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Advances in Statistical Methods for Genetic Improvement of Livestock

  • Book
  • © 1990

Overview

Editors:
  1. Daniel Gianola

    1. Department of Animal Sciences 126 Animal Sciences Laboratory, University of Illinois, Urbana, USA

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    You can also search for this editor in PubMed   Google Scholar

  2. Keith Hammond

    1. Animal Genetics and Breeding Unit (AGBU), University of New England and NSW Agriculture and Fisheries, Armidale, Australia

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

Part of the book series: Advanced Series in Agricultural Sciences (AGRICULTURAL, volume 18)

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Table of contents (23 chapters)

  1. Front Matter

    Pages i-xx
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  3. Back Matter

    Pages 56-57
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  5. Back Matter

    Pages 132-134
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  6. Estimation of Genetic Parameters

    1. Front Matter

      Pages 135-135
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    2. Computational Aspects of Likelihood-Based Inference for Variance Components

      • D. A. Harville, T. P. Callanan
      Pages 136-176

    3. Parameter Estimation in Variance Component Models for Binary Response Data

      • M. W. Knuiman, N. M. Laird
      Pages 177-189

    4. Estimation of Genetic Parameters in Non-Linear Models

      • S. P. Smith
      Pages 190-206

  7. Back Matter

    Pages 207-208
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  8. Prediction and Estimation of Genetic Merit

    1. Front Matter

      Pages 209-209
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    2. A Framework for Prediction of Breeding Value

      • D. Gianola, S. Im, F. W. Macedo
      Pages 210-238

    3. BLUP (Best Linear Unbiased Prediction) and Beyond

      • D. A. Harville
      Pages 239-276
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Keywords

About this book

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.

Editors and Affiliations

  • Department of Animal Sciences 126 Animal Sciences Laboratory, University of Illinois, Urbana, USA

    Daniel Gianola

  • Animal Genetics and Breeding Unit (AGBU), University of New England and NSW Agriculture and Fisheries, Armidale, Australia

    Keith Hammond

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