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
F_ST
. - 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.