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Application of noncollapsing methods to the gene-based association test: a comparison study using Genetic Analysis Workshop 18 data.

Overview of attention for article published in BMC Proceedings, January 2014
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1 tweeter

Citations

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4 Dimensions

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8 Mendeley
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Title
Application of noncollapsing methods to the gene-based association test: a comparison study using Genetic Analysis Workshop 18 data.
Published in
BMC Proceedings, January 2014
DOI 10.1186/1753-6561-8-s1-s53
Pubmed ID
Authors

Tian-Xiao Zhang, Yi-Ran Xie, John P Rice

Abstract

Rare variants have been proposed to play a significant role in the onset and development of common diseases. However, traditional analysis methods have difficulties in detecting association signals for rare causal variants because of a lack of statistical power. We propose a two-stage, gene-based method for association mapping of rare variants by applying four different noncollapsing algorithms. Using the Genome Analysis Workshop18 whole genome sequencing data set of simulated blood pressure phenotypes, we studied and contrasted the false-positive rate of each algorithm using receiver operating characteristic curves. The statistical power of these methods was also evaluated and compared through the analysis of 200 simulated replications in a smaller genotype data set. We showed that the Fisher's method was superior to the other 3 noncollapsing methods, but was no better than the standard method implemented with famSKAT. Further investigation is needed to explore the potential statistical properties of these approaches.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 8 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 13%
Unknown 7 88%

Demographic breakdown

Readers by professional status Count As %
Professor > Associate Professor 2 25%
Student > Master 2 25%
Student > Doctoral Student 1 13%
Researcher 1 13%
Student > Bachelor 1 13%
Other 1 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 5 63%
Mathematics 1 13%
Biochemistry, Genetics and Molecular Biology 1 13%
Unspecified 1 13%

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 19 December 2014.
All research outputs
#2,412,029
of 4,691,823 outputs
Outputs from BMC Proceedings
#64
of 183 outputs
Outputs of similar age
#78,320
of 154,697 outputs
Outputs of similar age from BMC Proceedings
#3
of 13 outputs
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So far Altmetric has tracked 183 research outputs from this source. They receive a mean Attention Score of 1.7. This one is in the 43rd percentile – i.e., 43% of its peers scored the same or lower than it.
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We're also able to compare this research output to 13 others from the same source and published within six weeks on either side of this one. This one is in the 46th percentile – i.e., 46% of its contemporaries scored the same or lower than it.