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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

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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
Overall attention for this book and its chapters
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (93rd percentile)
  • High Attention Score compared to outputs of the same age and source (96th percentile)

Mentioned by

blogs
1 blog
policy
1 policy source
twitter
8 X users
q&a
1 Q&A thread

Citations

dimensions_citation
100 Dimensions

Readers on

mendeley
966 Mendeley
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Title
Genome-Wide Association Studies and Genomic Prediction
Published by
Methods in molecular biology, January 2013
DOI 10.1007/978-1-62703-447-0
ISBNs
978-1-62703-446-3, 978-1-62703-447-0, 978-1-4939-5964-8
Editors

Cedric Gondro, Julius van der Werf, Ben Hayes

X Demographics

X Demographics

The data shown below were collected from the profiles of 8 X users 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 966 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 11 1%
United States 8 <1%
United Kingdom 4 <1%
France 3 <1%
Netherlands 3 <1%
Mexico 2 <1%
Belgium 2 <1%
Germany 1 <1%
Israel 1 <1%
Other 6 <1%
Unknown 925 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 240 25%
Researcher 172 18%
Student > Master 135 14%
Student > Bachelor 78 8%
Student > Doctoral Student 71 7%
Other 122 13%
Unknown 148 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 479 50%
Biochemistry, Genetics and Molecular Biology 135 14%
Medicine and Dentistry 43 4%
Computer Science 27 3%
Psychology 13 1%
Other 90 9%
Unknown 179 19%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 19. 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 07 February 2023.
All research outputs
#2,011,773
of 26,017,215 outputs
Outputs from Methods in molecular biology
#273
of 14,425 outputs
Outputs of similar age
#18,402
of 295,070 outputs
Outputs of similar age from Methods in molecular biology
#12
of 351 outputs
Altmetric has tracked 26,017,215 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 14,425 research outputs from this source. They receive a mean Attention Score of 3.5. This one has done particularly well, scoring higher than 97% 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 295,070 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 93% of its contemporaries.
We're also able to compare this research output to 351 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 96% of its contemporaries.