A free, open-source whole genome association analysis toolset, designed to perform a range of basic, large-scale analyses in a computationally efficient manner. The focus of PLINK is purely on analysis of genotype/phenotype data, so there is no support for steps prior to this (e.g. study design and planning, generating genotype or CNV calls from raw data). Through integration with gPLINK and Haploview, there is some support for the subsequent visualization, annotation and storage of results.
Supplies a method to compute exact values of standard test statistics in linear mixed models. GEMMA is a program built on EMMA software. The application fits three types of models: univariate and multivariate linear mixed model as well as Bayesian sparse linear mixed model. In addition, it estimates variance component and chip heritability. This tool provides a mean to make exact calculations for large genome wide association studies (GWAS).
A linear mixed model (LMM) algorithm for testing sets of genetic markers in the presence of confounding structure such as arises from ethnic diversity and family relatedness within a cohort. FaST-LMM uses two random effects—one to capture the set association signal and one to capture confounders.
Provides a method for resequencing data. The algorithm proposed is based on a genome continuum model and functional principal components. Beside, this algorithm was developed to test the phenotypic association of rare variants with high power, nominal type I error rates and the ability to buffer the impact of sequencing errors and missing data.
Identifies candidate causal single nucleotide polymorphisms (SNPs) and their corresponding candidate causal pathways from genome-wide association study (GWAS). ICSNPathway is a web server that integrates linkage disequilibrium (LD) analysis, functional SNP annotation and pathway-based analysis (PBA). The software can contribute to improve GWAS data interpretation from variants to biological mechanisms to better guide future biological mechanism studies.
Performs knowledge-based secondary analyses from genome-wide association studies (GWAS). KGG is a statistical framework to classify, weight, prioritize and interpret association p-values. It simultaneously models both the diverse biological knowledge and statistical association p-values to produce optimal weights for the prioritization. It can also find additional single nucleotide polymorphisms (SNPs) of the HapMap dataset in strong linkage disequilibrium (LD).
A computational algorithm to search for gene-disease associations from GWASs, taking advantage of independent eQTL data. Sherlock is applicable to any complex phenotype. It is readily generalizable to molecular traits other than gene expression, such as metabolites, noncoding RNAs, and epigenetic modifications.
An R library for genome-wide association (GWA) analysis. GenABEL implements effective storage and handling of GWA data, fast procedures for genetic data quality control, testing of association of single nucleotide polymorphisms with binary or quantitative traits, visualization of results and also provides easy interfaces to standard statistical and graphical procedures implemented in base R and special R libraries for genetic analysis.
A simple, ready-to-use software which has been designed to analyze genetic-epidemiology studies of association using SNPs. Main capabilities include descriptive analysis, test for Hardy-Weinberg equilibrium and linkage disequilibrium. Analysis of association is based on linear or logistic regression according to the response variable (quantitative or binary disease status, respectively). Analysis of single SNPs: multiple inheritance models (co-dominant, dominant, recessive, over-dominant and log-additive), and analysis of interactions (gene-gene or gene-environment). Analysis of multiple SNPs: haplotype frequency estimation, analysis of association of haplotypes with the response, including analysis of interactions.
Offers a way to solve large-scale, numerically intensive genome wide association studies (GWAS) calculations on multi-core symmetric multiprocessing computer architectures. SNPRelate permits basic calculations of sample and single nucleotide polymorphism (SNP) eigenvectors. It allows principal component analysis (PCA) and identity-by-descent (IBD) relatedness analysis on genomic data structure (GDS) genotype files. The tool permits to accelerate computations on SNP data.
Contains classes and methods to help the analysis of whole genome association studies. SNPassoc utilizes S4 classes and extends haplo.stats R package to facilitate haplotype analyses. The package is useful to carry out most common analysis when performing whole genome association studies. These analyses include descriptive statistics and exploratory analysis of missing values, calculation of Hardy-Weinberg equilibrium, analysis of association based on generalized linear models (either for quantitative or binary traits), and analysis of multiple SNPs (haplotype and epistasis analysis). Permutation test and related tests (sum statistic and truncated product) are also implemented.
A Bayesian approach to incorporate a set of important covariates into the fdr under a heteroscedastic model, where the probability of non-null status and the distribution of the test statistic under the non-null hypothesis are both modulated by covariates. The primary advantage of our methodology over traditional fdr methods is that two SNPs with the same z score can have different values of cmfdr if one is in a more enriched category than the other. Hence, by using SNP annotations to modulate fdr, more SNPs can be discovered for a given level of fdr control. In other words, methods such as cmfdr that break the exchangeability assumption are potentially more powerful than traditional fdr methods that assume exchangeability.
A high-dimensional variable selection method for survival analysis by improving the existing variable selection methods in several aspects. First, we have developed a computationally feasible variable selection approach for high-dimensional survival analysis. Second, we have designed a random sampling scheme to improve the control of the false discovery rate. Finally, the proposed framework is flexible to accommodate complex data structures. Comparisons between the proposed method and the commonly used univariate and Lasso approaches for variable selection reveal that the proposed method yields fewer false discoveries.
A permutation tool. PBOOST is based on GPU with highly reliable P-value estimation. In terms of speed, PBOOST completed 107 permutations for a single SNP pair from the Wellcome Trust Case Control Consortium (WTCCC) genome data (Wellcome Trust Case Control Consortium, 2007) within 1 min on a single Nvidia Tesla M2090 device, while it took 60 min in a single CPU Intel Xeon E5-2650 to finish the same task.
A software tool that estimates the p-value of a gene using information on annotation, single marker GWA results and genotype. The software tool is species and annotation independent, fast, highly parallelized, and ready for high-density marker studies.
A method for dividing the p-values into multiple groups and combine it at the group level. GCP integrates the significance values at different levels, and the power is improved. GCP can effectively control the type I error rates and have additional power over the existing methods – the power increase can be as high as over 50% under some situations.
Comprises a family of statistical methods designed to identify weak associations in genome-wide association studies that are not detectable by conventional analytical methods. Puma uses a regularized multiple regression in a penalized maximum likelihood framework using a generalized linear model in order to simultaneously consider tens to hundreds of thousands of genetic markers in a single statistical model. These methods are able to consider both case/control and continuous phenotypes and are optimized to efficiently handle very large datasets.
A hybrid approach that includes the principal components (PCs) of the genotype matrix as fixed effects in FaST-LMM Select. PC-Select leverages the advantages of the FaST-LMM Select framework while correcting for population stratification. The two main steps of FaST-LMM Select are ranking SNPs by linear regression P-values to form the genetic relationship matrix (GRM) with the top-ranked SNPs and then calculating association statistics in a mixed-model framework, using this GRM. We used the top five PCs as fixed effects in both of these steps. As a result, PC-Select yields noninflated test statistics in the presence of population stratification and maintains high power to detect causal SNPs.
Enables, in a combinatorial way, the analysis of single nucleotide polymorphism (SNP) genotype calls, copy numbers, polymorphic copy number variations (CNVs) and gene expression. SNPExpress is available for use with Affymetrix DNA mapping arrays, Illumina HumanHap550 Genotyping BeadChip and Affymetrix GeneChips. The software facilitates the identification of biologically and clinically relevant entities. It can be useful to genome-wide studies by providing an integrated view of data from DNA mapping and mRNA expression arrays.
Implements a method for computing posterior association probabilities of single-nucleotide polymorphisms (SNP) (and other quantities) in genome-wide association studies (GWAS) using Bayesian variable selection and model averaging. Bmagwa considers simultaneously all available variants for inclusion as predictors in a linear genotype-phenotype mapping and averages over the uncertainty in the variable selection. This leads to naturally interpretable summary quantities on the significances of the variants and their contribution to the genetic basis of the studied trait.
Tests genotype dependence for genome-wide scan of susceptibility gene. SWGDT utilizes known causal gene information to proceed. It provides an alternative approach for integrating known disease gene information into the susceptibility gene mapping of human complex diseases. This tool can be applied to genome wide association study (GWAS) data for genome-wide susceptibility gene scan. It aims to assist users to identify novel disease genes that are likely to be missed by GWAS.
A cross-platform integrated graphical analysis tool for conducting epidemiological, single SNP and haplotype-based association analysis. SimHap GUI features a workflow interface that guides the user through each logical step of the analysis process, making it accessible to both novice and advanced users. This tool provides a seamless interface to the SimHap R package, while providing enhanced functionality such as sophisticated data checking, automated data conversion, and real-time estimations of haplotype simulation progress.
Consists of a reverse regression model for multiple correlated phenotypes. SCOPA achieves multivariate association signals to find which phenotypes are jointly associated with the single nucleotide polymorphism (SNP). It employs reverse regression approach to work. This tool can aggregate association summary statistics across genome-wide association study (GWAS) through fixed-effects meta-analysis.
Handles large scale genome-wide data and allows for imputed genotypes by modelling time to event outcomes under a dosage model. SurvivalGWAS_SV is an analytics software capable of applying a range of survival analysis methods to genome-wide data, with appropriate handling of imputed genotypes. It can be applied to large-scale Genome Wide Association Studies (GWAS) datasets efficiently and effectively, without incurring memory issues.
Calculates the power to detect single nucleotide polymorphism (SNP) association with a time to event outcome over a range of study design scenarios. SurvivalGWAS_Power enables analyses under a Cox proportional hazards model or Weibull regression model, and can account for treatment and SNP-treatment interaction effects. Simulated data sets can also be generated by SurvivalGWAS_Power to enable analyses with methods that are not currently supported by the power calculator, thereby increasing the flexibility of the software. It also addresses the need for flexible and user-friendly software for power calculations for genetic association studies of time to event outcomes, with particular design features of relevance in pharmacogenetics.
Tests the hypothesis that variants nominally associated with a phenotype may be more likely to overlap genes than those not associated with a phenotype. For each clump, GENOVA determines whether it intersects a gene or not, and whether it is associated with the trait or not. It then uses logistic regression to determine the degree of overlap between gene-intersecting and trait-associated clumps, including as covariates potential confounders, namely the minor allele frequency for the SNPs in the clump, clump length and number of SNPs in the clump.
Permits gene expression level to be tested as an explanatory variable during genome-wide association studies (GWAS). eRD-GWAS employs a Bayesian-based statistical method. It utilizes RNA-seq measurements of transcript accumulation as the explanatory variables in GWAS and thereby directly demonstrate an association between variation in transcript accumulation of transcription factors (TFs) and phenotypic variation for a diverse collection of traits.
Finds the best possible markers for further linkage analysis. MarkerSet employs already available information about markers informativity, expressed in number of heterozygous animals out of all the reference animals tested in the experimental design. It is able to select the most informative markers in two windows separated by a constant gap, and sliding on the genome. This tool uses the distance between two markers as first criterion of selection.
Performs rapid mixed-model based genome-wide association analysis. OmicABEL implements algorithms that address the problems of single- and multi-trait Mixed-Model based genome-wide association studies (GWAS). It includes CLAK-Chol, that can be useful for investigation of complex traits in very large samples and CLAK-Eig. It can serve for the investigation of genetic control of different omics. It also can be used for the identification of trans-expression quantitative trait loci (eQTLs).
Conducts genome wide survival analyses. gwasurvivr is an application for genome wide survival analyses of imputed data in multiple file formats with flexible analysis and output options. This application performs survival analyses of imputed genotypes from Sanger and Michigan imputation servers and IMPUTE2 software. It also implements a Cox proportional hazards regression model to test each single nucleotide polymorphism (SNP) with an outcome, with or without covariates and/or SNP-covariate interaction.
Aims to furnish a computational method to calculate probabilistic distributional regression models (GAM) in a Bayesian framework. Bamlss gathered several frequently-used algorithms to estimate additive Bayesian models according to several effects as well as different response distributions, estimation techniques or model terms. Moreover, the package also includes functions able to generate post-estimation results like summary statistics and effect plots.
Assists in designing the prioritization of Single Nucleotide Polymorphisms (SNPs) biomarkers and the discovery of genes. METU-SNP is a java based integrated software system which can be used for the genome-wide association study (GWAS) and post-GWAS analysis. It facilitates the reliable identification of SNPs that are involved in the etiology of complex diseases and ultimately support timely identification of genomic disease biomarkers.
Allows users to run cross-trait genome wide association studies (GWAS) analysis. BGMG is a statistical method that is based on Gaussian mixture modelling framework. The application can be used for measuring polygenic overlap in various cross-trait scenarios, including beyond genetic correlation. The software was developed for considering the specificity of the polygenic architecture underlying each complex trait.
Computes the sample probability value (p-value) for the estimated coefficient from a standard genome-wide univariate regression. GWRPV calculates the skewness and kurtosis of the estimated regression coefficient, under the assumed mixture distribution of the dependent variable.
Provides a module for logistic regression analyses of single-nucleotide polymorphism (SNP) data. CGEN is a Bioconductor package for analysis of case-control studies in genetic epidemiology. It is designed to give the users flexibility of using a number of different methods for analysis of SNP-environment or SNP-SNP interactions. It also implements a number of different methods that can incorporate such independence constraints into analysis of interactions in the setting of both unmatched and matched case-control studies.
Provides a small collection of programs. SMA is a package that permits to perform different tests for association between genotypes at a single marker and a binary disease status. This method only read input files in the SNP file format.
A free, open-source software, designed to perform efficient permutation tests for large-scale genetic data sets. Using 10,000 permutations, a data set of several thousand individuals genotyped for millions of markers can be analyzed within a few hours on standard PCs. Since version 1.1.0, Permory in addition supports MPI, which enables parallel processing on computer clusters.
Permits users to find a set of single nucleotide polymorphism (SNP) markers according to the interval regularity. CHOISS gathers algorithms for selecting markers at a chosen density, given the desired number of SNPs or interval. It offers features such as graphical visualization of the results and an automatic construction of input file from a text form of NCBI GenBank contig data.
Permits investigation of principal component of genome-wide single nucleotide polymorphism (SNP) data. Shellfish permits the parallelization of principal component analysis (PCA) tasks and automates the process of data subsetting and allele-matching.
Provides tag single-nucleotide polymorphism (SNPs) selection application. WCLUSTAG allows users to prioritize different SNPs and genomic regions in a systematic association screen, depending on current genomic and disease data budget. This method takes account of functional as well as linkage disequilibrium (LD) information. An online web interface also permits users to import their own genotype data, or to directly withdraw HapMap data from the mirror database, for the calculations.
Enables informative single nucleotide polymorphism (SNP) tag selection and genotype prediction. MLR-tagging is a program assisting users to solve the informative SNP selection problem (ISSP) on genotypes. It implements a SNP prediction method based on multiple linear regression analysis. This tool is designed to directly predict genotypes without the explicit requirement of haplotypes.
Analyzes genome-wide interaction by using haplotype-based odds-ratio. The software allows users to perform a method that evaluates interaction between two linked or unlinked loci. It also can be used for identifying significant interaction between single-nucleotide polymorphism (SNPs) into the studied genome. It had been tested on two independent studies.
Offers a gene and pathway based genome wide association study (GWAS). The software aims to provides a general framework for complex diseases and novel statistics for testing association between disease and gene or pathway. It was tested on two independent studies from the Wellcome Trust Case–Control Consortium (WTCCC) and the North American Rheumatoid Arthritis Consortium (NARAC).
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