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Likelihood, Bayesian, and MCMC Methods in Quantitative Genetics

  • Daniel Sorensen
  • Daniel Gianola

Part of the Statistics for Biology and Health book series (SBH)

Table of contents

  1. Front Matter
    Pages i-xvii
  2. Review of Probability and Distribution Theory

    1. Front Matter
      Pages 1-1
    2. Daniel Sorensen, Daniel Gianola
      Pages 3-75
    3. Daniel Sorensen, Daniel Gianola
      Pages 77-116
  3. Methods of Inference

    1. Front Matter
      Pages 117-117
    2. Daniel Sorensen, Daniel Gianola
      Pages 119-160
    3. Daniel Sorensen, Daniel Gianola
      Pages 161-209
    4. Daniel Sorensen, Daniel Gianola
      Pages 211-285
    5. Daniel Sorensen, Daniel Gianola
      Pages 287-326
    6. Daniel Sorensen, Daniel Gianola
      Pages 327-397
    7. Daniel Sorensen, Daniel Gianola
      Pages 399-442
    8. Daniel Sorensen, Daniel Gianola
      Pages 443-473
  4. Markov Chain Monte Carlo Methods

    1. Front Matter
      Pages 475-475
    2. Daniel Sorensen, Daniel Gianola
      Pages 477-496
    3. Daniel Sorensen, Daniel Gianola
      Pages 497-537
    4. Daniel Sorensen, Daniel Gianola
      Pages 539-560
  5. Applications in Quantitative Genetics

    1. Front Matter
      Pages 561-561
    2. Daniel Sorensen, Daniel Gianola
      Pages 563-604
    3. Daniel Sorensen, Daniel Gianola
      Pages 605-626
    4. Daniel Sorensen, Daniel Gianola
      Pages 627-670
    5. Daniel Sorensen, Daniel Gianola
      Pages 671-699
  6. Back Matter
    Pages 701-740

About this book

Introduction

Over the last ten years the introduction of computer intensive statistical methods has opened new horizons concerning the probability models that can be fitted to genetic data, the scale of the problems that can be tackled and the nature of the questions that can be posed. In particular, the application of Bayesian and likelihood methods to statistical genetics has been facilitated enormously by these methods. Techniques generally referred to as Markov chain Monte Carlo (MCMC) have played a major role in this process, stimulating synergies among scientists in different fields, such as mathematicians, probabilists, statisticians, computer scientists and statistical geneticists. Specifically, the MCMC "revolution" has made a deep impact in quantitative genetics. This can be seen, for example, in the vast number of papers dealing with complex hierarchical models and models for detection of genes affecting quantitative or meristic traits in plants, animals and humans that have been published recently.

This book, suitable for numerate biologists and for applied statisticians, provides the foundations of likelihood, Bayesian and MCMC methods in the context of genetic analysis of quantitative traits. Most students in biology and agriculture lack the formal background needed to learn these modern biometrical techniques. Although a number of excellent texts in these areas have become available in recent years, the basic ideas and tools are typically described in a technically demanding style, and have been written by and addressed to professional statisticians. For this reason, considerable more detail is offered than what may be warranted for a more mathematically apt audience.

The book is divided into four parts. Part I gives a review of probability and distribution theory. Parts II and III present methods of inference and MCMC methods. Part IV discusses several models that can be applied in quantitative genetics, primarily from a bayesian perspective. An effort has been made to relate biological to statistical parameters throughout, and examples are used profusely to motivate the developments.

Keywords

Covariance matrix Evolution Excel Multinomial distribution Normal distribution Poisson distribution Probability distribution Radiologieinformationssystem Random variable Variance expectation–maximization algorithm genes genetics linear regression statistics

Authors and affiliations

  • Daniel Sorensen
    • 1
  • Daniel Gianola
    • 2
  1. 1.Department of Animal Breeding and GeneticsDanish Institute of Agricultural SciencesTjeleDenmark
  2. 2.Department of Animal Science, Department of Dairy Science, Department of Biostatistics and Medical InformaticsUniversity of Wisconsin-MadisonMadisonUSA

Bibliographic information

  • DOI https://doi.org/10.1007/b98952
  • Copyright Information Springer Science+Business Media New York 2002
  • Publisher Name Springer, New York, NY
  • eBook Packages Springer Book Archive
  • Print ISBN 978-0-387-95440-0
  • Online ISBN 978-0-387-22764-1
  • Series Print ISSN 1431-8776
  • Series Online ISSN 2197-5671
  • Buy this book on publisher's site