maternal immunity are minimal when the challenge is done. The ability to detect differences between different groups of animals differs substantially with measurement timing because of low repeatabilities of immunocompetence and responsiveness across time. In general, the probability of detecting differences becomes higher when the challenge age is increased. Consequently, both the age at selection and the age at which information is gathered for selection must be considered carefully when designing genetic evaluations.