1 - 36 of 36 results

SMR / Summary-data-based Mendelian Randomization

Identifies associations between gene expression and complex traits using summary data from genome-wide association studies (GWAS) and expression quantitative trait locus (eQTL). Then, a heterogeneity test to distinguish pleiotropy from linkage can be realized. The SMR tool allows to search the most functionally relevant genes at the loci identified in GWAS data for complex traits. It provides a useful tool to prioritize genes underlying GWAS hits for follow up functional studies.

iCTNet / integrated Complex Traits Network

Allows automated and systematic creation of networks. iCTNet facilitates the generation of general or specific network views with diverse options for more than 200 diseases. It provides omics information such as phenotype-single nucleotide polymorphism (SNP) association, protein-protein interaction (ppi), disease-tissue, tissue-gene, and drug-gene relationships. The tool can be used to integrate and analyze disparate data sources. It allows the user to load views for selected phenotypes, and provides two methods to evaluate and prioritize disease-causing genes.

GoShifter / Genomic Annotation Shifter

A statistical approach to assess whether enriched annotations are able to prioritize causal variation. GoShifter employs an intuitive method that locally shifts sites of tested features within each locus to generate a null distribution of annotations overlapping associated variants by chance. This approach is less sensitive to biases arising from local genomic structure than commonly used enrichment methods that depend on single-nucleotide polymorphism (SNP) matching. GoShifter is able to robustly identify informative annotations under a range of different scenarios. It also allows prioritization of loci by determining the most informative functional variants driving the observed enrichment. GoShifter represents an important advance over current approaches in its ability to assess independent effects from colocalizing annotations.

MEGHA / Massively Expedited Genome-wide Heritability Analysis

Allows for examination of complex phenotypes and the development of nonparametric sampling techniques. MEGHA is a statistical method for large-scale heritability analysis using genome-wide single-nucleotide polymorphism (SNP) data from unrelated individuals. It could be used to prioritize brain structural magnetic resonance imaging (MRI) phenotypes based on heritability. This method provides both magnitude estimates and significance measures of heritability with orders of magnitude less computational effort relative to GCTA.


Predicts complex traits. OmicKriging leverages and integrates similarity in genetic, transcriptomic, and/or other large scale omics data. It emphasizes the use of a wide variety of systems-level data. The tool is able to integrate other sources of information such as prior evidence of association or function and even geographic proximity. It generates correlation matrices from single nucleotide polymorphism (SNP), gene expression, methylation or other 'omics' datasets and predicts the phenotype of an individual by using the phenotypes of the remaining individuals through kriging.


Methods implemented in the software tool GetSynth for the search for multi-locus haplotype markers in near perfect linkage disequilibrium (LD) with a genome-wide association studies (GWAS) tag variant. Such haplotype markers fulfil the formal criteria of a synthetic association. GetSynth can be applied in a case-control setting as well as to public reference genotype data. Filter criteria, set size, function classes and the number of functional variants that shall be involved in a synthesis can be pre-specified by the user.


A unified marker wise test (uFineMap) to accurately localize causal loci and a unified high-dimensional set based test (uHDSet) to identify high-dimensional sparse associations in deep sequencing genomic data of multi-ethnic individuals with random relatedness. These two novel tests are based on scaled sparse linear mixed regressions with Lp (0 < p < 1) norm regularization. They jointly adjust for cryptic relatedness, population structure and other confounders to prevent false discoveries and improve statistical power for identifying promising individual markers and marker sets that harbor functional genetic variants of a complex trait.

SMART / Scalable Multiple Annotation integration for trait-Relevant Tissue identification

Identifies trait-relevant tissues. SMART is a method, based on an extension of a linear mixed model, that uses several complementary annotations coupled to various methods and algorithms. The application can be used to build efficient single nucleotide polymorphisms (SNP) set tests and to determine new trait-tissue relevance and informative annotations about trait-tissue relationships. It has been tested on 43 traits from genome wide analysis studies (GWASs) using tissue-specific annotations.


Predicts untested single crosses (SCs) in plant breeding. REALbreeding aims to assess the prediction efficiency by correlating the predicted and true genotypic values of untested SCs. It uses simulated data to measur the efficacy of identification of the best 300 untested SCs. The tool is able to generate the incidence matrices for models and for lines sampling processes. It can be useful in genomic selection, genome wide association studies (GWAS), LD analysis, and genetic diversity.


Provides a massive parallel and user friendly implementation of the PBAT-analysis tools for family based association tests (FBATs) in large-scale studies, including genome-wide association studies with several thousand subjects. P2BAT is a software that integrates all PBAT-analysis tools for binary and complex traits into R and makes them accessible through a user-friendly GUI. It also allows to run the analysis of genome-wide association studies massively parallel on cluster, reducing the analysis time of 100 000 SNPs and more to a couple of minutes.


Allows users to perform mixed model analysis for genome wide association studies (GWAS). rrBLUP aims to assist users in genomic selection. It provides predictions based on maximum likelihood (ML) or restricted maximum likelihood (REML) approach to ridge regression (RR) and other kernels. It also includes features dedicated to the resolution of marker-based and kinship-based versions of the genomic prediction problem as well as several genetic models such as the nonadditive Gaussian kernel.

GEMMA / Genome-wide Efficient Mixed Model Association

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).

PRSice / Polygenic Risk Score software

Allows users to capture current standard practices in polygenic risk score (PRS) studies and the different applications of PRS. PRSice performs a simulation study to estimate a P-value significance threshold for high-resolution PRS studies and produces plots for inspection of results. One of the function of this software is to automate PRS analyses. It is able to calculate PRS at any number of P-value thresholds (PT) and can thus identify the most predictive threshold.


A powerful and versatile computing tool for assessing the type and magnitude of genetic effects affecting a phenotype. The GVCBLUP package is a powerful and versatile computing tool for assessing the type and magnitude of genetic effects affecting a phenotype by estimating whole-genome additive and dominance heritabilities, for genomic prediction of breeding values, dominance deviations and genotypic values, for calculating genomic relationships, and for research and education in genomic prediction and estimation.

CAESAR / CAndidatE Search And Rank

Ranks all annotated human genes as candidates for a complex trait. CAESAR maps natural language descriptions of the trait with a variety of gene-centric information sources by using ontologies, text and data mining. It permits to process several hundred thousand biological annotations which require highly specialized domain expertise. The tool is principally limited to genes and traits for which there is sufficient information in the form of annotations and text descriptions. It can be used to prioritize candidate gene.