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Life Sciences - Systems Biology and Bioinformatics | Genome-Wide Association Studies and Genomic Prediction

Genome-Wide Association Studies and Genomic Prediction

Series: Methods in Molecular Biology, Vol. 1019

Gondro, Cedric, van der Werf, Julius, Hayes, Ben (Eds.)

2013, XI, 566 p. 67 illus., 31 illus. in color. With online files/update.

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  • 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
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.

Content Level » Professional/practitioner

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

Related subjects » Human Genetics - Systems Biology and Bioinformatics

Table of contents 

1. R for Genome-Wide Association Studies

            Cedric Gondro, Laercio R. Porto-Neto, and Seung Hwan Lee

 

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

            Sonja Dominik

 

3. Designing a Genome-Wide Association Studies (GWAS): Power, Sample Size, and Data Structure

            Roderick D. Ball

 

4. Managing Large SNP Datasets with SNPpy

            Faheem Mitha

 

5. Quality Control for Genome-Wide Association Studies

            Cedric Gondro, Seung Hwan Lee, Hak Kyo Lee, and Laercio R. Porto-Neto

 

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

            Ben Hayes

 

7. Statistical Analysis of Genomic Data

            Roderick D. Ball

 

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

            Miguel E. Rentería, Adrian Cortes, and Sarah E. Medland

 

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

            Jian Yang, Sang Hong Lee, Michael E. Goddard, and Peter M. Visscher

 

10. Bayesian Methods Applied to Genome-Wide Association Studies (GWAS)

            Rohan L. Fernando and Dorian J. Garrick

 

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

            Dorian J. Garrick and Rohan L. Fernando

 

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

            Gustavo de los Campos, Paulino Pérez, Ana I. Vazquez, and José Crossa

 

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

            Samuel A. Clark and Julius van der Werf

 

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

            James W. Kijas

 

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

            Tom Druet and Frédéric Farnir

 

16. Genotype Phasing in Populations of Closely Related Individuals

            John M. Hickey

 

17. Genotype Imputation to Increase Sample Size in Pedigreed Populations

            John M. Hickey, Matthew A. Cleveland, Christian Maltecca, Gregor Gorjanc, Birgit Gredler, and Andreas Kranis

 

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

            John M. Henshall

 

19. Detection of Signatures of Selection Using FST

            Laercio R. Porto-Neto, Seung Hwan Lee, Hak Kyo Lee, and Cedric Gondro

 

20. Association Weight Matrix: A Network-Based Approach Towards Functional Genome-Wide Association Studies

            Antonio Reverter and Marina R.S. Fortes

 

21. Mixed Effects Structural Equation Models and Phenotypic Causal Networks

            Bruno Dourado Valente and Guilherme Jordão de Magalhães Rosa

 

22. Epistasis, Complexity, and Multifactor Dimensionality Reduction

            Qinxin Pan, Ting Hu, and Jason H. Moore

 

23. Applications of Multifactor Dimensionality Reduction to Genome-Wide Data Using the R Package ‘MDR’

            Stacey Winham

 

24. Higher Order Interactions: Detection of Epistasis Using Machine Learning and Evolutionary Computation

            Ronald M. Nelson, Marcin Kierczak, and Örjan Carlborg

 

25. Incorporating Prior Knowledge to Increase the Power of Genome-Wide Association Studies

            Ashley Petersen, Justin Spratt, and Nathan L. Tintle

 

26. Genomic Selection in Animal Breeding Programs

            Julius van der Werf

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