Methods for Identifying SNP Interactions: A Review on Variations of Logic Regression, Random Forest and Bayesian Logistic Regression

Authors:
Carla C. M. Chen;Holger Schwender;Jonthan Keith;Robin Nunkesser;Kerrie Mengersen;Paula Macrossan
Affiliations:
Queensland University of Technology Gardens Point, Brisbane;Johns Hopkins Bloomberg, Baltimore and TU Dortmund University, Dortmund;Queensland University of Technology Gardens Point, Brisbane and Monash University, Melbourne;TU Dortmund University, Dortmund;Queensland University of Technology Gardens Point, Brisbane;Queensland University of Technology Gardens Point, Brisbane
Venue:
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Year:
2011

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Abstract

Due to advancements in computational ability, enhanced technology and a reduction in the price of genotyping, more data are being generated for understanding genetic associations with diseases and disorders. However, with the availability of large data sets comes the inherent challenges of new methods of statistical analysis and modeling. Considering a complex phenotype may be the effect of a combination of multiple loci, various statistical methods have been developed for identifying genetic epistasis effects. Among these methods, logic regression (LR) is an intriguing approach incorporating tree-like structures. Various methods have built on the original LR to improve different aspects of the model. In this study, we review four variations of LR, namely Logic Feature Selection, Monte Carlo Logic Regression, Genetic Programming for Association Studies, and Modified Logic Regression-Gene Expression Programming, and investigate the performance of each method using simulated and real genotype data. We contrast these with another tree-like approach, namely Random Forests, and a Bayesian logistic regression with stochastic search variable selection.