Predicting rates of inbreeding for livestock improvement schemes

This article presents a deterministic method to predict rates of inbreeding (DeltaF) for typical livestock improvement schemes. The method is based on a recently developed general theory to predict rates of inbreeding, which uses the concept of long-term genetic contributions. A typical livestock br eeding population was modeled, with overlapping generations, BLUP selection , and progeny testing of male selection candidates. Two types of selection were practiced: animals were either selected by truncation on estimated bre eding values (EBV) across age classes, or the number of parents selected fr om each age class was set to a fixed value and truncation selection was pra cticed within age classes. Bulmer's equilibrium genetic parameters were obt ained by iterating on a pseudo-BLUP selection index and DeltaF was predicte d for the equilibrium situation. Predictions were substantially more accura te than predictions from other available methods, which ignore the effect o f selection on DeltaF. Predictions were accurate for schemes with up to 20 sires. Predicted DeltaF was somewhat too low for schemes with more than 20 sires, which was due to the use of simple linear models to predict genetic contributions. The present method provides a computationally feasible (i.e. , deterministic) tool to consider both the rate of inbreeding and the rate of genetic gain when optimizing livestock improvement schemes.