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.