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SNP by SNP by environment interaction network of alcoholism

Overview of attention for article published in BMC Systems Biology, March 2017
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Title
SNP by SNP by environment interaction network of alcoholism
Published in
BMC Systems Biology, March 2017
DOI 10.1186/s12918-017-0403-7
Pubmed ID
Authors

Amin Zollanvari, Gil Alterovitz

Abstract

Alcoholism has a strong genetic component. Twin studies have demonstrated the heritability of a large proportion of phenotypic variance of alcoholism ranging from 50-80%. The search for genetic variants associated with this complex behavior has epitomized sequence-based studies for nearly a decade. The limited success of genome-wide association studies (GWAS), possibly precipitated by the polygenic nature of complex traits and behaviors, however, has demonstrated the need for novel, multivariate models capable of quantitatively capturing interactions between a host of genetic variants and their association with non-genetic factors. In this regard, capturing the network of SNP by SNP or SNP by environment interactions has recently gained much interest. Here, we assessed 3,776 individuals to construct a network capable of detecting and quantifying the interactions within and between plausible genetic and environmental factors of alcoholism. In this regard, we propose the use of first-order dependence tree of maximum weight as a potential statistical learning technique to delineate the pattern of dependencies underpinning such a complex trait. Using a predictive based analysis, we further rank the genes, demographic factors, biological pathways, and the interactions represented by our SNP [Formula: see text]SNP[Formula: see text]E network. The proposed framework is quite general and can be potentially applied to the study of other complex traits.

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

Geographical breakdown

Country Count As %
Unknown 24 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 5 21%
Researcher 5 21%
Student > Bachelor 3 13%
Student > Ph. D. Student 3 13%
Student > Postgraduate 2 8%
Other 2 8%
Unknown 4 17%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 7 29%
Neuroscience 5 21%
Medicine and Dentistry 2 8%
Agricultural and Biological Sciences 1 4%
Pharmacology, Toxicology and Pharmaceutical Science 1 4%
Other 2 8%
Unknown 6 25%

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 01 April 2017.
All research outputs
#20,412,387
of 22,962,258 outputs
Outputs from BMC Systems Biology
#1,011
of 1,144 outputs
Outputs of similar age
#268,642
of 307,967 outputs
Outputs of similar age from BMC Systems Biology
#25
of 32 outputs
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