Genome-Wide Association Studies and Genomic Prediction

Table of Contents

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   Book Overview
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   Chapter 1 R for genome-wide association studies.
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   Chapter 2 Descriptive statistics of data: understanding the data set and phenotypes of interest.
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   Chapter 3 Designing a GWAS: Power, Sample Size, and Data Structure.
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   Chapter 4 Managing Large SNP Datasets with SNPpy.
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   Chapter 5 Quality control for genome-wide association studies.
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   Chapter 6 Overview of Statistical Methods for Genome-Wide Association Studies (GWAS).
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   Chapter 7 Statistical analysis of genomic data.
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   Chapter 8 Using PLINK for Genome-Wide Association Studies (GWAS) and Data Analysis.
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    Chapter 9 Genome-Wide Complex Trait Analysis (GCTA): Methods, Data Analyses, and Interpretations
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    Chapter 10 Bayesian Methods Applied to GWAS.
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    Chapter 11 Implementing a QTL Detection Study (GWAS) Using Genomic Prediction Methodology.
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    Chapter 12 Genome-Enabled Prediction Using the BLR (Bayesian Linear Regression) R-Package.
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    Chapter 13 Genomic Best Linear Unbiased Prediction (gBLUP) for the Estimation of Genomic Breeding Values.
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    Chapter 14 Detecting regions of homozygosity to map the cause of recessively inherited disease.
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    Chapter 15 Use of ancestral haplotypes in genome-wide association studies.
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    Chapter 16 Genotype phasing in populations of closely related individuals.
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    Chapter 17 Genotype Imputation to Increase Sample Size in Pedigreed Populations
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    Chapter 18 Validation of Genome-Wide Association Studies (GWAS) Results.
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    Chapter 19 Detection of Signatures of Selection Using F ST.
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    Chapter 20 Association weight matrix: a network-based approach towards functional genome-wide association studies.
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    Chapter 21 Mixed effects structural equation models and phenotypic causal networks.
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    Chapter 22 Epistasis, complexity, and multifactor dimensionality reduction.
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    Chapter 23 Applications of Multifactor Dimensionality Reduction to Genome-Wide Data Using the R Package 'MDR'.
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    Chapter 24 Higher order interactions: detection of epistasis using machine learning and evolutionary computation.
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    Chapter 25 Incorporating prior knowledge to increase the power of genome-wide association studies.
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    Chapter 26 Genome-Wide Association Studies and Genomic Prediction