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. 2014 Mar 1;30(5):698-705.
doi: 10.1093/bioinformatics/btt572. Epub 2013 Oct 21.

ATHENA: the analysis tool for heritable and environmental network associations

Emily R Holzinger  1 , Scott M Dudek, Alex T Frase, Sarah A Pendergrass, Marylyn D Ritchie
Affiliations

ATHENA: the analysis tool for heritable and environmental network associations

Emily R Holzinger et al. Bioinformatics. .

Abstract

Motivation: Advancements in high-throughput technology have allowed researchers to examine the genetic etiology of complex human traits in a robust fashion. Although genome-wide association studies have identified many novel variants associated with hundreds of traits, a large proportion of the estimated trait heritability remains unexplained. One hypothesis is that the commonly used statistical techniques and study designs are not robust to the complex etiology that may underlie these human traits. This etiology could include non-linear gene × gene or gene × environment interactions. Additionally, other levels of biological regulation may play a large role in trait variability.

Results: To address the need for computational tools that can explore enormous datasets to detect complex susceptibility models, we have developed a software package called the Analysis Tool for Heritable and Environmental Network Associations (ATHENA). ATHENA combines various variable filtering methods with machine learning techniques to analyze high-throughput categorical (i.e. single nucleotide polymorphisms) and quantitative (i.e. gene expression levels) predictor variables to generate multivariable models that predict either a categorical (i.e. disease status) or quantitative (i.e. cholesterol levels) outcomes. The goal of this article is to demonstrate the utility of ATHENA using simulated and biological datasets that consist of both single nucleotide polymorphisms and gene expression variables to identify complex prediction models. Importantly, this method is flexible and can be expanded to include other types of high-throughput data (i.e. RNA-seq data and biomarker measurements).

Availability: ATHENA is freely available for download. The software, user manual and tutorial can be downloaded from http://ritchielab.psu.edu/ritchielab/software.

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Figures

Fig. 1.
Fig. 1.
ATHENA filtering and modeling components
Fig. 2.
Fig. 2.
Schematic of the three meta-dimensional genetic effect models. From left to right: main effect model, SNP × SNP + EV (S × S + E), SNP × EV (S × E). SNP.IND had an indirect effect on the phenotype via its correlation with the EV
Fig. 3.
Fig. 3.
Results from the SNP-only simulation analyses. Description of the 14 genetic effect models is shown in the first three columns. Detection power is defined as the number of times out of 100 datasets the indicated variable(s) is identified. Avg. Fitness is defined as balanced accuracy. Avg. Model Size is defined as the average number of variables in the best model
Fig. 4.
Fig. 4.
Results from the meta-dimensional simulation analyses. Description of the 14 genetic effect models is shown in the first three columns. Detection power is defined as the number of times out of 100 datasets the indicated variable(s) is identified. Avg. Fitness is defined as R2. Avg. Model Size is defined as the average number of variables in the best model
Fig. 5.
Fig. 5.
Schematic of the filtering-modeling pipeline for the biological dataset analysis
See this image and copyright information in PMC

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