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Imputation of coding variants in African Americans: better performance using data from the exome sequencing project

Overview of attention for article published in Bioinformatics, August 2013
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Article details
Title
Imputation of coding variants in African Americans: better performance using data from the exome sequencing project
Published in
Bioinformatics, August 2013
DOI 10.1093/bioinformatics/btt477
Pubmed ID
Authors
Abstract

Although the 1000 Genomes haplotypes are the most commonly used reference panel for imputation, medical sequencing projects are generating large alternate sets of sequenced samples. Imputation in African Americans using 3384 haplotypes from the Exome Sequencing Project, compared with 2184 haplotypes from 1000 Genomes Project, increased effective sample size by 8.3-11.4% for coding variants with minor allele frequency <1%. No loss of imputation quality was observed using a panel built from phenotypic extremes. We recommend using haplotypes from Exome Sequencing Project alone or concatenation of the two panels over quality score-based post-imputation selection or IMPUTE2's two-panel combination.

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Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 46 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
United States 1 2%
New Zealand 1 2%
Ireland 1 2%
United Kingdom 1 2%
Germany 1 2%
Unknown 41 89%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Researcher 14 30%
Student > Ph. D. Student 10 22%
Professor > Associate Professor 7 15%
Student > Doctoral Student 3 7%
Professor 3 7%
Other 6 13%
Unknown 3 7%
Readers by discipline
Readers by discipline Count As %
Agricultural and Biological Sciences 25 54%
Biochemistry, Genetics and Molecular Biology 7 15%
Mathematics 3 7%
Medicine and Dentistry 3 7%
Computer Science 2 4%
Other 4 9%
Unknown 2 4%
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 30 October 2013.
All research outputs
#23,381,540
of 28,573,925 outputs
Outputs from Bioinformatics
#9,855
of 10,815 outputs
Outputs of similar age
#154,637
of 200,110 outputs
Outputs of similar age from Bioinformatics
#125
of 137 outputs
Altmetric has tracked 28,573,925 research outputs across all sources so far. This one is in the 9th percentile – i.e., 9% of other outputs scored the same or lower than it.
So far Altmetric has tracked 10,815 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.3. This one is in the 3rd percentile – i.e., 3% of its peers scored the same or lower than it.
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