Genome-Wide Association Studies and Genomic Prediction

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

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

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)

• , 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

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