Genome-Wide Association Studies and Genomic Prediction

ISBN-13: 9781493959648 / Angielski / Miękka / 2017 / 566 str.

Cedric Gondro; Julius Van Der Werf; Ben Hayes

Opis

This Methods in Molecular Biology(TM) book covers phenotypes of interest as well as design issues for GWAS, methods for storing and handling large datasets, quality control measures, haplotype inference, and imputation and statistical approaches to data analysis.

Recenzje

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)

Spis treści

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 F_ST

            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

Blurb

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.