Advances in Statistical Methods for Genetic Improvement of Livestock

Advances in Statistical Methods for Genetic Improvement of Livestock edited by Daniel Gianola, Keith Hammond.

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, q...

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Bibliographic Details
Corporate Author: SpringerLink (Online service)
Other Authors: Gianola, Daniel (Editor), Hammond, Keith (Editor)
Format: eBook
Language:English
Published: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 1990.
Edition:1st ed. 1990.
Series:Advanced Series in Agricultural Sciences, 18
Springer eBook Collection.
Subjects:
Agriculture.
Forestry.
Statistics .
Cell biology.
Electronic resources (E-books)
Online Access:Click to view e-book
Holy Cross Note:Loaded electronically.
Electronic access restricted to members of the Holy Cross Community.
Table of Contents:
  • I: General
  • 1 Statistical Methods in Animal Improvement: Historical Overview
  • 2 Mixed Model Methodology and the Box-Cox Theory of Transformations: A Bayesian Approach
  • 3 Models for Discrimination Between Alternative Modes of Inheritance
  • Discussion Summary
  • II: Design of Experiments and Breeding Programs
  • 4 Considerations in the Design of Animal Breeding Experiments
  • 5 Use of Mixed Model Methodology in Analysis of Designed Experiments
  • 6 Statistical Aspects of Design of Animal Breeding Programs: A Comparison Among Various Selection Strategies
  • 7 Optimum Designs for Sire Evaluation Schemes
  • Discussion Summary
  • III: Estimation of Genetic Parameters
  • 8 Computational Aspects of Likelihood-Based Inference for Variance Components
  • 9 Parameter Estimation in Variance Component Models for Binary Response Data
  • 10 Estimation of Genetic Parameters in Non-Linear Models
  • Discussion Summary
  • IV: Prediction and Estimation of Genetic Merit
  • 11 A Framework for Prediction of Breeding Value
  • 12 BLUP (Best Linear Unbiased Prediction) and Beyond
  • 13 Connectedness in Genetic Evaluation
  • Discussion Summary
  • V: Prediction and Estimation in Non-Linear Models
  • 14 Generalized Linear Models and Applications to Animal Breeding
  • 15 Analysis of Linear and Non-Linear Growth Models with Random Parameters
  • 16 Survival, Endurance and Censored Observations in Animal Breeding
  • 17 Genetic Evaluation for Discrete Polygenic Traits in Animal Breeding
  • Discussion Summary
  • VI: Selection and Non-Random Mating
  • 18 Accounting for Selection and Mating Biases in Genetic Evaluation
  • 19 Statistical Inferences in Populations Undergoing Selection or Non-Random Mating
  • 20 Problems in the Use of the Relationship Matrix in Animal Breeding
  • Discussion Summary
  • VII: Statistics and New Genetic Technology
  • 21 Identification of Genes with Large Effects
  • 22 A General Linkage Method for the Detection of Major Genes
  • 23 Reproductive Technology and Genetic Evaluation
  • Discussion Summary.

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