Genome-Wide Association Studies and Genomic Prediction

R for Genome-Wide Association Studies.- Descriptive Statistics of Data: Understanding the Data Set and Phenotypes of Interest.- Designing a Genome-Wide Association Studies (GWAS): Power, Sample Size, and Data Structure.- Managing Large SNP Datasets with SNPpy.- Quality Control for Genome-Wide Association Studies.- Overview of Statistical Methods for Genome-Wide Association Studies (GWAS).- Statistical Analysis of Genomic Data.- Using PLINK for Genome-Wide Association Studies (GWAS) and Data Analysis.- Genome-Wide Complex Trait Analysis (GCTA): Methods, Data Analyses, and Interpretations.- Bayesian Methods Applied to Genome-Wide Association Studies (GWAS).- Implementing a QTL Detection Study (GWAS) Using Genomic Prediction Methodology.- Genome-Enabled Prediction Using the BLR (Bayesian Linear Regression) R-Package.- Genomic Best Linear Unbiased Prediction (gBLUP) for the Estimation of Genomic Breeding Values.- Detecting Regions of Homozygosity to Map the Cause of Recessively Inherited Disease.- Use of Ancestral Haplotypes in Genome-Wide Association Studies.- Genotype Phasing in Populations of Closely Related Individuals.- Genotype Imputation to Increase Sample Size in Pedigreed Populations.- Validation of Genome-Wide Association Studies (GWAS) Results.- Detection of Signatures of Selection Using FST.- Association Weight Matrix: A Network-Based Approach Towards Functional Genome-Wide Association Studies.- Mixed Effects Structural Equation Models and Phenotypic Causal Networks.- Epistasis, Complexity, and Multifactor Dimensionality Reduction.- Applications of Multifactor Dimensionality Reduction to Genome-Wide Data Using the R Package 'MDR'.- Higher Order Interactions:Detection of Epistasis Using Machine Learning and Evolutionary Computation.- Incorporating Prior Knowledge to Increase the Power of Genome-Wide Association Studies.- Genomic Selection in Animal Breeding Programs.

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)