Predicting rates of inbreeding for livestock improvement schemes

• Published in: Journal of animal science 2001-04, Vol.79 (4), p.840-853
• Main Authors: Bijma, P, Van Arendonk, J. A, Woolliams, J. A
• Format: Article
• Language: English
• Subjects: Animal Breeding and Genetics; Animal Breeding and Genomics; Animal productions; Animal reproduction; Animals; Animals, Domestic - genetics; Biological and medical sciences; Breeding - methods; Breeding of animals; Female; Fokkerij en Genetica; Fokkerij en Genomica; Forecasting; Fundamental and applied biological sciences. Psychology; Genetics; Genetics of eukaryotes. Biological and molecular evolution; Inbreeding; Leerstoelgroep Fokkerij en genetica; Livestock; Male; Models, Genetic; Phenotype; Population Density; Population genetics, reproduction patterns; Selection, Genetic; Terrestrial animal productions; Vertebrata; Vertebrates; WIAS
• ISSN: 0021-8812; 1525-3163; 0021-8812
• DOI: 10.2527/2001.794840x

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 breeding 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 breeding values (EBV) across age classes, or the number of parents selected from each age class was set to a fixed value and truncation selection was practiced within age classes. Bulmer's equilibrium genetic parameters were obtained by iterating on a pseudo-BLUP selection index and deltaF was predicted for the equilibrium situation. Predictions were substantially more accurate than predictions from other available methods, which ignore the effect of 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.