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Genome-Wide Association Studies and Genomic Prediction

Overview of attention for book
Cover of 'Genome-Wide Association Studies and Genomic Prediction'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    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.
  27. Altmetric Badge
    Chapter 26 Genome-Wide Association Studies and Genomic Prediction
Attention for Chapter 26: Genome-Wide Association Studies and Genomic Prediction
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Chapter title
Genome-Wide Association Studies and Genomic Prediction
Chapter number 26
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_26
Pubmed ID
23756910
Book ISBNs
978-1-62703-446-3, 978-1-62703-447-0
Authors

van der Werf J, Julius van der Werf

Editors

Cedric Gondro, Julius van der Werf, Ben Hayes

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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 69 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 3%
Colombia 1 1%
Unknown 66 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 19 28%
Student > Master 11 16%
Researcher 9 13%
Student > Doctoral Student 4 6%
Lecturer 4 6%
Other 10 14%
Unknown 12 17%
Readers by discipline Count As %
Agricultural and Biological Sciences 36 52%
Biochemistry, Genetics and Molecular Biology 10 14%
Veterinary Science and Veterinary Medicine 3 4%
Computer Science 3 4%
Mathematics 1 1%
Other 3 4%
Unknown 13 19%
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 27 November 2016.
All research outputs
#18,483,671
of 22,903,988 outputs
Outputs from Methods in molecular biology
#7,927
of 13,133 outputs
Outputs of similar age
#145,801
of 193,952 outputs
Outputs of similar age from Methods in molecular biology
#20
of 32 outputs
Altmetric has tracked 22,903,988 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,133 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 24th percentile – i.e., 24% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 193,952 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 12th percentile – i.e., 12% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 32 others from the same source and published within six weeks on either side of this one. This one is in the 18th percentile – i.e., 18% of its contemporaries scored the same or lower than it.

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