Related Experiment Videos
Methods for identifying SNP interactions: a review on variations of Logic Regression, Random Forest and Bayesian
Carla Chia-Ming Chen1, Holger Schwender, Jonathan Keith
1Discipline of Mathematical Sciences, Queensland University of Technology, Gardens Point, Brisbane, Queensland 4001, Australia. gcewels@gmail.com
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.
Related Concept Videos
Comparing Copy Number Variations and SNPs
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
Principles of Pharmacogenetics: Types of Genetic Variants
Genome-wide Association Studies-GWAS
GWAS does not require the identification of the target gene involved in...
Single Nucleotide Polymorphisms-SNPs
Epistasis Analysis
Pharmacogenomics: Identification of New Drug Targets