Methods for Identifying SNP Interactions: A Review on Variations of Logic Regres

IEEE/ACM Trans Comput Biol Bioinform. 2011 Mar 3. [Epub ahead of print]
Methods for Identifying SNP Interactions: A Review on Variations of Logic Regression, Random Forest and Bayesian Logistic Regression.Chen CC, Schwender H, Keith J, Nunkesser R, Mengersen K, Macrossan P.
SourceQueensland University of Technology Gardens Point, Brisbane.

AbstractDue to advancements in computational ability, enhanced technology and a reduction i 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 modelling. 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.

PMID:21383421[PubMed - as supplied by publisher]



http://www.ncbi.nlm.nih.gov/pubmed/21383421