ATHENA optimization: the effect of initial parameter settings across different genetic models

Authors:
Emily R. Holzinger;Scott M. Dudek;Eric C. Torstenson;Marylyn D. Ritchie
Affiliations:
Department of Molecular Physiology & Biophysics, Vanderbilt University, Nashville, TN;Department of Molecular Physiology & Biophysics, Vanderbilt University, Nashville, TN;Department of Molecular Physiology & Biophysics, Vanderbilt University, Nashville, TN;Department of Molecular Physiology & Biophysics, Vanderbilt University, Nashville, TN
Venue:
EvoBIO'11 Proceedings of the 9th European conference on Evolutionary computation, machine learning and data mining in bioinformatics
Year:
2011

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Abstract

Rapidly advancing technology has allowed for the generation of massive amounts data assessing variation across the human genome. One analysis method for this type of data is the genome-wide association study (GWAS) where each variation is assessed individually for association to disease. While these studies have elucidated novel etiology, much of the variation due to genetics remains unexplained. One hypothesis is that some of the variation lies in gene-gene interactions. An impediment to testing for interactions is the infeasibility of exhaustively searching all multi-locus models. Novel methods are being developed that perform a non-exhaustive search. Because these methods are new to genetic studies, rigorous parameter optimization is necessary. Here, we assess genotype encodings, function sets, and cross-over in two algorithms which use grammatical evolution to optimize neural networks or symbolic regression formulas in the ATHENA software package. Our results show that the effect of these parameters is highly dependent on the underlying disease model.