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Genome-Wide Association Studies and Genomic Prediction

  • Book
  • © 2013

Overview

Editors:
  1. Cedric Gondro

    1. , Ctr. Genetic Analysis and Applications, University of New England, Armidale, Australia

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  2. Julius van der Werf

    1. School of Environmental and Rural Scienc, Div. Animal Science, University of New England, Armidale, Australia

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    You can also search for this editor in PubMed   Google Scholar

  3. Ben Hayes

    1. , Biosciences Research Division, Department of Primary Industries, Bundoora, Australia

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    You can also search for this editor in PubMed   Google Scholar

  • Examines genome-wide association studies, from the preliminary issues to statistical approaches and more
  • Features detailed, step-by-step instruction
  • Includes tips and expert implementation advice to ensure successful results

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

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Table of contents (26 protocols)

  1. Front Matter

    Pages i-xi
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  2. R for Genome-Wide Association Studies

    • Cedric Gondro, Laercio R. Porto-Neto, Seung Hwan Lee
    Pages 1-17

  3. Descriptive Statistics of Data: Understanding the Data Set and Phenotypes of Interest

    • Sonja Dominik
    Pages 19-35

  4. Designing a GWAS: Power, Sample Size, and Data Structure

    • Roderick D. Ball
    Pages 37-98

  5. Managing Large SNP Datasets with SNPpy

    • Faheem Mitha
    Pages 99-127

  6. Quality Control for Genome-Wide Association Studies

    • Cedric Gondro, Seung Hwan Lee, Hak Kyo Lee, Laercio R. Porto-Neto
    Pages 129-147

  7. Overview of Statistical Methods for Genome-Wide Association Studies (GWAS)

    • Ben Hayes
    Pages 149-169

  8. Statistical Analysis of Genomic Data

    • Roderick D. Ball
    Pages 171-192

  9. Using PLINK for Genome-Wide Association Studies (GWAS) and Data Analysis

    • Miguel E. Rentería, Adrian Cortes, Sarah E. Medland
    Pages 193-213

  10. Genome-Wide Complex Trait Analysis (GCTA): Methods, Data Analyses, and Interpretations

    • Jian Yang, Sang Hong Lee, Michael E. Goddard, Peter M. Visscher
    Pages 215-236

  11. Bayesian Methods Applied to GWAS

    • Rohan L. Fernando, Dorian Garrick
    Pages 237-274

  12. Implementing a QTL Detection Study (GWAS) Using Genomic Prediction Methodology

    • Dorian J. Garrick, Rohan L. Fernando
    Pages 275-298

  13. Genome-Enabled Prediction Using the BLR (Bayesian Linear Regression) R-Package

    • Gustavo de los Campos, Paulino Pérez, Ana I. Vazquez, José Crossa
    Pages 299-320

  14. Genomic Best Linear Unbiased Prediction (gBLUP) for the Estimation of Genomic Breeding Values

    • Samuel A. Clark, Julius van der Werf
    Pages 321-330

  15. Detecting Regions of Homozygosity to Map the Cause of Recessively Inherited Disease

    • James W. Kijas
    Pages 331-345

  16. Use of Ancestral Haplotypes in Genome-Wide Association Studies

    • Tom Druet, Frédéric Farnir
    Pages 347-380

  17. Genotype Phasing in Populations of Closely Related Individuals

    • John M. Hickey
    Pages 381-394

  18. Genotype Imputation to Increase Sample Size in Pedigreed Populations

    • John M. Hickey, Matthew A. Cleveland, Christian Maltecca, Gregor Gorjanc, Birgit Gredler, Andreas Kranis
    Pages 395-410

  19. Validation of Genome-Wide Association Studies (GWAS) Results

    • John M. Henshall
    Pages 411-421

  20. Detection of Signatures of Selection Using F ST

    • Laercio R. Porto-Neto, Seung Hwan Lee, Hak Kyo Lee, Cedric Gondro
    Pages 423-436
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Keywords

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.

Reviews

From the reviews:

“A detailed review that will help both genomics newbies and experts to have a better picture of what their genome sequences can offer them today. … People working in medicine and health sciences should read this book and get involved in the field. … anyone unfamiliar with the topic, but with the desire to learn more about what they could find in their own genome, can start learning from scratch by reading this book.” (Alejandra Manjarrez, Lab Times, Issue 5, September, 2013)

“A practical guide for experts to obtain, qualify, and statistically analyse data on genomes and to support genotype-phenotype information. In a growing field, this is the first hands-on book for experts in a relatively new discipline. … the book is too good to ignore once you start reading and pick up information along the way. … if you are new to the field, this book will certainly extend you a warm welcome to the tricky world of GWAS.” (Vijay Shankar, Lab Times, Issue 6, 2013)

Editors and Affiliations

  • , Ctr. Genetic Analysis and Applications, University of New England, Armidale, Australia

    Cedric Gondro

  • School of Environmental and Rural Scienc, Div. Animal Science, University of New England, Armidale, Australia

    Julius van der Werf

  • , Biosciences Research Division, Department of Primary Industries, Bundoora, Australia

    Ben Hayes

Bibliographic Information

  • Book Title: Genome-Wide Association Studies and Genomic Prediction

  • Editors: Cedric Gondro, Julius van der Werf, Ben Hayes

  • Series Title: Methods in Molecular Biology

  • DOI: https://doi.org/10.1007/978-1-62703-447-0

  • Publisher: Humana Totowa, NJ

  • eBook Packages: Springer Protocols

  • Copyright Information: Springer Science+Business Media, LLC 2013

  • Hardcover ISBN: 978-1-62703-446-3Published: 12 June 2013

  • Softcover ISBN: 978-1-4939-5964-8Published: 30 April 2017

  • eBook ISBN: 978-1-62703-447-0Published: 12 June 2013

  • Series ISSN: 1064-3745

  • Series E-ISSN: 1940-6029

  • Edition Number: 1

  • Number of Pages: XI, 566

  • Number of Illustrations: 36 b/w illustrations, 31 illustrations in colour

  • Topics: Bioinformatics, Human Genetics

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