Modeling selection for production traits under constant infection pressure.

Abstract

This article presents a model describing the relationship between level of disease resistance and production under constant infection pressure. The model assumes that given a certain infection pressure, there is a threshold for resistance below which animals will stop producing, and that there is also a threshold for resistance above which animals produce at production potential. In between both thresholds animals will show a decrease in production, the size of decrease depending on the severity of infection and the level of resistance. The dynamic relationship between production and resistance when level of resistance changes, such as due to infection, is modeled both stochastically and deterministically. Selection started in a population with very poor level of resistance introduced in an environment with constant infection pressure. Mass selection on observed production was applied, which resulted in a nonlinear selection response for all three traits considered. When resistance is poor, selection for observed production results in increased level of resistance. With increasing level of resistance, selection response shifts to production potential and eventually selection for observed production is equivalent to selection for production potential. The rate at which resistance is improved depends on its heritability, the difference between both thresholds, and selection intensity. The model also revealed that when a zero correlation between resistance and production potential is assumed, the phenotypic correlation between resistance and observed production level increases for low levels of resistance and subsequently asymptotes to zero, whereas the phenotypic correlation between production potential and observed production asymptotes to 1.0. For most breeding schemes investigated, the deterministic model performed well in relation to the stochastic simulation results. Experimental results reported in literature support the model predictions.