Genome-Wide Association Studies and Genomic Prediction by Cedric Gondro, Julius Van Der Werf, Ben Hayes , Humana

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Humana Genome-Wide Association Studies and Genomic Prediction by Cedric Gondro, Julius Van Der Werf, Ben Hayes

With the detailed genomic information that is now becoming available, we have a plethora of data that allows researchers to address questions in a variety of areas. Genome-wide association studies (GWAS) have become a vital approach to identify candidate regions associated with complex diseases in human medicine, production traits in agriculture, and variation in wild populations. Genomic prediction goes a step further, attempting to predict phenotypic variation in these traits from genomic information. Genome-Wide Association Studies and Genomic Prediction pulls together expert contributions to address this important area of study. The volume begins with a section covering the phenotypes of interest as well as design issues for GWAS, then moves on to discuss efficient computational methods to store and handle large datasets, quality control measures, phasing, haplotype inference, and imputation. Later chapters deal with statistical approaches to data analysis where the experimental objective is either to confirm the biology by identifying genomic regions associated to a trait or to use the data to make genomic predictions about a future phenotypic outcome (e.g. predict onset of disease). As part of the Methods in Molecular Biology series, chapters provide helpful, real-world implementation advice._x000D_ Table of contents : - _x000D_ 1. R for Genome-Wide Association Studies_x000D_ _x000D_ Cedric Gondro, Laercio R. Porto-Neto, and Seung Hwan Lee_x000D_ _x000D_ _x000D_ _x000D_ 2. Descriptive Statistics of Data: Understanding the Data Set and Phenotypes of Interest_x000D_ _x000D_ Sonja Dominik_x000D_ _x000D_ _x000D_ _x000D_ 3. Designing a Genome-Wide Association Studies (GWAS): Power, Sample Size, and Data Structure_x000D_ _x000D_ Roderick D. Ball_x000D_ _x000D_ _x000D_ _x000D_ 4. Managing Large SNP Datasets with SNPpy_x000D_ _x000D_ Faheem Mitha_x000D_ _x000D_ _x000D_ _x000D_ 5. Quality Control for Genome-Wide Association Studies_x000D_ _x000D_ Cedric Gondro, Seung Hwan Lee, Hak Kyo Lee, and Laercio R. Porto-Neto_x000D_ _x000D_ _x000D_ _x000D_ 6. Overview of Statistical Methods for Genome-Wide Association Studies (GWAS)_x000D_ _x000D_ Ben Hayes_x000D_ _x000D_ _x000D_ _x000D_ 7. Statistical Analysis of Genomic Data_x000D_ _x000D_ Roderick D. Ball_x000D_ _x000D_ _x000D_ _x000D_ 8. Using PLINK for Genome-Wide Association Studies (GWAS) and Data Analysis_x000D_ _x000D_ Miguel E. Renteria, Adrian Cortes, and Sarah E. Medland_x000D_ _x000D_ _x000D_ _x000D_ 9. Genome-Wide Complex Trait Analysis (GCTA): Methods, Data Analyses, and Interpretations_x000D_ _x000D_ Jian Yang, Sang Hong Lee, Michael E. Goddard, and Peter M. Visscher_x000D_ _x000D_ _x000D_ _x000D_ 10. Bayesian Methods Applied to Genome-Wide Association Studies (GWAS)_x000D_ _x000D_ Rohan L. Fernando and Dorian J. Garrick_x000D_ _x000D_ _x000D_ _x000D_ 11. Implementing a QTL Detection Study (GWAS) Using Genomic Prediction Methodology_x000D_ _x000D_ Dorian J. Garrick and Rohan L. Fernando_x000D_ _x000D_ _x000D_ _x000D_ 12. Genome-Enabled Prediction Using the BLR (Bayesian Linear Regression) R-Package_x000D_ _x000D_ Gustavo de los Campos, Paulino Perez, Ana I. Vazquez, and Jose Crossa_x000D_ _x000D_ _x000D_ _x000D_ 13. Genomic Best Linear Unbiased Prediction (gBLUP) for the Estimation of Genomic Breeding Values_x000D_ _x000D_ Samuel A. Clark and Julius van der Werf_x000D_ _x000D_ _x000D_ _x000D_ 14. Detecting Regions of Homozygosity to Map the Cause of Recessively Inherited Disease_x000D_ _x000D_ James W. Kijas_x000D_ _x000D_ _x000D_ _x000D_ 15. Use of Ancestral Haplotypes in Genome-Wide Association Studies_x000D_ _x000D_ Tom Druet and Frederic Farnir_x000D_ _x000D_ _x000D_ _x000D_ 16. Genotype Phasing in Populations of Closely Related Individuals_x000D_ _x000D_ John M. Hickey_x000D_ _x000D_ _x000D_ _x000D_ 17. Genotype Imputation to Increase Sample Size in Pedigreed Populations_x000D_ _x000D_ John M. Hickey, Matthew A. Cleveland, Christian Maltecca, Gregor Gorjanc, Birgit Gredler, and Andreas Kranis_x000D_ _x000D_ _x000D_ _x000D_ 18. Validation of Genome-Wide Association Studies (GWAS) Results_x000D_ _x000D_ John M. Henshall_x000D_ _x000D_ _x000D_ _x000D_ 19. Detection of Signatures of Selection Using FST_x000D_ _x000D_ Laercio R. Porto-Neto, Seung Hwan Lee, Hak Kyo Lee, and Cedric Gondro_x000D_ _x000D_ _x000D_ _x000D_ 20. Association Weight Matrix: A Network-Based Approach Towards Functional Genome-Wide Association Studies_x000D_ _x000D_ Antonio Reverter and Marina R.S. Fortes_x000D_ _x000D_ _x000D_ _x000D_ 21. Mixed Effects Structural Equation Models and Phenotypic Causal Networks_x000D_ _x000D_ Bruno Dourado Valente and Guilherme Jordao de Magalhaes Rosa_x000D_ _x000D_ _x000D_ _x000D_ 22. Epistasis, Complexity, and Multifactor Dimensionality Reduction_x000D_ _x000D_ Qinxin Pan, Ting Hu, and Jason H. Moore_x000D_ _x000D_ _x000D_ _x000D_ 23. Applications of Multifactor Dimensionality Reduction to Genome-Wide Data Using the R Package 'MDR'_x000D_ _x000D_ Stacey Winham_x000D_ _x000D_ _x000D_ _x000D_ 24. Higher Order Interactions: Detection of Epistasis Using Machine Learning and Evolutionary Computation_x000D_ _x000D_ Ronald M. Nelson, Marcin Kierczak, and OErjan Carlborg_x000D_ _x000D_ _x000D_ _x000D_ 25. Incorporating Prior Knowledge to Increase the Power of Genome-Wide Association Studies_x000D_ _x000D_ Ashley Petersen, Justin Spratt, and Nathan L. Tintle_x000D_ _x000D_ _x000D_ _x000D_ 26. Genomic Selection in Animal Breeding Programs_x000D_ _x000D_ Julius van der Werf_x000D_