Advancements in high-throughput technology have allowed researchers to examine the genetic etiology of complex human traits in a robust fashion. While genome-wide association studies (GWAS) 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 x gene or gene x environment interactions. Additionally, other levels of biological regulation may play a large role in trait variability.
In order to address the need for computational tools that can explore enormous data sets to detect complex susceptibility models, researchers at the National Human Genome Research Institute and Penn State 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 in order to analyze high-throughput categorical (i.e. SNPs) and quantitative (i.e. gene expression levels) predictor variables to generate multi-variable models that predict either a categorical (i.e. disease status) or quantitative (i.e. cholesterol levels) outcomes. The researchers demonstrate the utility of ATHENA using simulated and biological data sets that consist of both SNPs 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.
- R Holzinger E, M Dudek S, T Frase A, A Pendergrass S, D Ritchie M. (2013) ATHENA: The Analysis Tool for Heritable and Environmental Network Associations. Bioinformatics [Epub ahead of print]. [abstract]