Main description:

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.

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. Renteria, 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 Perez, Ana I. Vazquez, and Jose 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 Frederic 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 Jordao de Magalhaes 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 OErjan 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