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Genome-Wide Association Studies and Genomic Prediction
Title
Genome-Wide Association Studies and Genomic Prediction [electronic resource] / edited by Cedric Gondro, Julius van der Werf, Ben Hayes.
ISBN
9781627034470
Publication
Totowa, NJ : Humana Press : Imprint: Humana Press, 2013.
Physical Description
1 online resource (XI, 566 p.) 67 illus., 31 illus. in color.
Local Notes
Access is available to the Yale community.
Access and use
Access restricted by licensing agreement.
Summary
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.
Variant and related titles
Springer protocols (Series)
Other formats
Printed edition:
Format
Books / Online
Language
English
Added to Catalog
August 31, 2018
Series
Methods in Molecular Biology, Methods and Protocols, 1019
Contents
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.
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