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Genome-Wide Association Studies and Genomic Prediction

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Cover of 'Genome-Wide Association Studies and Genomic Prediction'

Table of Contents

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    Book Overview
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    Chapter 1 R for genome-wide association studies.
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    Chapter 2 Descriptive statistics of data: understanding the data set and phenotypes of interest.
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    Chapter 3 Designing a GWAS: Power, Sample Size, and Data Structure.
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    Chapter 4 Managing Large SNP Datasets with SNPpy.
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    Chapter 5 Quality control for genome-wide association studies.
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    Chapter 6 Overview of Statistical Methods for Genome-Wide Association Studies (GWAS).
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    Chapter 7 Statistical analysis of genomic data.
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    Chapter 8 Using PLINK for Genome-Wide Association Studies (GWAS) and Data Analysis.
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    Chapter 9 Genome-Wide Complex Trait Analysis (GCTA): Methods, Data Analyses, and Interpretations
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    Chapter 10 Bayesian Methods Applied to GWAS.
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    Chapter 11 Implementing a QTL Detection Study (GWAS) Using Genomic Prediction Methodology.
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    Chapter 12 Genome-Enabled Prediction Using the BLR (Bayesian Linear Regression) R-Package.
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    Chapter 13 Genomic Best Linear Unbiased Prediction (gBLUP) for the Estimation of Genomic Breeding Values.
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    Chapter 14 Detecting regions of homozygosity to map the cause of recessively inherited disease.
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    Chapter 15 Use of ancestral haplotypes in genome-wide association studies.
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    Chapter 16 Genotype phasing in populations of closely related individuals.
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    Chapter 17 Genotype Imputation to Increase Sample Size in Pedigreed Populations
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    Chapter 18 Validation of Genome-Wide Association Studies (GWAS) Results.
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    Chapter 19 Detection of Signatures of Selection Using F ST.
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    Chapter 20 Association weight matrix: a network-based approach towards functional genome-wide association studies.
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    Chapter 21 Mixed effects structural equation models and phenotypic causal networks.
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    Chapter 22 Epistasis, complexity, and multifactor dimensionality reduction.
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    Chapter 23 Applications of Multifactor Dimensionality Reduction to Genome-Wide Data Using the R Package 'MDR'.
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    Chapter 24 Higher order interactions: detection of epistasis using machine learning and evolutionary computation.
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    Chapter 25 Incorporating prior knowledge to increase the power of genome-wide association studies.
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    Chapter 26 Genome-Wide Association Studies and Genomic Prediction
Attention for Chapter 23: Applications of Multifactor Dimensionality Reduction to Genome-Wide Data Using the R Package 'MDR'.
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Chapter title
Applications of Multifactor Dimensionality Reduction to Genome-Wide Data Using the R Package 'MDR'.
Chapter number 23
Book title
Genome-Wide Association Studies and Genomic Prediction
Published in
Methods in molecular biology, May 2013
DOI 10.1007/978-1-62703-447-0_23
Pubmed ID
Book ISBNs
978-1-62703-446-3, 978-1-62703-447-0
Authors

Winham S, Stacey Winham

Editors

Cedric Gondro, Julius van der Werf, Ben Hayes

Abstract

This chapter describes how to use the R package 'MDR' to search and identify gene-gene interactions in high-dimensional data and illustrates applications for exploratory analysis of multi-locus models by providing specific examples.

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 21 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Colombia 1 5%
United States 1 5%
Germany 1 5%
Unknown 18 86%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 19%
Researcher 4 19%
Student > Master 4 19%
Student > Doctoral Student 2 10%
Lecturer 1 5%
Other 1 5%
Unknown 5 24%
Readers by discipline Count As %
Agricultural and Biological Sciences 7 33%
Computer Science 5 24%
Mathematics 1 5%
Pharmacology, Toxicology and Pharmaceutical Science 1 5%
Biochemistry, Genetics and Molecular Biology 1 5%
Other 1 5%
Unknown 5 24%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 21 June 2013.
All research outputs
#18,340,605
of 22,712,476 outputs
Outputs from Methods in molecular biology
#7,851
of 13,079 outputs
Outputs of similar age
#145,535
of 193,693 outputs
Outputs of similar age from Methods in molecular biology
#20
of 32 outputs
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