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LDSC / LD SCore regression
Provides a method for partitioning heritability from genome-wide association studies (GWAS) summary statistics while accounting for linked markers. LD SCore regression identified strong enrichment for conserved regions across all traits, and immunological disease-specific enrichment for FANTOM5 enhancers. It is computationally tractable at very large sample sizes. It also includes a tool for applying the method to sets of specifically expressed genes for identifying disease relevant tissues and cell types.
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.
HESS / Heritability Estimation from Summary Statistics
Estimates the amount of variance in trait explained by typed single nucleotide polymorphisms (SNPs) at each single locus on the genome from genome-wide association study (GWAS) summary statistics, while accounting for linkage disequilibrium (LD). HESS can be viewed as a weighted summation of the squares of the projection of GWAS effect sizes onto the eigenvectors of the LD matrix at the considered locus. It also provides a variance estimator according to quadratic form theory. HESS is unbiased when given in-sample LD and yields more consistent and less biased estimates of local SNP heritability than LDSC given external reference LD.
DIST / Direct Imputation of summary Statistics
Imputes the summary statistics of untyped variants without first imputing their subject-level genotypes. This is achieved by (i) using the conditional expectation formula for multivariate normal variates and (ii) using the correlation structure from a relevant reference population. When compared with genotype imputation methods, DIST (i) requires only a fraction of their computational resources, (ii) has comparable imputation accuracy for independent subjects and (iii) is readily applicable to the imputation of association statistics coming from large pedigree data. Thus, the proposed application is useful for a fast imputation of summary results for (i) studies of unrelated subjects, which (a) do not provide subject-level genotypes or (b) have a large size and (ii) family association studies.
A software tool which uses only GWAS summary statistics to i) impute the summary statistics at unmeasured eQTLs and ii) test for the joint effect of all measured and imputed eQTLs in a gene. Applied analyses results suggest that JEPEG complements commonly used univariate GWAS tools by: i) increasing signal detection power via uncovering a) novel genes or b) known associated genes in smaller cohorts, and ii) assisting in fine-mapping of challenging regions, e.g. Major Histocompatibility Complex (MHC) for schizophrenia.
DISSCO / Direct Imputation of Summary Statistics allowing COvariates
Offers a method for direct imputation of summary statistics allowing for covariates. DISSCO provides three different modes: (i) “calculate” can compute Z statistics at typed markers (ii) “project” can be used for the projection of the sample covariate(s) into reference, based on genotype and covariate data in sample, and genotype data in reference and lastly; (iii) “impute” is made for imputing Z statistics at untyped markers, based on reference, sample Z statistics and pseudo-covariate(s).
Estimates variance parameters of a bivariate lineax mixed model (LMM) in the following context arising in genetics research. BiMM is a computationally efficient implementation of a bivariate linear mixed model for settings where hundreds of traits have been measured on partially overlapping sets of individuals. This tool allows user to control how much missing data are tolerated for a single analysis and automatically excecutes both phenotype imputation and matrix decompositions required to achieve this tolerance.
LSMM / Latent Sparse Mixed Model
Integrates genic category annotations and cell-type specific functional annotations with genome-wide association studies (GWAS). LSMM is built on a variational expectation-maximization (EM) algorithm for model parameter estimation and statistical inference. This software enhances the statistical power in the identification of risk single-nucleotide polymorphisms (SNPs) and can infer relevant cell-type specific functional annotations to the phenotype.
Integrates individual level genotype data and summary statistics for exploration of genetic architecture of complex phenotypes. The IGESS algorithm is based on variational inference and is developed to treat the analysis of genome wide. It provides the posterior probability of association status between each single nucleotide polymorphism (SNP) and the given phenotype and the effect size of each SNP for risk prediction. This tool can serve in integrating individual-level data and summary statistics for more powerful genetic analysis.
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.
MTAG / Multi-Trait Analysis of GWAS
Allows users to conduct joint analysis of multiple traits. MTAG generates trait-specific effect estimates for each single-nucleotide polymorphism (SNP). The application can be applied to genome wide association studies (GWAS) summary statistics, including those not belonging to independent discovery samples, from an arbitrary number of traits. It aims to improve the rapidity and the efficiency of the detection of the genetic associations for each trait.
Enables tightly integrated comparative variant analysis and visualization of thousands of next generation sequencing (NGS) data samples and millions of variants. BasePlayer is a highly efficient and user-friendly software for biological discovery in large-scale NGS data. It transforms an ordinary desktop computer into a large-scale genomic research platform, enabling also a non-technical user to perform complex comparative variant analyses, population frequency filtering and genome level annotations under intuitive, scalable and highly-responsive user interface to facilitate everyday genetic research as well as the search of novel discoveries.
RiVIERA-MT / Risk Variant Inference using Epigenomic Reference Annotations to predict Multiple Trait-causing mutations
An integrative Bayesian fine-mapping model. RiVIERA-MT includes: 1) the ability to model epigenomic covariance of multiple related traits; 2) efficient posterior inference of causal configuration; 3) efficient Bayesian inference of enrichment parameters, allowing incorporation of large number of functional annotations; 4) simultaneously modeling the underlying heritability parameters. The goal of RiVIERA-MT is to infer for each single nucleotide polymorphism (SNP) in disease their posterior probability of disease association and to detect functional enrichments or depletions from the annotations, taking into account the underlying multi-trait epigenomic covariance.
Assess whether two association signals which are consistent with a shared causal variant. The COLOC method is appropriate for associations detected by GWAS (genome-wide association study). It is able to derive the output statistics from single single nucleotide polymorphism (SNP) summary statistics and to produce systematic meta-analysis type comparisons across multiple GWAS datasets. This tool gives several informations concerning candidate causal genes in associated intervals, and work in the understanding of complex diseases.
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