The prediction of producing desirable traits in offspring such as increased growth rate or superior meat, milk and wool production is a vital economic tool to the animal scientist. Summarizing the latest developments in genomics relating to animal breeding values and design of breeding programs, this new edition includes models of survival analysis, social interaction and sire and dam models, as well as advancements in the use of SNPs in the computation of genomic breeding values.

Fundamental to any livestock improvement programme by animal scientists, is the prediction of genetic merit in the offspring generation for desirable production traits such as increased growth rate, or superior meat, milk and wool production. Covering the foundational principles on the application of linear models for the prediction of genetic merit in livestock, this new edition is fully updated to incorporate recent advances in genomic prediction approaches, genomic models for multi-breed and crossbred performance, dominance and epistasis. It provides models for the analysis of main production traits as well as functional traits and includes numerous worked examples. For the first time, R codes for key examples in the textbook are provided online. Suitable for graduate and postgraduate students, researchers and lecturers of animal breeding, genetics and genomics, this established textbook provides a thorough grounding in both the basics and in new developments of linear models and animal genetics.

Best Linear Unbiased Prediction (BLUP) has become the most widely accepted method for the genetic evaluation of domestic livestock. Since its introduction, the method has evolved in terms of its application to sire models, and to sire and maternal grandsire models in the early years, to animal models and multivariate analysis more recently. Despite these developments, there has been no straightforward text available on the application of linear models to the prediction of animal breeding values. This book fills this gap, providing a blend of theory and practical applications. It covers basic principles of breeding value predictions and the application of BLUP to genetic evaluations under various models. Some knowledge of basic matrix algebra and of quantitative genetics is assumed on the part of the reader, although some introductory matrix algebra is included in an appendix.

Developments in statistics and computing as well as their application to genetic improvement of livestock gained momentum over the last 20 years. This text reviews and consolidates the statistical foundations of animal breeding. This text will prove useful as a reference source to animal breeders, quantitative geneticists and statisticians working in these areas. It will also serve as a text in graduate courses in animal breeding methodology with prerequisite courses in linear models, statistical inference and quantitative genetics.