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VARPRISM: incorporating variant prioritization in tests of de novo mutation association

Overview of attention for article published in Genome Medicine, August 2016
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Title
VARPRISM: incorporating variant prioritization in tests of de novo mutation association
Published in
Genome Medicine, August 2016
DOI 10.1186/s13073-016-0341-9
Pubmed ID
Authors

Hao Hu, Hilary Coon, Man Li, Mark Yandell, Chad D. Huff

Abstract

Patients with certain genetic diseases, such as autism spectrum disorder, have increased rates of de novo mutations within some protein-coding genes. We introduce the VARiant PRIoritization SuM (VARPRISM), a software package which incorporates functional variant prioritization information to improve the power to detect de novo mutations influencing disease risk. VARPRISM evaluates the consequence of any given exonic mutation on the protein sequence to estimate the likelihood that the mutation is benign or damaging and conducts a likelihood ratio test on the gene level. We analyzed the Simons Simplex Collection of 2508 parent-offspring autism trios using VARPRISM, replicating 44 genes previously implicated in autism susceptibility and identifying 20 additional candidate genes, including MYO1E, KCND3, PDCD1, DLX3, and TSPAN4 (false discovery rate < 0.3). By incorporating functional predictions, VARPRISM improved the statistical power to identify de novo mutations increasing disease risks. VARPRISM is available at http://www.hufflab.org/software/VARPRISM .

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The data shown below were collected from the profiles of 2 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 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 3%
Unknown 28 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 8 28%
Student > Master 6 21%
Student > Ph. D. Student 4 14%
Student > Doctoral Student 2 7%
Student > Postgraduate 2 7%
Other 1 3%
Unknown 6 21%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 9 31%
Psychology 3 10%
Computer Science 3 10%
Mathematics 2 7%
Agricultural and Biological Sciences 2 7%
Other 4 14%
Unknown 6 21%
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 28 March 2017.
All research outputs
#16,427,366
of 24,198,461 outputs
Outputs from Genome Medicine
#1,378
of 1,497 outputs
Outputs of similar age
#223,811
of 346,248 outputs
Outputs of similar age from Genome Medicine
#24
of 25 outputs
Altmetric has tracked 24,198,461 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,497 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 26.6. This one is in the 5th percentile – i.e., 5% 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 346,248 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 26th percentile – i.e., 26% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 25 others from the same source and published within six weeks on either side of this one. This one is in the 8th percentile – i.e., 8% of its contemporaries scored the same or lower than it.