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A set-based association test identifies sex-specific gene sets associated with type 2 diabetes

Overview of attention for article published in Frontiers in Genetics, November 2014
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2 X users
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1 Google+ user

Citations

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10 Dimensions

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15 Mendeley
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Title
A set-based association test identifies sex-specific gene sets associated with type 2 diabetes
Published in
Frontiers in Genetics, November 2014
DOI 10.3389/fgene.2014.00395
Pubmed ID
Authors

Tao He, Ping-Shou Zhong, Yuehua Cui

Abstract

Single variant analysis in genome-wide association studies (GWAS) has been proven to be successful in identifying thousands of genetic variants associated with hundreds of complex diseases. However, these identified variants only explain a small fraction of inheritable variability in many diseases, suggesting that other resources, such as multilevel genetic variations, may contribute to disease susceptibility. In this work, we proposed to combine genetic variants that belong to a gene set, such as at gene- and pathway-level to form an integrated signal aimed to identify major players that function in a coordinated manner conferring disease risk. The integrated analysis provides novel insight into disease etiology while individual signals could be easily missed by single variant analysis. We applied our approach to a genome-wide association study of type 2 diabetes (T2D) with male and female data analyzed separately. Novel sex-specific genes and pathways were identified to increase the risk of T2D. We also demonstrated the performance of signal integration through simulation studies.

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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 15 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 15 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 13%
Librarian 1 7%
Other 1 7%
Student > Bachelor 1 7%
Lecturer 1 7%
Other 2 13%
Unknown 7 47%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 2 13%
Agricultural and Biological Sciences 2 13%
Social Sciences 1 7%
Medicine and Dentistry 1 7%
Chemistry 1 7%
Other 1 7%
Unknown 7 47%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 18 November 2014.
All research outputs
#13,923,205
of 22,770,070 outputs
Outputs from Frontiers in Genetics
#3,509
of 11,758 outputs
Outputs of similar age
#130,190
of 258,738 outputs
Outputs of similar age from Frontiers in Genetics
#58
of 107 outputs
Altmetric has tracked 22,770,070 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 11,758 research outputs from this source. They receive a mean Attention Score of 3.7. This one has gotten more attention than average, scoring higher than 67% 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 258,738 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 107 others from the same source and published within six weeks on either side of this one. This one is in the 38th percentile – i.e., 38% of its contemporaries scored the same or lower than it.