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BEAM / Bayesian Epistasis Association Mapping
A method for genome-wide case-control studies. BEAM treats the disease-associated markers and their interactions via a bayesian partitioning model and computes, via Markov chain Monte Carlo, the posterior probability that each marker set is associated with the disease. Testing this on an age-related macular degeneration genome-wide association data set, we demonstrate that the method is significantly more powerful than existing approaches and that genome-wide case-control epistasis mapping with many thousands of markers is both computationally and statistically feasible.
A general measure for epistasis testing. W-test is fast, model-free, and powerful. We have demonstrated that the W-test has robust power for linear and non-linear genetic models over a range of genetic environments. The method is especially advantageous for low frequency variants and has persistent power when the sample size is small. The proposed method aims to test the distributional differences between cases and controls, using the sum of squared log odds ratio over the complete cell distribution in a contingency table. The cell distribution that is formed by a pair of markers has the overall probability to be one, in the control group and the case group, respectively. This constraint keeps the cell proportions to reflect distributional differences, which are tested cell by cell using the odds ratio.
Explores and identifies synergistic pairs of single nucleotide polymorphisms (SNPs). SNPsyn provides an interactive graphical interface for explorative analysis of synergistic gene interactions. The software supports all steps in the analysis of genome-wide association study (GWAS) data: data preparation, interaction analysis and exploration of results. It implements two approaches to SNP selection for synergy exploration: (1) The hypothesis-free de novo and (2) the hypothesis-driven investigation.
Allows to make epistasis detection based on hierarchical representation of linkage disequilibrium (LD). LinDen reduces the number of tests performed in epistasis detection. It uses correlations between the genotypes of neighboring loci to construct groups that hierarchically represent LD trees and derives representative genotypes for these LD groups. It utilizes these representative genotypes to score the potential interaction between any pair of loci in the respective groups, but also to filter out pairs of loci groups that are not promising.
MBS / Multiple Beam Search
Discovers genome-phenome relationship using Bayesian Networks (BNs). MBS employs the extended greedy search and learn directed acyclic graph (DAG) models that contain two or more predictors in the epistatic interaction. It can be used to learn from data the interactive relationship among a subset of predictors that together can have a causal effect on a clinical feature. This tool has been tested on genome-wide association study (GWAS) data and successfully discovered the epistatic interaction of single nucleotide polymorphisms (SNPs) that have causal effect on Late Onset Alzheimer disease (LOAD).
Detects SNP-SNP interactions in GWA studies. SNPHarvester creates multiple paths in which the visited SNP groups tend to be statistically associated with diseases, and then harvests those significant SNP groups which pass the statistical tests. It greatly reduces the number of SNPs. Consequently, existing tools can be directly used to detect epistatic interactions. By using a wide range of simulated data and a real genome-wide data, we demonstrate that SNPHarvester outperforms its recent competitor significantly and is promising for practical disease prognosis.
An efficient parallel solution extending the PLINK epistasis module, designed to test for epistasis effects when analyzing continuous phenotypes. FastEpistasis is capable of analyzing several different phenotypes simultaneously, using the same genotypes. By performing the QR decomposition of the covariate matrix once and applying the result to several phenotypes, the total number of computations is reduced compared to carrying out the computations separately for each phenotype. FastEpistasis is capable of testing the association of a continuous trait with all single nucleotide polymorphism (SNP) pairs from 500,000 SNPs, totaling 125 billion tests, in a population of 5,000 individuals in 29, 4 or 0.5 days using 8, 64 or 512 processors.
MDR / Multifactor Dimensionality Reduction
Detects epistatic relationships between genes. MDR is a nonparametric and genetic model-free data mining alternative to logistic regression for detecting and characterizing nonlinear interactions among discrete genetic and environmental attributes. The MDR method combines attribute selection, attribute construction, and classification with cross-validation and permutation testing to provide a comprehensive and powerful approach to detecting nonlinear interactions. Using graphics processing units (GPUs) to run MDR on a genome-wide dataset allows for statistically rigorous testing of epistasis.
MECPM / Maximum Entropy Conditional Probability Modelling
An algorithm for learning maximum entropy probability models of disease risk that efficiently searches and sparingly encodes multilocus genomic interactions. Unlike neural networks and support vector machines (SVMs), MECPM makes explicit and is determined by the interactions that confer phenotype-predictive power. MECPM identifies both a marker subset and the multiple k-way interactions between these markers. Additional key aspects are: (i) evaluation of a select subset of up to five-way interactions while retaining relatively low complexity; (ii) flexible single nucleotide polymorphism (SNP) coding (dominant, recessive) within each interaction; (iii) no mathematical interaction form assumed; (iv) model structure and order selection based on the Bayesian Information Criterion, which fairly compares interactions at different orders and automatically sets the experiment-wide significance level; (v) MECPM directly yields a phenotype-predictive model.
Detects two-locus associations allowing for interactions from GWAS. SNPAssociation displays many advantages over existing methods: 1) it can detect two-locus associations allowing for interactions from genome-wide data in a fast manner; 2) it does not assume any particular epistasis model. This is very important for real studies because the patterns of SNP interactions are generally unknown and could be complex; 3) it can be extended into distributed and parallel computing to analyze the phase 2 datasets from WTCCC.
A multi-objective heuristic optimization methodology for detecting genetic interactions. In MACOED, we combine both logistical regression and Bayesian network methods, which are from opposing schools of statistics. The combination of these two evaluation objectives proved to be complementary, resulting in higher power with a lower false-positive rate than observed for optimizing either objective independently. To solve the space and time complexity for high-dimension problems, a memory-based multi-objective ant colony optimization algorithm is designed in MACOED that is able to retain non-dominated solutions found in past iterations.
An efficient family-based gene-gene interaction test for trios (i.e., two parents and one affected sib). The GCORE compares interlocus correlations at two SNPs between the transmitted and non-transmitted alleles. We used simulation studies to compare the statistical properties such as type I error rates and power for the GCORE with several other family-based interaction tests under various scenarios. We applied the GCORE to a family-based GWAS for autism consisting of approximately 2,000 trios. Testing a total of 22,471,383,013 interaction pairs in the GWAS can be finished in 36 hours by the GCORE without large-scale computing resources, demonstrating that the test is practical for genome-wide gene-gene interaction analysis in trios.
umMDR / Unified Model based Multifactor Dimensionality Reduction
Obtains the significance of a multi-locus model, even a high-order model through a regression framework with a semi-parametric correction procedure for controlling Type I error rates. UM-MDR avoids heavy computation in order to achieve the significance of a multi-locus model. The approach is able to incorporate different types of traits and evaluate significances of the existing MDR extensions. The tool provides a supplement of existing MDR method due to its efficiency in achieving significance for every multi-locus model, its power and its flexibility of handling different types of traits.
SIPI / SNP Interaction Pattern Identifier
Takes non-hierarchical models, inheritance modes and mode coding direction into consideration. SIPI can intensively and effectively search pairwise SNP–SNP interactions. It detects 45 interaction models, which take inheritance mode (both original and reverse coding), and risk category grouping (model structure) into consideration. Benchmark shows that SIPI is a more comprehensive and flexible tool for detecting two-way SNP–SNP interactions compared with the three full model approaches: AA_Full in PLINK, Geno_Full and SNPassoc. All these methods are based on hierarchical models, and the difference is how the inheritance modes are deal.
TS-GSIS / Two Stage-Grouped Sure Independence Screening
Permits the study of single nucleotide polymorphism (SNP)–SNP interactions with or without marginal effects. TS-GSIS provides valid variable selection for the analysis of quantitative and disease traits under various types of correlation, MAF and trait dispersion. This method can (i) determine whether SNP jointly form a candidate model with associations, (ii) determine the size of the candidate model automatically, (iii) discover significant SNP–SNP interactions without individual marginal SNP effects, (iv) make direct inference and easy interpretation on the biologically meaningful gene, (v) identifies SNPs in a gene when they jointly contribute to the trait y and (vi) reduce the search space for identification of the interaction effects.
SMMB / Stochastic Multiple Markov Blankets
Allows users to analyze epistatic patterns in genome wide association studies (GWAS) data. SMMB is a program dedicated to feature selection based on Markov blanket (MB) construction. This program can only focus on binary phenotypes, such as disease status. It is divided in two routines: (1) in each iteration, a Markov blanket is learned from K single nucleotide polymorphism (SNPs); and (2) the second routine is the stochastic algorithm proposed to learn a Markov blanket taking into account potential epistatic interaction between single nucleotides polymorphisms (SNPs).
Combines the differential evolution (DE) algorithm with a classification based multifactor-dimensionality reduction (CMDR) to identify potential epistasis in genome-wide association studies (GWAS). DECMDR is a fast and accurate method for epistatic interaction detection. It uses the metaheuristics to find the significant epistasis in genome-wide data sets to allow a shorter execution time. DECMDR is a powerful method for handling large-scale GWAS data both in terms of speed and detection of the more significant, previously unidentified interactions.
Provides algorithms for training and evaluating several types of Boltzmann Machines (BMs). BoltzmannMachines.jl is a Julia package that supports multiple cores: (i) learning of Restricted Boltzmann Machines (RBMs) using Contrastive Divergence, (ii) greedy layerwise pre-training of Deep Boltzmann Machines, (iii) learning procedure for general Boltzmann Machines using mean-field inference and stochastic approximation, (iv) exact calculation of the likelihood of BMs, (v) Annealed Importance Sampling (AIS) for estimating the likelihood of larger BMs.
JBASE / Joint Bayesian Analysis of Subphenotypes and Epistasis
An integrative mixture model. JBASE explores two major reasons of missing heritability: interactions between genetic variants, a phenomenon known as epistasis and phenotypic heterogeneity, addressed via subphenotyping. Our extensive simulations in a wide range of scenarios repeatedly demonstrate that JBASE can identify true underlying subphenotypes, including their associated variants and their interactions, with high precision. JBASE is the first algorithm to tackle modeling of epistasis and subphenotyping simultaneously. We show that taking both of these causes of missing heritability into account increases the power and reduces the Type 1 Error in detecting associations.
ClinGen Pathogenicity Calculator / Clinical Genome Resource Pathogenicity Calculator
Assesses pathogenicity of Mendelian germline sequence variants. ClinGen Pathogenicity Calculator allows users to enter the applicable American College of Medical Genetics and Genomics /Association for Molecular Pathology-style evidence tags for a specific allele with links to supporting data for each tag and generate guideline-based pathogenicity assessment for the allele. The software is modular, equipped with robust application program interfaces and as a cloud-hosted web service, thus facilitating both stand-alone use and integration with existing variant curation and interpretation systems.
Source code from Determination of Nonlinear Genetic Architecture using Compressed Sensing
A compressed sensing method that can reconstruct nonlinear genetic models (i.e., including epistasis, or gene-gene interactions) from phenotype-genotype (GWAS) data. Our method uses L1-penalized regression applied to nonlinear functions of the sensing matrix. Our results indicate that predictive models for many complex traits, including a variety of human disease susceptibilities (e.g., with additive heritability h2 ∼0.5), can be extracted from data sets comprised of n⋆ ∼100s individuals, where s is the number of distinct causal variants influencing the trait. For example, given a trait controlled by ∼10 k loci, roughly a million individuals would be sufficient for application of the method.
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.
BaDGE / Bayesian model for Detecting Gene Environment interaction
Allows the study of gene-environment interaction. BaDGE implements a Bayesian model and the associated post-processing procedures. The method permits users to look at gene–environment interaction, at the gene/region level, by integrating information observed on multiple genetic markers within the selected gene/region with measures of environmental exposure. It provides a strategy for studying the interaction between an observed risk factor and a latent categorical variable not directly observed or clearly defined, but that can be derived from a set of observed relevant covariates.
An efficient algorithm for performing ANOVA tests on SNP-pairs in a batch mode, which also supports large permutation test. We derive an upper bound of SNP-pair ANOVA test, which can be expressed as the sum of two terms. The first term is based on single-SNP ANOVA test. The second term is based on the SNPs and independent of any phenotype permutation. Furthermore, SNP-pairs can be organized into groups, each of which shares a common upper bound. This allows for maximum reuse of intermediate computation, efficient upper bound estimation, and effective SNP-pair pruning. Consequently, FastANOVA only needs to perform the ANOVA test on a small number of candidate SNP-pairs without the risk of missing any significant ones. FastANOVA is designed for studies with homozygous genotypes and relatively small sample sizes.
PEPIS / Pipeline for estimating EPIStatic effect
A web server-based tool for analysing polygenic epistatic effects. PEPIS is based on a linear mixed model that has been used to predict the performance of hybrid rice. It includes two main sub-pipelines: the first for kinship matrix calculation, and the second for polygenic component analyses and genome scanning for main and epistatic effects. PEPIS was dedicatedly developed for epistatic genetic estimation. It will help overcome the bottleneck in genetic epistasis analysis.
epiNEM / Epistatic NEMs
Can take into account double knockouts and infer more complex network signalling pathways. EpiNEM incorporates logical functions that describe interactions between regulators. The epiNEM method can be applied to all datasets that measure multi-parametric phenotypes for combinatorial perturbations. This tool is designed to use large knock-out screens to identify those hidden signalling genes as modulators of the signal and explanation of the corresponding data. It allows to understand mediators of complex phenotypes of genetic interactions.
BHIT / Bayesian High-order Interaction Toolkit
A Bayesian partition computational method for detecting SNP interactions (epistasis). The proposed approach builds a Bayesian model on both continuous data and discrete data to partition multiple-phenotype data. Comparing with other methods on both simulation data and real data, the key strengths of BHIT are as follows: (i) With the advanced Bayesian model equipped with MCMC search, BHIT can efficiently explore high-order interactions. (ii) BHIT has a flexible Bayesian model on continuous and discrete data, so that both continuous and discrete phenotypes could be handled simultaneously, and the interaction within or between phenotypes and genetic data can also be detected.
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