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Methods for Identifying SNP Interactions: A Review on Variations of Logic Regression, Random Forest and Bayesian Logistic Regression
November/December 2011 (vol. 8 no. 6)
pp. 1580-1591
ASCII Text
x 
 
C. C. M. Chen, H. Schwender, J. Keith, R. Nunkesser, K. Mengersen, P. Macrossan, "Methods for Identifying SNP Interactions: A Review on Variations of Logic Regression, Random Forest and Bayesian Logistic Regression," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 8, no. 6, pp. 1580-1591, November/December, 2011.
 
 
BibTex
x
 
@article{ 10.1109/TCBB.2011.46,
author = {C. C. M. Chen and H. Schwender and J. Keith and R. Nunkesser and K. Mengersen and P. Macrossan},
title = {Methods for Identifying SNP Interactions: A Review on Variations of Logic Regression, Random Forest and Bayesian Logistic Regression},
journal ={IEEE/ACM Transactions on Computational Biology and Bioinformatics},
volume = {8},
number = {6},
issn = {1545-5963},
year = {2011},
pages = {1580-1591},
doi = {http://doi.ieeecomputersociety.org/10.1109/TCBB.2011.46},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE/ACM Transactions on Computational Biology and Bioinformatics
TI - Methods for Identifying SNP Interactions: A Review on Variations of Logic Regression, Random Forest and Bayesian Logistic Regression
IS - 6
SN - 1545-5963
SP1580
EP1591
EPD - 1580-1591
A1 - C. C. M. Chen,
A1 - H. Schwender,
A1 - J. Keith,
A1 - R. Nunkesser,
A1 - K. Mengersen,
A1 - P. Macrossan,
PY - 2011
KW - Monte Carlo methods
KW - belief networks
KW - genetics
KW - genomics
KW - medical computing
KW - molecular biophysics
KW - molecular configurations
KW - single nucleotide polymorphism
KW - SNP interactions
KW - logic regression
KW - random forest
KW - Bayesian logistic regression
KW - tree-like structures
KW - logic feature selection
KW - Monte Carlo logic regression
KW - Genetic Programming for Association Studies
KW - modified logic regression-gene expression programming
KW - real genotype data
KW - random forests
KW - stochastic search variable selection
KW - Regression analysis
KW - Mathematical model
KW - Bayesian methods
KW - Genetic programming
KW - Monte Carlo methods
KW - candidate gene search.
KW - Logic regressions
KW - Genetic Programming for Association Studies
KW - Modified Logic Regression-Gene Expression Programming
KW - Random Forest
KW - Bayesian logistic regression with stochastic search algorithm
VL - 8
JA - IEEE/ACM Transactions on Computational Biology and Bioinformatics
ER -
 
C. C. M. Chen, Discipline of Math. Sci., Queensland Univ. of Technol., Brisbane, QLD, Australia
H. Schwender, Dept. of Biostat., Johns Hopkins Univ., Baltimore, MD, USA
J. Keith, Sch. of Math. Sci., Monash Univ., Clayton, VIC, Australia
R. Nunkesser, Dept. of Comput. Sci., Tech. Univ. Dortmund, Dortmund, Germany
K. Mengersen, Discipline of Math. Sci., Queensland Univ. of Technol., Brisbane, QLD, Australia
P. Macrossan, Discipline of Math. Sci., Queensland Univ. of Technol., Brisbane, QLD, Australia
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.

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Index Terms:
Monte Carlo methods,belief networks,genetics,genomics,medical computing,molecular biophysics,molecular configurations,single nucleotide polymorphism,SNP interactions,logic regression,random forest,Bayesian logistic regression,tree-like structures,logic feature selection,Monte Carlo logic regression,Genetic Programming for Association Studies,modified logic regression-gene expression programming,real genotype data,random forests,stochastic search variable selection,Regression analysis,Mathematical model,Bayesian methods,Genetic programming,Monte Carlo methods,candidate gene search.,Logic regressions,Genetic Programming for Association Studies,Modified Logic Regression-Gene Expression Programming,Random Forest,Bayesian logistic regression with stochastic search algorithm
Citation:
C. C. M. Chen, H. Schwender, J. Keith, R. Nunkesser, K. Mengersen, P. Macrossan, "Methods for Identifying SNP Interactions: A Review on Variations of Logic Regression, Random Forest and Bayesian Logistic Regression," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 8, no. 6, pp. 1580-1591, Nov.-Dec. 2011, doi:10.1109/TCBB.2011.46
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