ATHENA: the analysis tool for heritable and environmental network associations
Open accessAbstract
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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ATHENA was developed for analysing multi-omics data.
“In the past years, ATHENA (Analysis Tool for Heritable and Environmental Network Associations) was developed for analysing multi-omics data (Chung and Kang, 2019;Holzinger et al., 2014).”
From Using machine learning approaches for multi-omics data analysis: A review (2021) · cites this paper as Holzinger et al., 2014
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- Authors
- Holzinger, E. R., Dudek, S. M., Frase, A. T., Pendergrass, S. A., & Ritchie, M. D.
- Affiliations
- National Institutes of Health (US)
- Corresponding author
- Ritchie, Marylyn D.
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2013-10-21
- Volume
- 30
- Issue
- 5
- Pages
- 698-705
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC3933870
- OpenAlex
- W2101235384
- Cited by
- 54
- References
- 35
- Citations by year
- 2026: 1 · 2024: 5 · 2022: 1 · 2021: 9 · 2020: 3 · 2019: 7 · 2018: 7 · 2017: 6 · 2016: 6 · 2015: 6 · 2014: 2 · 2013: 1
- FWCI
- 3.31
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Bioinformatics (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health
- National Heart, Lung, and Blood Institute (U19HL065962, HL065962, U01HL065962)
- National Institute of General Medical Sciences (HL065962, T32GM080178)
- U.S. National Library of Medicine (LM010040, R01LM010040)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Keywords
- gene-environment interaction, gene-gene interaction, single nucleotide polymorphisms, gene expression, multivariable prediction models, machine learning, genetic susceptibility, cholesterol levels, disease status prediction
- MeSH terms
- Humans, Phenotype, Software, Polymorphism, Single Nucleotide, Genome-Wide Association Study, Gene-Environment Interaction
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