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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.
Requires only a small number of O(MN)-time iterations and increases power by modeling more realistic, non-infinitesimal genetic architectures via a Bayesian mixture prior on marker effect sizes. BOLT-LMM algorithm consists of four main steps, each of which require a small number of O(MN)-time iterations. These steps are: (1a) Estimate variance parameters; (1b) Compute infinitesimal mixed model association statistics (denoted BOLT-LMM-inf); (2a) Estimate Gaussian mixture parameters; (2b) Compute Gaussian mixture model association statistics (BOLT-LMM). Simulations show that BOLT-LMM achieves increased association power over standard infinitesimal mixed model analysis of traits driven by a few thousand causal Single Nucleotide Polymorphims (SNPs).
Allows simulation of cell structure and function. DCell is an interpretable or “visible” neural network (VNN) simulating a basic eukaryotic cell. The functional state of each subsystem is represented by a bank of neurons and connectivity of these neurons is set to mirror the biological hierarchy. The software hierarchical structure captures many different clusters of features at multiple scales, pushing interpretation from the model input to internal features representing biological subsystems.
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Controls for the disruptive effects of both population structure and recombination. treeWAS is a phylogenetic approach that is able to overcome many of the limitations of existing microbial genome wide association studies (GWAS) approaches. This method uses the simulation of a null genetic dataset to establish whether high association score values in the empirical dataset. It provides both specificity and power in a wide range of settings, and consistently offers the best overall performance.
CERAMIC / Case-control Efficient Related-individual Association Mapping Incorporating Covariates
Provides a method for genetic association mapping of binary traits in samples with related individuals. CERAMIC gathers relevant covariates, pedigree and can incorporate data on individuals with partially missing data. Moreover, the software is able to correct binary phenotypes for both covariates and additive polygenic effects and can be used for performing calculations for current association studies.
TASSEL / Trait Analysis by aSSociation, Evolution and Linkage
Implements general linear model and mixed linear model approaches for controlling population and family structure. For result interpretation, the program allows for linkage disequilibrium statistics to be calculated and visualized graphically. Database browsing and data importation is facilitated by integrated middleware. Other features include analyzing insertions/deletions, calculating diversity statistics, integration of phenotypic and genotypic data, imputing missing data and calculating principal components.
An R/Bioconductor package for quality control and analysis of genome-wide association studies (GWAS). GWASTools brings the interactive capability and extensive statistical libraries of R to GWAS. Data are stored in NetCDF format to accommodate extremely large datasets that cannot fit within R's memory limits. The benefits of GWASTools include the interactive analysis provided by R’s interface and the ability to include intensity data. Intensity data can be used to detect sex chromosome aneuploidies (which can be confused with sex mis-annotation), autosomal anomalies (which generate genotyping errors) and evaluation of clustering by genotype call. The documentation includes instructions for converting data from multiple formats, including variants called from sequencing. GWASTools provides a convenient interface for linking genotypes and intensity data with sample and single nucleotide polymorphism annotation.
GWASpi / Genome Wide Association Study pipeline
A user-friendly, multiplatform, desktop-able application for the management and analysis of GWAS data, with a novel approach on database technologies to leverage the most out of commonly available desktop hardware. GWASpi aims to be a start-to-finish GWAS management application, from raw data to results, containing the most common analysis tools. As a result, GWASpi is easy to use and reduces in up to two orders of magnitude the time needed to perform the fundamental steps of a GWAS.
iPat / Intelligent Prediction and Association Tool
Allows users to perform genomic analyses. iPat performs both genome-wide association studies (GWAS) and genomic prediction, including GWAS-assisted genomic prediction. It offers a friendly graphical user interface (GUI) to reduce user learning time and requires only one input data format to conduct any analysis with any incorporated method. These analyses include both mapping genes through GWAS and genomic prediction through understanding the relationships between genotypes and phenotypes.
An open source software SeqFeatR has been developed to identify associations between mutation patterns in biological sequences and specific selection pressures ("features"). SeqFeatR allows for instance to identify viral immune escape mutations for hosts of given HLA types. The underlying statistical method is Fisher's exact test, with appropriate corrections for multiple testing, or Bayes. Patterns may be point mutations or n-tuple of mutations. SeqFeatR offers several ways to visualize the results of the statistical analyses.
PCAN / Phenotype Consensus ANalysis
A phenotype-based method to support the identification of disease genes by evaluating whether similar phenotypes are linked to genes in the same signaling neighborhood. PCAN allows the user to prioritize putative disease causing genes but importantly, it also enables granular, biological interpretation of the output to relate high-scoring, matching traits to specific sub-processes and interactions. As a standalone exploration tool, PCAN can be used in broader contexts than variant prioritization from whole exome sequencing (WES) data. Each co-segregating gene may harbor the true causal variant, which PCAN can be used to highlight if the signaling environment of the gene is linked to similar diseases.
ACID / Association Correction for Imbalanced Data
Provides a correction tool for the association analysis on the biased data. ACID integrates a sampling technique to the probabilistic generative model, with adapting to the genomic structures. This method assists in the significance of suspicious loci. Its applicable field is not restricted to conventional situations that association is caused by a single common marker. It also has the potential ability to be extended to other complex situations such as gene-based associations or rare variant associations.
OGA / Ontological tool of phenotypes with Genetic Associations
Allows users to perform meaningful tests that take advantage of the ontology. OGA offers a way to find specific phenotypes and browse the ontology. It stores about 2 300 concepts linked to a total of more than 135 000 associations. This tool combines Genetic Association Database (GAD) and GWASdb to map genetic associations to the human phenotype ontology. It is able to construct links between the concepts present in phenotype ontologies and the entries on genetic association databases.
DMU / Derivative-free approach to MUltivariate analysis by Restricted Maximum Likelihood
Allows analysis of multivariate mixed models. DMU provides tools for estimating variance components and fixed effects (BLUE) and predicting random effects (BLUP). The software includes several modules such as (1) DMU1 (the Initial program), (2) DMU4 to predict future outcomes of random effects and estimate fixed effects, (3) DMU5 to solve the multiple trait mixed model equations based on iteration on data, or (4) DMUAI to estimate (co)variance components.
Fits linear models using Residual Maximum Likelihood (REML). ASREML is a data analysis software that allows users to fit linear mixed models to quite large data sets with complex variance models. The software provides a platform to deliver well established procedures as well as current research in the application of linear mixed models. It has applications in analysis of (un)balanced longitudinal data, repeated measures data, (un)balanced designed experiments, or regular or irregular spatial data for instance.
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