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Characterizing gene-gene interactions in a statistical epistasis network of twelve candidate genes for obesity

Overview of attention for article published in BioData Mining, December 2015
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2 tweeters

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16 Dimensions

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38 Mendeley
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Title
Characterizing gene-gene interactions in a statistical epistasis network of twelve candidate genes for obesity
Published in
BioData Mining, December 2015
DOI 10.1186/s13040-015-0077-x
Pubmed ID
Authors

Rishika De, Ting Hu, Jason H. Moore, Diane Gilbert-Diamond

Abstract

Recent findings have reemphasized the importance of epistasis, or gene-gene interactions, as a contributing factor to the unexplained heritability of obesity. Network-based methods such as statistical epistasis networks (SEN), present an intuitive framework to address the computational challenge of studying pairwise interactions between thousands of genetic variants. In this study, we aimed to analyze pairwise interactions that are associated with Body Mass Index (BMI) between SNPs from twelve genes robustly associated with obesity (BDNF, ETV5, FAIM2, FTO, GNPDA2, KCTD15, MC4R, MTCH2, NEGR1, SEC16B, SH2B1, and TMEM18). We used information gain measures to identify all SNP-SNP interactions among and between these genes that were related to obesity (BMI > 30 kg/m(2)) within the Framingham Heart Study Cohort; interactions exceeding a certain threshold were used to build an SEN. We also quantified whether interactions tend to occur more between SNPs from the same gene (dyadicity) or between SNPs from different genes (heterophilicity). We identified a highly connected SEN of 709 SNPs and 1241 SNP-SNP interactions. Combining the SEN framework with dyadicity and heterophilicity analyses, we found 1 dyadic gene (TMEM18, P-value = 0.047) and 3 heterophilic genes (KCTD15, P-value = 0.045; SH2B1, P-value = 0.003; and TMEM18, P-value = 0.001). We also identified a lncRNA SNP (rs4358154) as a key node within the SEN using multiple network measures. This study presents an analytical framework to characterize the global landscape of genetic interactions from genome-wide arrays and also to discover nodes of potential biological significance within the identified network.

Twitter Demographics

The data shown below were collected from the profiles of 2 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 38 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Switzerland 1 3%
Unknown 37 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 24%
Researcher 6 16%
Student > Bachelor 5 13%
Professor > Associate Professor 3 8%
Student > Master 3 8%
Other 8 21%
Unknown 4 11%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 10 26%
Agricultural and Biological Sciences 6 16%
Computer Science 6 16%
Pharmacology, Toxicology and Pharmaceutical Science 2 5%
Medicine and Dentistry 2 5%
Other 5 13%
Unknown 7 18%

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 31 December 2015.
All research outputs
#11,149,324
of 17,800,904 outputs
Outputs from BioData Mining
#204
of 277 outputs
Outputs of similar age
#193,756
of 378,757 outputs
Outputs of similar age from BioData Mining
#1
of 1 outputs
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