Epistasis detection software tools | Genome-wide association analysis data analysis
The term epistasis describes a certain relationship between genes, where an allele of one gene hides or masks the visible output, or phenotype, of another gene. Epistasis is entirely different from dominant and recessive, which are terms that apply to different alleles of the same gene. With the advent of whole-genome sequencing, new algorithms and software have been developed to studies the impact of epistasis in human health at a genome scale, by considering the interaction of multiple single nucleotide polymorphisms (SNPs).
Assists users in performing large-scale analyses. Inbix is a command-line bioinformatics toolbox including software for machine learning and epistasis network analysis for high-dimensional data, such as genome-wide association study (GWAS), microarray, RNA-Seq, or expression quantitative trait loci (eQTLs). It includes Relief-based and evaporative cooling-based algorithms for feature selection to detect main effects and interaction effects for case-control and quantitative trait data.
A software tool that is able to detect multiple sets of significant gene-gene and/or gene-environment interactions in relation to a trait of interest, while efficiently controlling type I error rates. It is mainly used to analyse data were the trait is expressed either on a binary or continuous scale, but can also handle a censored trait.
Consists of an extension of the generalized multifactor dimensionality reduction (GMDR) to the survival phenotype. Cox-MDR is an algorithm that uses the martingale residual of the Cox regression model as a score to classify multi-loci genotype combinations into high and low-risk groups. It is able to adjust for covariate and can be extended to some types of high-dimensional data such as copy number variation (CNV) and next generation sequencing (NGS) data.
Provides a convenient single interface for accessing multiple publicly available human genetic data sources that have been compiled in the supporting database of the Library of Knowledge Integration (LOKI). Biofilter is a software which allows to annotate genomic location or region based data, filter genomic location or region based data on biological criteria and generate predictive models for gene-gene, single nucleotide polymorphism (SNP)-SNP, or copy number variants (CNV)-CNV interactions based on biological information, with priority for models to be tested based on biological relevance.
A GPU-implementation of BOOST based on the CUDA technology by Nvidia. GBOOST achieves a 40-fold speedup compared with BOOST. It completes the analysis of Wellcome Trust Case Control Consortium Type 2 Diabetes (WTCCC T2D) genome data within 1.34 h on a desktop computer equipped with Nvidia GeForce GTX 285 display card.
A learning approach based on the predictive rule inference to find disease-associated epistatic interactions. SNPRuler first uses the predictive rule learning to narrow down possible interactions among SNPs and then captures true interactions using χ2 statistic test. The experimental results demonstrate that SNPRuler is a powerful tool in handling large-scale SNP data both in terms of speed and detection of potential interactions that were not identified before.
Enables detection of gene-gene interactions with the survival time. AFT-MDR consists of an extension of the multifactor dimensionality reduction (MDR) method to the accelerated failure time (AFT) model. This software’ power is sensitive to the censoring fraction. It was used to analyze a real data for leukemia Korean patients.