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Identifying gene-gene interactions that are highly associated with Body Mass Index using Quantitative Multifactor Dimensionality Reduction (QMDR)

Overview of attention for article published in BioData Mining, December 2015
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (83rd percentile)

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7 tweeters
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1 Wikipedia page

Citations

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14 Dimensions

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62 Mendeley
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Title
Identifying gene-gene interactions that are highly associated with Body Mass Index using Quantitative Multifactor Dimensionality Reduction (QMDR)
Published in
BioData Mining, December 2015
DOI 10.1186/s13040-015-0074-0
Pubmed ID
Authors

Rishika De, Shefali S. Verma, Fotios Drenos, Emily R. Holzinger, Michael V. Holmes, Molly A. Hall, David R. Crosslin, David S. Carrell, Hakon Hakonarson, Gail Jarvik, Eric Larson, Jennifer A. Pacheco, Laura J. Rasmussen-Torvik, Carrie B. Moore, Folkert W. Asselbergs, Jason H. Moore, Marylyn D. Ritchie, Brendan J. Keating, Diane Gilbert-Diamond

Abstract

Despite heritability estimates of 40-70 % for obesity, less than 2 % of its variation is explained by Body Mass Index (BMI) associated loci that have been identified so far. Epistasis, or gene-gene interactions are a plausible source to explain portions of the missing heritability of BMI. Using genotypic data from 18,686 individuals across five study cohorts - ARIC, CARDIA, FHS, CHS, MESA - we filtered SNPs (Single Nucleotide Polymorphisms) using two parallel approaches. SNPs were filtered either on the strength of their main effects of association with BMI, or on the number of knowledge sources supporting a specific SNP-SNP interaction in the context of BMI. Filtered SNPs were specifically analyzed for interactions that are highly associated with BMI using QMDR (Quantitative Multifactor Dimensionality Reduction). QMDR is a nonparametric, genetic model-free method that detects non-linear interactions associated with a quantitative trait. We identified seven novel, epistatic models with a Bonferroni corrected p-value of association < 0.1. Prior experimental evidence helps explain the plausible biological interactions highlighted within our results and their relationship with obesity. We identified interactions between genes involved in mitochondrial dysfunction (POLG2), cholesterol metabolism (SOAT2), lipid metabolism (CYP11B2), cell adhesion (EZR), cell proliferation (MAP2K5), and insulin resistance (IGF1R). Moreover, we found an 8.8 % increase in the variance in BMI explained by these seven SNP-SNP interactions, beyond what is explained by the main effects of an index FTO SNP and the SNPs within these interactions. We also replicated one of these interactions and 58 proxy SNP-SNP models representing it in an independent dataset from the eMERGE study. This study highlights a novel approach for discovering gene-gene interactions by combining methods such as QMDR with traditional statistics.

Twitter Demographics

The data shown below were collected from the profiles of 7 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 62 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 3%
Ireland 1 2%
Unknown 59 95%

Demographic breakdown

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

Attention Score in Context

This research output has an Altmetric Attention Score of 9. 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 14 May 2017.
All research outputs
#3,434,545
of 21,423,731 outputs
Outputs from BioData Mining
#86
of 300 outputs
Outputs of similar age
#67,147
of 404,674 outputs
Outputs of similar age from BioData Mining
#1
of 1 outputs
Altmetric has tracked 21,423,731 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 300 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.9. This one has gotten more attention than average, scoring higher than 71% 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 404,674 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 83% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them