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GxGrare: gene-gene interaction analysis method for rare variants from high-throughput sequencing data

Overview of attention for article published in BMC Systems Biology, March 2018
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  • Above-average Attention Score compared to outputs of the same age (51st percentile)
  • High Attention Score compared to outputs of the same age and source (80th percentile)

Mentioned by

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5 tweeters

Citations

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7 Dimensions

Readers on

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31 Mendeley
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Title
GxGrare: gene-gene interaction analysis method for rare variants from high-throughput sequencing data
Published in
BMC Systems Biology, March 2018
DOI 10.1186/s12918-018-0543-4
Pubmed ID
Authors

Minseok Kwon, Sangseob Leem, Joon Yoon, Taesung Park

Abstract

With the rapid advancement of array-based genotyping techniques, genome-wide association studies (GWAS) have successfully identified common genetic variants associated with common complex diseases. However, it has been shown that only a small proportion of the genetic etiology of complex diseases could be explained by the genetic factors identified from GWAS. This missing heritability could possibly be explained by gene-gene interaction (epistasis) and rare variants. There has been an exponential growth of gene-gene interaction analysis for common variants in terms of methodological developments and practical applications. Also, the recent advancement of high-throughput sequencing technologies makes it possible to conduct rare variant analysis. However, little progress has been made in gene-gene interaction analysis for rare variants. Here, we propose GxGrare which is a new gene-gene interaction method for the rare variants in the framework of the multifactor dimensionality reduction (MDR) analysis. The proposed method consists of three steps; 1) collapsing the rare variants, 2) MDR analysis for the collapsed rare variants, and 3) detect top candidate interaction pairs. GxGrare can be used for the detection of not only gene-gene interactions, but also interactions within a single gene. The proposed method is illustrated with 1080 whole exome sequencing data of the Korean population in order to identify causal gene-gene interaction for rare variants for type 2 diabetes. The proposed GxGrare performs well for gene-gene interaction detection with collapsing of rare variants. GxGrare is available at http://bibs.snu.ac.kr/software/gxgrare which contains simulation data and documentation. Supported operating systems include Linux and OS X.

Twitter Demographics

The data shown below were collected from the profiles of 5 tweeters 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 31 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 31 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 26%
Student > Master 7 23%
Researcher 6 19%
Student > Doctoral Student 3 10%
Unspecified 2 6%
Other 0 0%
Unknown 5 16%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 7 23%
Agricultural and Biological Sciences 6 19%
Medicine and Dentistry 3 10%
Computer Science 2 6%
Unspecified 1 3%
Other 4 13%
Unknown 8 26%

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 28 January 2019.
All research outputs
#9,154,984
of 16,534,657 outputs
Outputs from BMC Systems Biology
#427
of 1,106 outputs
Outputs of similar age
#133,801
of 284,400 outputs
Outputs of similar age from BMC Systems Biology
#6
of 31 outputs
Altmetric has tracked 16,534,657 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,106 research outputs from this source. They receive a mean Attention Score of 3.4. This one has gotten more attention than average, scoring higher than 58% 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 284,400 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 51% of its contemporaries.
We're also able to compare this research output to 31 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 80% of its contemporaries.