ATHENA Optimization: The Effect of Initial Parameter Settings across Different Genetic Models

Holzinger, Emily R.; Dudek, Scott M.; Torstenson, Eric; Ritchie, Marylyn D.

doi:10.1007/978-3-642-20389-3_5

Cited by 5 publications

(2 citation statements)

References 21 publications

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“…Dummy configuration files showing specific parameters for the simulated and biological dataset analyses are shown in Supplementary Table S3. These parameter settings were chosen based on a series of previous optimization tests that were done to identify the settings that performed best across various genetic models (Holzinger et al, 2010(Holzinger et al, , 2011.…”

Section: Athenamentioning

confidence: 99%

ATHENA: the analysis tool for heritable and environmental network associations

et al. 2013

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Section: Athenamentioning

confidence: 99%

ATHENA: the analysis tool for heritable and environmental network associations

et al. 2013

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“…Both methods have been able to detect various types of genetic models, including highly epistatic interactions, using in silico genotype data [70–75]. GENN has been used in biological data to find interactions between eQTLs [76] and to model environmental and SNP genotypes to predict age-related macular degeneration [77].…”

Section: Simultaneous Analysismentioning

confidence: 99%

Integrating Heterogeneous High-Throughput Data for Meta-Dimensional Pharmacogenomics and Disease-Related Studies

Holzinger

Ritchie

2012

Pharmacogenomics

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The current paradigm of human genetics research is to analyze variation of a single data type (i.e., DNA sequence or RNA levels) to detect genes and pathways that underlie complex traits such as disease state or drug response. While these studies have detected thousands of variations that associate with hundreds of complex phenotypes, much of the estimated heritability, or trait variability due to genetic factors, remain unexplained. We may be able to account for a portion of the missing heritability if we incorporate a systems biology approach into these analyses. Rapid technological advances will make it possible for scientists to explore this hypothesis via the generation of high-throughput omics data – transcriptomic, proteomic and methylomic to name a few. Analyzing this ‘meta-dimensional’ data will require clever statistical techniques that allow for the integration of qualitative and quantitative predictor variables. For this article, we examine two major categories of approaches for integrated data analysis, give examples of their use in experimental and in silico datasets, and assess the limitations of each method.

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