By Daniel Gianola & Keith Hammond
Advances in Statistical Methods for Genetic Improvement of Livestock is a comprehensive scholarly resource that consolidates the statistical foundations and methodologies applied to the genetic improvement of livestock. Drawing on major developments in statistics and computing over recent decades, this book provides a detailed overview of advanced statistical techniques relevant to animal breeding, quantitative genetics, and statistical inference.
Intended as a reference for animal breeders, quantitative geneticists, and statisticians, the text also serves as an advanced academic resource for graduate courses in animal breeding methodology, requiring knowledge in linear models, statistical inference, and quantitative genetics.
Table of Contents
Table of Contents
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Part I: Statistical Methods in Animal Improvement
- Statistical Methods in Animal Improvement: Historical Overview
- Mixed Model Methodology and the Box–Cox Theory of Transformations: A Bayesian Approach
- Models for Discrimination Between Alternative Modes of Inheritance
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Part II: Design of Experiments and Breeding Programs
- Considerations in the Design of Animal Breeding Experiments
- Use of Mixed Model Methodology in Analysis of Designed Experiments
- Statistical Aspects of Design of Animal Breeding Programs
- Optimal Designs for Sire Evaluation Schemes
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Part III: Estimation of Genetic Parameters
- Computational Aspects of Likelihood-Based Inference for Variance Components
- Parameter Estimation in Variance Component Models for Binary Response Data
- Estimation of Genetic Parameters in Non-Linear Models
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Part IV: Prediction and Estimation of Genetic Merit
- A Framework for Prediction of Breeding Value
- BLUP (Best Linear Unbiased Prediction) and Beyond
- Connectedness in Genetic Evaluation
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Part V: Prediction and Estimation in Non-Linear Models
- Generalized Linear Models and Applications to Animal Breeding
- Analysis of Linear and Non-Linear Growth Models with Random Parameters
- Survival, Endurance and Censored Observations in Animal Breeding
- Genetic Evaluation for Discrete Polygenic Traits
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Part VI: Selection and Non-Random Mating
- Accounting for Selection and Mating Biases in Genetic Evaluations
- Statistical Inferences in Populations Undergoing Selection or Non-Random Mating
- Problems in the Use of the Relationship Matrix in Animal Breeding
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Part VII: Statistics and New Genetic Technology
- Identification of Genes with Large Effects
- A General Linkage Method for the Detection of Major Genes
- Reproductive Technology and Genetic Evaluation
