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Table of contents

  1. Front Matter
    Pages i-xii
  2. Essential Concepts

    1. Front Matter
      Pages 1-1
    2. Gustavo V. Barroso, Ana Filipa Moutinho, Julien Y. Dutheil
      Pages 3-17 Open Access
  3. Statistical Methods for Analyzing Genomes in Populations

    1. Front Matter
      Pages 19-19
    2. Christopher C. Chang
      Pages 49-65 Open Access
    3. Chi-Chun Liu, Suyash Shringarpure, Kenneth Lange, John Novembre
      Pages 67-86 Open Access
    4. Angelos Koropoulis, Nikolaos Alachiotis, Pavlos Pavlidis
      Pages 87-123 Open Access
    5. Stephan Schiffels, Ke Wang
      Pages 147-166 Open Access
    6. Jade Yu Cheng, Thomas Mailund
      Pages 167-189 Open Access
    7. Jerome Kelleher, Konrad Lohse
      Pages 191-230 Open Access
    8. Melissa Hubisz, Adam Siepel
      Pages 231-266 Open Access
  4. Advances in Population Genomics

    1. Front Matter
      Pages 267-267
    2. Tiina M. Mattila, Benjamin Laenen, Tanja Slotte
      Pages 269-287 Open Access
    3. Anne Lorant, Jeffrey Ross-Ibarra, Maud Tenaillon
      Pages 289-311 Open Access
    4. Pierre Gladieux, Fabien De Bellis, Christopher Hann-Soden, Jesper Svedberg, Hanna Johannesson, John W. Taylor
      Pages 313-336 Open Access
    5. Christoph J. Eschenbrenner, Alice Feurtey, Eva H. Stukenbrock
      Pages 337-355 Open Access
    6. Annabelle Haudry, Stefan Laurent, Martin Kapun
      Pages 357-396 Open Access
    7. Arne W. Nolte
      Pages 397-411 Open Access
    8. Kira E. Delmore, Miriam Liedvogel
      Pages 413-433 Open Access
    9. Kristian K. Ullrich, Diethard Tautz
      Pages 435-452 Open Access
    10. David Castellano, Kasper Munch
      Pages 453-463 Open Access
  5. Back Matter
    Pages 465-468

About this book

Introduction

This open access volume presents state-of-the-art inference methods in population genomics, focusing on data analysis based on rigorous statistical techniques. After introducing general concepts related to the biology of genomes and their evolution, the book covers state-of-the-art methods for the analysis of genomes in populations, including demography inference, population structure analysis and detection of selection, using both model-based inference and simulation procedures. Last but not least, it offers an overview of the current knowledge acquired by applying such methods to a large variety of eukaryotic organisms. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, pointers to the relevant literature, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls.

Authoritative and cutting-edge, Statistical Population Genomics aims to promote and ensure successful applications of population genomic methods to an increasing number of model systems and biological questions.



Keywords

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Editors and affiliations

  • Julien Y. Dutheil
    • 1
  1. 1.Department of Evolutionary GeneticsMax Planck Institute for Evolutionary BiologyPlönGermany

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-0716-0199-0
  • Copyright Information The Editor(s) (if applicable) and The Author(s) 2020
  • License CC BY
  • Publisher Name Humana, New York, NY
  • eBook Packages Springer Protocols
  • Print ISBN 978-1-0716-0198-3
  • Online ISBN 978-1-0716-0199-0
  • Series Print ISSN 1064-3745
  • Series Online ISSN 1940-6029
  • Buy this book on publisher's site