Genome-Wide Association Studies and Genomic Prediction

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. Renteria, 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 Perez, Ana I. Vazquez, and Jose 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 Frederic 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 FST 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 Jordao de Magalhaes 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 Orjan 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