Genome-Wide Association Studies and Genomic Prediction

  • Cedric Gondro
  • Julius van der Werf
  • Ben Hayes

Part of the Methods in Molecular Biology book series (MIMB, volume 1019)

Table of contents

  1. Front Matter
    Pages i-xi
  2. Cedric Gondro, Laercio R. Porto-Neto, Seung Hwan Lee
    Pages 1-17
  3. Faheem Mitha
    Pages 99-127
  4. Cedric Gondro, Seung Hwan Lee, Hak Kyo Lee, Laercio R. Porto-Neto
    Pages 129-147
  5. Roderick D. Ball
    Pages 171-192
  6. Miguel E. Rentería, Adrian Cortes, Sarah E. Medland
    Pages 193-213
  7. Jian Yang, Sang Hong Lee, Michael E. Goddard, Peter M. Visscher
    Pages 215-236
  8. Rohan L. Fernando, Dorian Garrick
    Pages 237-274
  9. Dorian J. Garrick, Rohan L. Fernando
    Pages 275-298
  10. Gustavo de los Campos, Paulino Pérez, Ana I. Vazquez, José Crossa
    Pages 299-320
  11. Tom Druet, Frédéric Farnir
    Pages 347-380
  12. John M. Hickey, Matthew A. Cleveland, Christian Maltecca, Gregor Gorjanc, Birgit Gredler, Andreas Kranis
    Pages 395-410
  13. Laercio R. Porto-Neto, Seung Hwan Lee, Hak Kyo Lee, Cedric Gondro
    Pages 423-436
  14. Bruno Dourado Valente, Guilherme Jordão de Magalhães Rosa
    Pages 449-464
  15. Qinxin Pan, Ting Hu, Jason H. Moore
    Pages 465-477
  16. Ashley Petersen, Justin Spratt, Nathan L. Tintle
    Pages 519-541
  17. Julius van der Werf
    Pages 543-561
  18. Back Matter
    Pages 563-566

About this book


With the detailed genomic information that is now becoming available, we have a plethora of data that allows researchers to address questions in a variety of areas. Genome-wide association studies (GWAS) have become a vital approach to identify candidate regions associated with complex diseases in human medicine, production traits in agriculture, and variation in wild populations.  Genomic prediction goes a step further, attempting to predict phenotypic variation in these traits from genomic information.  Genome-Wide Association Studies and Genomic Prediction pulls together expert contributions to address this important area of study.  The volume begins with a section covering the phenotypes of interest as well as design issues for GWAS, then moves on to discuss efficient computational methods to store and handle large datasets, quality control measures, phasing, haplotype inference, and imputation.  Later chapters deal with statistical approaches to data analysis where the experimental objective is either to confirm the biology by identifying genomic regions associated to a trait or to use the data to make genomic predictions about a future phenotypic outcome (e.g. predict onset of disease). As part of the Methods in Molecular Biology series, chapters provide helpful, real-world implementation advice.


Computational methods GWAS Genome analysis Genome-wide association study Genomic prediction Phenotypic outcomes Statistical approaches

Editors and affiliations

  • Cedric Gondro
    • 1
  • Julius van der Werf
    • 2
  • Ben Hayes
    • 3
  1. 1., Ctr. Genetic Analysis and ApplicationsUniversity of New EnglandArmidaleAustralia
  2. 2.School of Environmental and Rural Scienc, Div. Animal ScienceUniversity of New EnglandArmidaleAustralia
  3. 3., Biosciences Research DivisionDepartment of Primary IndustriesBundooraAustralia

Bibliographic information

  • DOI
  • Copyright Information Springer Science+Business Media, LLC 2013
  • Publisher Name Humana Press, Totowa, NJ
  • eBook Packages Springer Protocols
  • Print ISBN 978-1-62703-446-3
  • Online ISBN 978-1-62703-447-0
  • Series Print ISSN 1064-3745
  • Series Online ISSN 1940-6029
  • Buy this book on publisher's site