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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 3: Designing a GWAS: Power, Sample Size, and Data Structure.
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (75th percentile)
  • High Attention Score compared to outputs of the same age and source (80th percentile)

Mentioned by

policy
1 policy source
q&a
1 Q&A thread

Citations

dimensions_citation
101 Dimensions

Readers on

mendeley
132 Mendeley
citeulike
1 CiteULike
Summary Policy documents Q&A Dimensions citations
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Chapter title
Designing a GWAS: Power, Sample Size, and Data Structure.
Chapter number 3
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_3
Pubmed ID
23756887
Book ISBNs
978-1-62703-446-3, 978-1-62703-447-0
Authors

Roderick D. Ball, Ball, Roderick D.

Editors

Cedric Gondro, Julius van der Werf, Ben Hayes

View on publisher site
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 132 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 <1%
Colombia 1 <1%
Denmark 1 <1%
Unknown 129 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 27 20%
Researcher 25 19%
Student > Master 17 13%
Student > Doctoral Student 11 8%
Student > Bachelor 8 6%
Other 13 10%
Unknown 31 23%
Readers by discipline Count As %
Agricultural and Biological Sciences 47 36%
Biochemistry, Genetics and Molecular Biology 23 17%
Medicine and Dentistry 5 4%
Psychology 4 3%
Social Sciences 4 3%
Other 13 10%
Unknown 36 27%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 01 January 2023.
All research outputs
#5,732,931
of 23,462,326 outputs
Outputs from Methods in molecular biology
#1,569
of 13,337 outputs
Outputs of similar age
#46,690
of 195,205 outputs
Outputs of similar age from Methods in molecular biology
#6
of 31 outputs
Altmetric has tracked 23,462,326 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 13,337 research outputs from this source. They receive a mean Attention Score of 3.4. This one has done well, scoring higher than 88% of its peers.
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 195,205 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 75% of its contemporaries.
We're also able to compare this research output to 31 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 80% of its contemporaries.

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