wiki
Genomics
Genome Wide Association Study
GWAS Tools (Kontou & Bagos, BioData Mining 2024)

GWAS Tools (Kontou & Bagos, BioData Mining 2024)

Table S1:

ToolURLReferenceUsePlatformDescription
NHGRI-EBI GWAS Cataloghttps://www.ebi.ac.uk/gwas/https://pubmed.ncbi.nlm.nih.gov/30445434DatabasewebA high-quality curated collection of published GWAS. Currently contains 6,652 publications, 561,096 top associations and 66,522 full summary statistics
dbGAPhttps://www.ncbi.nlm.nih.gov/gap/https://pubmed.ncbi.nlm.nih.gov/17898773/DatabasewebContains both IPD and summary data from GWAS. The latter are generally available to the public, while access to IPD  require varying levels of authorization
GWAScentralhttps://www.gwascentral.orghttps://pubmed.ncbi.nlm.nih.gov/36350644/DatabasewebPreviously known as the Human Genome Variation database of Genotype-to-Phenotype information. Currently contains over 72.5 million P-values for over 5000 studies testing over 7.4 million unique genetic markers investigating over 1700 unique phenotypes.
OpenGWAShttps://gwas.mrcieu.ac.ukhttps://www.biorxiv.org/content/10.1101/2020.08.10.244293v1DatabasewebA database of 346,288,362,703 genetic associations from 50,037 GWAS summary datasets
GWAS ATLAShttp://atlas.ctglab.nlhttps://pubmed.ncbi.nlm.nih.gov/31427789DatabasewebCurrently contains 4,756 GWAS from 473 unique studies across 3,302 unique traits. Each GWAS is accompanied by the results of MAGMA (i.e. gene-based) results, SNP heritability and genetic correlations with other GWAS in the database.
GeneATLAShttp://geneatlas.roslin.ed.ac.ukhttps://pubmed.ncbi.nlm.nih.gov/30349118DatabasewebA database of associations using the UK Biobank cohort. Currently contains data for 452,264 Individuals, 778 traits and 30 Million Variants
GWASROCShttps://gwasrocs.cahttps://pubmed.ncbi.nlm.nih.gov/31805043DatabasewebDatabase containing the largest and most comprehensive set of SNP-derived AUROCs.  The database currently houses 579 simulated populations (corresponding to 219 different conditions) and SNP data (odds ratio, risk allele frequency, and p-values) for 2886 unique SNPs
GBEbiobankengine.stanford.eduhttps://pubmed.ncbi.nlm.nih.gov/30520965DatabasewebContains summary statistics from over 750,000 individuals across three population cohorts: UK Biobank, Million Veterans Program and Biobank Japan
GTExhttps://www.gtexportal.org/home/https://pubmed.ncbi.nlm.nih.gov/32913098/DatabasewebThe Genotype-Tissue Expression project contains data of gene expression and splicing in 838 individuals over 49 tissues (see the Perspective by Wilson).
QTLbase2http://mulinlab.org/qtlbasehttps://pubmed.ncbi.nlm.nih.gov/36330927DatabasewebCompiles genome-wide QTL summary statistics for many human molecular traits across over 95 tissue/cell types and multiple biological conditions. Contains tens of millions significant genotype-molecular trait associations under different conditions
COLOCdbhttps://ngdc.cncb.ac.cn/colocdbhttps://pubmed.ncbi.nlm.nih.gov/37941154DatabasewebThe most comprehensive colocalization analysis by integrating publicly available GWASs, different types of xQTL and three different colocalization algorithms. Allows for GWAS-GWAS, GWAS-xQTL, and xQTL-xQTL comparisons
webTWAShttp://www.webtwas.nethttps://pubmed.ncbi.nlm.nih.gov/34669946DatabasewebCurrently contains data for over 1,389 full GWAS. It calculates the causal genes using single tissue expression imputation (MetaXcan and FUSION) or cross-tissue expression imputation (UTMOST). The users can also upload their own GWAS data
TSEA-DBhttps://bioinfo.uth.edu/TSEADB/https://pubmed.ncbi.nlm.nih.gov/33211888DatabasewebA database for trait-associated tissues. Uses TSEA to infer tissues in which trait-associated genes are enriched.Contains information of 5,019 GWAS summary statistics data sets for human complex traits and diseases (non-UKBB and UKBB)
PCGAhttps://pmglab.top/pcgahttps://pubmed.ncbi.nlm.nih.gov/35639771DatabasewebA web server to simultaneously estimate associated tissues/cell types and genes of complex diseases and traits. Includes data for 54 human tissues, 2,214 human single cell types and 4,384 mouse single cell types
LD Hubhttp://ldsc.broadinstitute.org/https://pubmed.ncbi.nlm.nih.gov/27663502DatabasewebA centralized database of GWAS results for 173 diseases/traits from publicly available resources and a web interface that automates the LD score regression analysis pipeline.
PheWAS Cataloghttps://phewascatalog.orghttps://pubmed.ncbi.nlm.nih.gov/24270849/DatabasewebThe PheWAS catalog contains the PheWAS results for 3,144 single-nucleotide polymorphisms (SNPs) present in the NHGRI GWAS Catalog
Phenoscannerhttp://www.phenoscanner.medschl.cam.ac.uk/https://pubmed.ncbi.nlm.nih.gov/31233103/DatabasewebA curated database with publicly available results from GWAS used for facilitating “phenome scans”. Currently contains over 65 billion associations and over 150 million unique genetic variants. Comes with a Python command-line tool.
QCGWAShttps://cran.r-project.org/web/packages/QCGWAS/index.htmlhttps://pubmed.ncbi.nlm.nih.gov/24395754/Quality ControlRAutomates the quality control of GWAS result files. Its main purpose is to facilitate the quality control of a large number of such files before meta-analysis.
DENTISThttps://zenodo.org/records/5516202https://pubmed.ncbi.nlm.nih.gov/34880243Quality ControlC/C++Leverages LD among SNPs to detect and eliminate errors in GWAS or LD reference and heterogeneity between the two
GWAS-SSFhttps://github.com/EBISPOT/gwas-summary-statistics-standardhttps://www.biorxiv.org/content/10.1101/2022.07.15.500230v2.fullQuality ControlPythonSpecifications for the first version of the GWAS-SSF format, which was developed to meet the requirements discussed with the community. GWAS-SSF consists of a tab-separated data file with well-defined fields and an accompanying metadata file
MungeSumstatshttps://neurogenomics.github.io/MungeSumstatshttps://pubmed.ncbi.nlm.nih.gov/34601555Quality ControlRA tool for the standardization and quality control of GWAS summary statistics. It can handle the most common summary statistic formats
GWASinspectorhttp://gwasinspector.comhttps://pubmed.ncbi.nlm.nih.gov/33416854/Quality ControlRDeveloped to facilitate and streamline this process and provide the user with a comprehensive report. It will also generate cleaned, harmonized GWAS files ready for meta-analysis
VCFhttps://github.com/MRCIEU/gwas-vcf-specification/releases/tag/1.0.0, https://github.com/mrcieu/gwas2vcfhttps://pubmed.ncbi.nlm.nih.gov/33441155Quality ControlPythonThe variant call format is used to store GWAS summary statistics along with open-source tools to be uses in downstream analyses.
SumStatsRehabhttps://github.com/Kukuster/SumStatsRehabhttps://pubmed.ncbi.nlm.nih.gov/36284273Quality ControlPythonA tool for data validation, restoration of missing data, correction and formatting
GQShttps://github.com/Xswapnil/GQS/https://pubmed.ncbi.nlm.nih.gov/36651666Quality ControlPythonIdentifies suspicious regions and prevents erroneous interpretations. Aassesses all measured SNPs that are in LD and compares the significance of trait association of each SNP to its LD value for the reported index SNP
GWAtoolboxhttps://github.com/cran/GWAtoolboxhttps://pubmed.ncbi.nlm.nih.gov/22155946/Quality ControlRContains three particular data quality aspects: data formatting, quality of the GWAS results and data consistency across studies
EasyQChttps://www.uni-regensburg.de/medizin/epidemiologie-praeventivmedizin/genetische-epidemiologie/software/index.htmlhttps://pubmed.ncbi.nlm.nih.gov/24762786/Quality ControlRA general protocol for conducting meta-analysis and carrying out QC to minimize errors and to guarantee maximum use of the data
GEARhttps://github.com/syntheke/GEARhttps://pubmed.ncbi.nlm.nih.gov/27552965Quality ControlJavaA tool that contains functions to identify significant sample overlap or heterogeneity between pairs of cohorts
EXTminus23andMehttps://github.com/Camzcamz/EXTminus23andMehttps://pubmed.ncbi.nlm.nih.gov/37713023Quality ControlRA tool to evaluate the quality of summary statistics after data removal and the suitability of these downsampled summary statistics for typical follow-up genetic analyses
GWASlabhttps://github.com/Cloufield/gwaslabhttps://jxiv.jst.go.jp/index.php/jxiv/preprint/view/370Quality ControlPythonA toolkit for handling GWAS summary statistics. Offers functionalities for converting most formats, standardization, normalization, harmonization, filtering and visualization.
OATHhttps://github.com/gc5k/GEARhttps://pubmed.ncbi.nlm.nih.gov/28122950ReconstructionJavaReproduces reported results from a GWAS and recovers underreported results from other alternative models with a different combination of nuisance parameters
Metasubtracthttps://cran.r-project.org/web/packages/MetaSubtracthttps://pubmed.ncbi.nlm.nih.gov/32696040ReconstructionRSubtracts the results of the validation cohort from meta-GWAS summary statistics analytically
LMORhttps://github.com/lukelloydjones/ORShinyhttps://pubmed.ncbi.nlm.nih.gov/29429966ReconstructionRPerforms transformations from the genetic effects estimated under the Linear Mixed Model to the Odds Ratio that only rely on summary statistics
ReACthttps://github.com/Paschou-Lab/ReACthttps://pubmed.ncbi.nlm.nih.gov/35581276ReconstructionC/C++Performs genotype reconstruction for case-control GWAS summary statistics. It includes three modules: Meta-analysis, group PRS and case-case GWAS.
simGWAShttp://github.com/chr1swallace/simGWAShttps://pubmed.ncbi.nlm.nih.gov/30371734ReconstructionRSimulates GWAS summary data without individual data as an intermediate step
spkmthttps://osf.io/spkmt/https://pubmed.ncbi.nlm.nih.gov/37518004ReconstructionRMethod to derive GWAS summary statistics for one parent when observations have only been made on the offspring and another parent
FAPIhttps://pmglab.top/fapi/https://pubmed.ncbi.nlm.nih.gov/26306642ImputationExecutableFast and accurate P-value imputation method that utilizes summary statistics of common variants. Its computational cost is linear with the number of untyped variants
impGhttps://bogdan.dgsom.ucla.edu/pages/impg/https://pubmed.ncbi.nlm.nih.gov/24990607ImputationC/C++Uses the multivariate normal distribution and LD from external source
SSimphttps://github.com/zkutalik/ssimp_softwarehttps://pubmed.ncbi.nlm.nih.gov/29782485/ImputationC/C++Uses the multivariate normal distribution and LD from external source
RAISS https://gitlab.pasteur.fr/statistical-genetics/raisshttps://pubmed.ncbi.nlm.nih.gov/31173064ImputationPythonUses LD and the multivariate normal distriburion along with several optimizations
LS-METAhttps://github.com/ren328/LS-Metahttps://pubmed.ncbi.nlm.nih.gov/37369060ImputationRImputes both genetic and environmental components of a trait using both SNP-trait and omics-trait association summary data
DIST/DISTMIXhttps://dleelab.github.io/distmix/https://pubmed.ncbi.nlm.nih.gov/26059716, https://pubmed.ncbi.nlm.nih.gov/23990413ImputationExecutableUses the multivariate normal distribution and the correlation structure from a relevant reference population. DISTMIX is the extension to mixed ethnicity cohorts
ARDISShttps://github.com/BorgwardtLab/ARDISShttps://pubmed.ncbi.nlm.nih.gov/30423082ImputationPythonImputes missing summary statistics in mixed-ethnicity cohorts through Gaussian Process Regression and automatic relevance determination
LSimputinghttps://github.com/ren328/LSimputinghttps://pubmed.ncbi.nlm.nih.gov/37181332ImputationRA nonparametric method for large-scale imputation of the genotype effects. If a sample of IPD is available the method allows for nonlinear SNP-trait associations and predictions
DISSCOhttps://yunliweb.its.unc.edu/DISSCO/https://pubmed.ncbi.nlm.nih.gov/25810429ImputationJavaUses the multivariate normal distribution and LD, and allows for covariates
Adapt-Mixhttps://github.com/dpark27/adapt_mixhttps://pubmed.ncbi.nlm.nih.gov/26072481ImputationPythonCombines information across all available reference panels to produce estimates of local genetic correlation structure for summary statistics-based methods in arbitrary populations
IGESShttps://github.com/daviddaigithub/IGESShttps://pubmed.ncbi.nlm.nih.gov/28498950IPD+SDRUses variational inference to increase statistical power and improve accuracy of risk prediction by integrating individual level genotype data and summary statistics
MetaGIMhttps://github.com/fushengstat/MetaGIMhttps://pubmed.ncbi.nlm.nih.gov/36964712IPD+SDRA divide and conquer method to increase inference efficiency by incorporating aggregated summary information from other sources to an IPD analysis
LEPhttps://github.com/daviddaigithub/LEPhttps://pubmed.ncbi.nlm.nih.gov/30307540IPD+SDRIntegrates IPD and SD by Leveraging Pleiotropy to increase the statistical power of risk variants identification and the accuracy of risk prediction
PolyGIMhttps://github.com/fushengstat/PolyGIMhttps://pubmed.ncbi.nlm.nih.gov/37437002IPD+SDRUses polytomous logistic regression to investigate disease subtype heterogeneity in situations when only summary data is available
GWASmetahttps://github.com/sjl-sjtu/GWAS_metahttps://pubmed.ncbi.nlm.nih.gov/35286307Meta-analysisRA method for the optimal ABF in the GWAS meta-analysis. Uses shotgun stochastic search  to improve the Bayesian GWAS meta-analysis framework
nGWAMAhttps://github.com/baselmans/multivariate_GWAMAhttps://pubmed.ncbi.nlm.nih.gov/30643256/Meta-analysisRPerforms multivariate meta-analysis correcting for sample overlap
METALhttp://csg.sph.umich.edu/abecasis/Metal/https://pubmed.ncbi.nlm.nih.gov/20616382/Meta-analysisC/C++A versatile and efficient tool for meta-analysis of GWAS. It can combine test statistics and standard errors, or p-values across studies
PLINKhttps://www.cog-genomics.org/plink/https://pubmed.ncbi.nlm.nih.gov/17701901/Meta-analysisC/C++A versatile program which supports data management, quality control, and common statistical computations including meta-analysis
GWAMAhttps://genomics.ut.ee/en/toolshttps://pubmed.ncbi.nlm.nih.gov/20509871/Meta-analysisExecutableA flexible, open-source tool for meta-analysis of GWAS. It incorporates a variety of error trapping facilities, and provides a range of meta-analysis summary statistics
YAMAShttps://github.com/cmeesters/yamashttps://pubmed.ncbi.nlm.nih.gov/22971100/Meta-analysisC/C++Meta-analysis including missing SNPs identified with LD (proxy SNPs)
GWARhttp://www.compgen.org/tools/GWARhttps://pubmed.ncbi.nlm.nih.gov/28108451/Meta-analysisStata/webAnalysis and meta-analysis of GWAS using standard as well as robust methods (MAX, MIN2, MERT)
MAGENTAhttps://software.broadinstitute.org/mpg/magenta/https://pubmed.ncbi.nlm.nih.gov/20714348/Meta-analysisMatlabMeta-analysis with gene set enrichment analysis (GSEA)
CPBayeshttps://cran.r-project.org/web/packages/CPBayes/index.htmlhttps://pubmed.ncbi.nlm.nih.gov/29432419Meta-analysisRBayesian method for studying cross-phenotype genetic associations.
metaSKAThttps://cran.r-project.org/web/packages/MetaSKAT/index.htmlhttps://pubmed.ncbi.nlm.nih.gov/23768515Meta-analysisRExtensions of the Burden Test, SKAT and Optimal SKAT (SKAT-O) for multiple studies.
METACARPAhttps://github.com/hmgu-itg/metacarpahttps://pubmed.ncbi.nlm.nih.gov/23424128Meta-analysisC/C++Meta-analysis of GWAS with overlapping or related samples, when details of the overlap or relatedness are unknown
metaCCAhttps://www.bioconductor.org/packages/release/bioc/html/metaCCA.htmlhttps://pubmed.ncbi.nlm.nih.gov/27153689/Meta-analysisRMultivariate analysis and meta-anaysis of GWAS. It uses canonical correlation analysis and employs a covariance shrinkage algorithm to achieve robustness
metaUSAThttps://github.com/RayDebashree/metaUSAThttps://pubmed.ncbi.nlm.nih.gov/29226385Meta-analysisRA method for multiple traits. It is robust to the association structure of correlated traits. It can also be used to analyze a single trait over multiple studies, accounting for overlapping samples
CPASSOChttp://hal.case.edu/~xxz10/zhu-web/ https://pubmed.ncbi.nlm.nih.gov/25500260/Meta-analysisRMethod applicable to a multivariate phenotype containing any type of components including continuous, categorical and survival phenotypes, as well as to samples consisting of families or unrelated samples
GCPBayeshttps://github.com/tbaghfalaki/GCPBayeshttps://pubmed.ncbi.nlm.nih.gov/33368447Meta-analysisRBayesian meta-analysis methods for pleiotropy that extend CPBayes to the gene or pathway level
MetABFhttps://github.com/trochet/metabfhttps://pubmed.ncbi.nlm.nih.gov/30920090Meta-analysisRA simple Bayesian framework for performing integrative meta-analysis across multiple GWAS
rareMETALShttps://genome.sph.umich.edu/wiki/Rare_Variant_Analysis_and_Meta-Analysishttps://pubmed.ncbi.nlm.nih.gov/30016313Meta-analysisRWorks even when the data contain large amounts of missing values. Uses a score statistic called PCBS (partial correlation based score statistic) for conditional analysis of single-variant and gene-level associations
meta-simulationhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5863759/https://pubmed.ncbi.nlm.nih.gov/29577025Meta-analysisRA tool to implement an alternate strategy for the additive model based on simulating data for the individual studies
metaGAPhttps://www.devlaming.eu/metagap.htmlhttps://pubmed.ncbi.nlm.nih.gov/28095416Meta-analysiswebA versatile tool for calculating the statistical power of a meta-analysis of GWAS results and of the polygenic-score R² in a hold-out sample
rfdrhttp://bioinformatics.ust.hk/RFdr.htmlhttps://pubmed.ncbi.nlm.nih.gov/28067114Meta-analysisRA method for replication of GWAS. Provides the most powerful significance levels when controlling the FDR in the two-stage study
jlfdrhttps://bioinformatics.hkust.edu.hk/Jlfdr.htmlhttps://pubmed.ncbi.nlm.nih.gov/28011772Meta-analysisRA joint analysis method based on controlling the joint local false discovery rate
RRatehttp://bioinformatics.ust.hk/RRate.htmlhttps://pubmed.ncbi.nlm.nih.gov/27687799Meta-analysisRA Bayesian probabilistic measure of the Replication Rate with which we can determine the sample size of the replication study and to check the consistency between the primary  and the replication study.
MAJARhttps://github.com/JustinaXie/MAJARhttps://pubmed.ncbi.nlm.nih.gov/37519295Meta-analysisRMethod to jointly test prognostic and predictive effects in meta-analysis without the need of using an independent cohort for replication of the detected biomarkers
sPLINKhttps://exbio.wzw.tum.de/splink/https://pubmed.ncbi.nlm.nih.gov/35073941Meta-analysisPythonPerforms privacy-aware GWAS on distributed datasets while preserving accuracy
XPEBhttps://med.stanford.edu/tanglab/software/XPEB.htmlhttps://pubmed.ncbi.nlm.nih.gov/25892113Meta-analysisRAn empirical Bayes approach to improve the power of GWAS in a minority population by exploiting information from another ethnic population
MESChttps://github.com/douglasyao/meschttps://pubmed.ncbi.nlm.nih.gov/32424349HeritabilityPythonEstimates the proportion of heritability mediated by assayed gene expression levels using linkage disequilibrium (LD) scores and eQTL
GENESIShttps://github.com/yandorazhang/GENESIShttps://pubmed.ncbi.nlm.nih.gov/30104760HeritabilityRUses LD information and a Likelihood-based approach to estimate variants effect-size distributions. It also allows users to make predictions regarding yield of future GWAS.
GWEHShttps://gitlab.com/elcortegano/GWEHShttps://pubmed.ncbi.nlm.nih.gov/31344961HeritabilityRCalculates the distribution of effect sizes of SNPs affecting traits, as well as their contribution to heritability. It also allows for predictions as new loci are found
GWIZhttps://github.com/jonaspatronjp/GWIZ-Rscript/ https://gwasrocs.cahttps://pubmed.ncbi.nlm.nih.gov/31805043HeritabilityRA method to generate ROC curves and calculate the AUROC
SummaryAUChttps://github.com/lsncibb/SummaryAUChttps://pubmed.ncbi.nlm.nih.gov/30911754HeritabilityRA method for approximating the AUC and its variance of a PRS when only the summary level data of the validation dataset are available.
GxEsumhttps://github.com/honglee0707/GxEsumhttps://pubmed.ncbi.nlm.nih.gov/34154633HeritabilityRA method for estimating the phenotypic variance explained by genome-wide GxE
VarExphttps://gitlab.pasteur.fr/statistical-genetics/VarExphttps://pubmed.ncbi.nlm.nih.gov/29726908HeritabilityRA method that allows for the estimation of the proportion of phenotypic variance explained. It allows for a range of models to be evaluated, including marginal genetic effects, GxE interaction effects and both effects jointly
SumVGhttps://github.com/lab-hcso/SumVghttps://pubmed.ncbi.nlm.nih.gov/21618601/HeritabilityRProvides estimates of the sum of heritability explained by all true susceptibility variants in GWAS. It also estimates the standard error based on re-sampling approaches
SumHerhttp://dougspeed.com/sumher/https://pubmed.ncbi.nlm.nih.gov/30510236/HeritabilityExecutableEstimates the SNP Heritability of a trait, Heritability Enrichments and Genetic Correlations between traits
AVENGEMEhttps://sites.google.com/site/fdudbridge/software/https://pubmed.ncbi.nlm.nih.gov/26189816HeritabilityRA method to estimate the variance in disease liability explained by large sets of genetic markers. Uses polygenic scores, based on the formula for the non-centrality parameter of the association test of the score.
HESShttps://github.com/huwenboshi/hesshttps://pubmed.ncbi.nlm.nih.gov/26189816HeritabilityPythonProvides utilities for estimating and analyzing local SNP-heritability and genetic covariance
HAMSTAhttps://github.com/tszfungc/HAMSTAhttps://pubmed.ncbi.nlm.nih.gov/37875120/HeritabilityPythonEstimates the heritability explained by local ancestry in admixture mapping studies. It also quantifies inflation in test statistics that is not contributed by local ancestry effects, and determines significance threshold for admixture mapping
HEELShttps://github.com/huilisabrina/HEELShttps://pubmed.ncbi.nlm.nih.gov/38040712/HeritabilityPythonUses REML to produce accurate and precise local heritability estimates
LDERhttps://github.com/shuangsong0110/LDERhttps://pubmed.ncbi.nlm.nih.gov/35421325HeritabilityRExtends the LDSC method making full use of the information from the LD matrix and provides more accurate estimates of heritability and confounding inflation
s-LDSChttps://github.com/bulik/ldschttps://pubmed.ncbi.nlm.nih.gov/26414678HeritabilityRExtension of LDSC for partitioning heritability
FMRhttps://github.com/lukejoconnor/FMRhttps://pubmed.ncbi.nlm.nih.gov/34326547HeritabilityMatlabA method-of-moments estimator of the effect-size distribution. The coefficients quantify the heritability explained by components of a mixture model for the effect-size distribution
GWAS-Causal-Effects-Modelhttps://github.com/dominicholland/GWAS-Causal-Effects-Modelhttps://pubmed.ncbi.nlm.nih.gov/32427991HeritabilityMatlabRandom effects model for estimating the causal variants and their effect size distribution from a dense panel
GCSChttps://github.com/ksiewert/GCSChttps://pubmed.ncbi.nlm.nih.gov/35108496HeritabilityPythonUses TWAS results in a gene co-regulation score regression, to identify gene sets that are enriched for disease heritability explained by predicted expression
JEPEG/JEPEGMIXhttp://dleelab.github.io/jepegmix/ , http://dleelab.github.io/jepeghttps://pubmed.ncbi.nlm.nih.gov/26428293 , https://pubmed.ncbi.nlm.nih.gov/25505091Gene-based testsExecutableA gene-based method for testing the joint effects on trait for SNPs functionally associated with a gene (eQTLs)
VEGAShttps://cran.r-project.org/web/packages/snpsettest/index.htmlhttps://pubmed.ncbi.nlm.nih.gov/20598278/Gene-based testsROne of the first multivariate methods. Takes account of LD between markers in a gene by using simulation based on the LD of a reference panel
AgglomerativLDhttps://github.com/ryurko/Agglomerative-LD-loci-testinghttps://pubmed.ncbi.nlm.nih.gov/34459489Gene-based testsRCaptures LD of SNPs falling in nearby genes, which induces correlation of gene-based test statistics
sumFREGAThttps://cran.r-project.org/web/packages/sumFREGAThttps://pubmed.ncbi.nlm.nih.gov/35653402Gene-based testsROffers a wide range of gene-based methods to combine. It allows the user to arbitrarily define a set of these methods, weighting functions and probabilities of genetic variants being causal.
LDAK-GBAThttps://dougspeed.com/ldak-gbat/https://pubmed.ncbi.nlm.nih.gov/36480927Gene-based testsExecutableA computationally efficient method for gene-based association testing. Produces well-calibrated p values and is significantly more powerful than existing tools
nMAGMAhttps://github.com/sldrcyang/nMAGMAhttps://pubmed.ncbi.nlm.nih.gov/33230537Gene-based testsRAn extension of MAGMA which extends the lists of genes that can be annotated to SNPs by integrating local signals, long-range regulation signals, and tissue-specific gene networks. It also provides tissue-specific risk signals, which are useful for understanding disorders with multitissue origins
MKATRhttp://www.github.com/baolinwu/mkatr.https://pubmed.ncbi.nlm.nih.gov/29558699Gene-based testsRThe method calculates the correlation of the the test Z-statistics across variants using LD from a population reference panel. Incorporates various tests (sum test, SKAT, adaptive test)
GPAhttps://github.com/Biocomputing-Research-Group/GPAhttps://pubmed.ncbi.nlm.nih.gov/31392781Gene-based testsC/C++A general gene-based p-value adaptive combination approach (GPA) which can integrate association evidence of multiple SNPs. It is applicable to both continuous and binary traits and also to multiple studies
OWChttps://github.com/Xuexia-Wang/OWC-R-packagehttps://pubmed.ncbi.nlm.nih.gov/36597047Gene-based testsRA gene-based test that incorporates different weighting schemes and includes several popular methods as its special cases (burden test, weighted sum of squared score test [SSU], weighted sum statistic [WSS], SNP-set Kernel Association Test [SKAT], and score test)
MCAhttps://github.com/biostatpzeng/MCAhttps://pubmed.ncbi.nlm.nih.gov/36042399Gene-based testsRImplements 22 different gene-based methods, including  linear regression, higher criticism tests, Berk-Jones tests, burden test; SKAT and SKAT-O, Simes and GATES, aggregated Cauchy association test and more
COMBAThttps://cran.r-project.org/web/packages/COMBAT/https://pubmed.ncbi.nlm.nih.gov/28878002Gene-based testsRA combined association test for genes, which incorporates strengths from existing gene-based tests and shows higher overall performance than individual tests
oTFisherhttps://cran.r-project.org/web/packages/TFisher/index.htmlhttps://pubmed.ncbi.nlm.nih.gov/36468009Gene-based testsRThe omnibus thresholding Fisher’s method for performing SNP-set and gene-based tests
H-MAGMAhttps://github.com/thewonlab/H-MAGMAhttps://pubmed.ncbi.nlm.nih.gov/36289406Gene-based testsRExtends MAGMA by incorporating 3D chromatin configuration in assigning variants to their putative target genes
eMAGMAhttps://github.com/eskederks/eMAGMA-tutorialhttps://pubmed.ncbi.nlm.nih.gov/33624746Gene-based testsC/C++Gene-based approach with a modification of MAGMA, leverages significant tissue-specific cis-eQTL information to assign SNPs to putative genes
EPIChttps://github.com/rujinwang/EPIChttps://pubmed.ncbi.nlm.nih.gov/35709291Gene-based testsRMethod that relates large-scale GWAS summary statistics to cell-type-specific gene expression measurements from single-cell RNA sequencing
GAMBIThttps://github.com/corbinq/GAMBIThttps://pubmed.ncbi.nlm.nih.gov/33320851Gene-based testsC/C++Integrates heterogeneous annotations with GWAS summary statistics for gene-based analysis, using various coding and tissue-specific regulatory annotations. Allows various tests like SKAT, minP, ACAT, HMP etc
MARShttps://github.com/junghyunJJ/marsRhttps://pubmed.ncbi.nlm.nih.gov/33931127Gene-based testsRFinds associations between variants in risk loci and a phenotype, considering the causal status of variants
GBJhttps://cran.r-project.org/web/packages/GBJ/index.htmlhttps://pubmed.ncbi.nlm.nih.gov/33041403Gene-based testsRGeneralized Berk-Jones test for the association between a SNP-set and outcome by accounting for LD. Includes also tests for Berk-Jones (BJ), Higher Criticism (HC), Generalized Higher Criticism (GHC), Minimum p-value (minP), and an an omnibus test (OMNI) which integrates information from each of the tests.
GENE-Ehttps://github.com/ramachandran-lab/geneehttps://pubmed.ncbi.nlm.nih.gov/32542026Gene-based testsRA gene-based test using an empirical Bayesian approach and a mixture of normal distributions over the (regularized) effect size estimates
PEGASUShttps://github.com/ramachandran-lab/PEGASUShttps://pubmed.ncbi.nlm.nih.gov/27489002Gene-based testsPerlGene-based method that uses an analytical approach to compute gene-level P-values of observed gene scores according to a null distribution modeling LD
FSThttps://cran.r-project.org/web/packages/FSTpackage/https://pubmed.ncbi.nlm.nih.gov/28844485Gene-based testsRCombining dispersion and burden tests and an efficient perturbation method for individual gene/large gene-set/genome wide analysis
ACAThttps://github.com/yaowuliu/ACAThttps://pubmed.ncbi.nlm.nih.gov/30849328Gene-based testsRA gene-based method using the Cauchy Combination Test. Includes also an omnibus procefure combining SKAT, BT and ACAT
DOThttps://github.com/dmitri-zaykin/Total_Decorhttps://pubmed.ncbi.nlm.nih.gov/32287273Gene-based testsRDecorrelation-based approach (DOT) for combining SNP-level summary statistics (or, equivalently, P-values)
fastBAThttps://yanglab.westlake.edu.cn/software/gctahttps://pubmed.ncbi.nlm.nih.gov/27604177Gene-based testsC/C++Performs a fast set-based association analysis for human complex traits using summary-level data from GWAS and LD
KGGhttp://pmglab.top/kggsee/#/https://pubmed.ncbi.nlm.nih.gov/30101339Gene-based testsJavaConditional test that uses a sequential analysis with a linear combination of chi-square statistics
TShttps://github.com/Jianjun-CN/c-code-for-TShttps://pubmed.ncbi.nlm.nih.gov/32366212Gene-based testsExecutableUses a truncated method to find the genes that have a true contribution to the genetic association
HSVS-Mhttps://github.com/yiyangphd/HSVSMhttps://pubmed.ncbi.nlm.nih.gov/34787916Gene-based testsRMultivariate hierarchically structured variable selection model, a flexible Bayesian model that tests the association of a gene with multiple correlated traits
GATEShttp://pmglab.top/kggsee/#/https://pubmed.ncbi.nlm.nih.gov/21397060/Gene-based testsJavaAn extended Simes test that integrates functional information and association evidence to combine the p-values of the SNP within a gene to obtain an overall p-value
HYSThttp://pmglab.top/kgg/https://pubmed.ncbi.nlm.nih.gov/22958900/Gene-based testsJavaA set-based statistical mwthod combining the extended Sime’s test and the scaled chi-square test to examine the overall association significance in a set of SNPs
GCTAhttps://yanglab.westlake.edu.cn/software/gcta/https://pubmed.ncbi.nlm.nih.gov/22426310Gene-based testsC/C++An approximate conditional and joint association analysis that uses LD from a reference sample
aSPUpath2https://github.com/ChongWu-Biostat/aSPUpath2https://pubmed.ncbi.nlm.nih.gov/29411426/GSARIntegrates gene expression reference weights, GWAS summary data, LD information, and candidate pathways to identify pathways whose expression is associated with complex traits
GIGSEAwww.github.com/zhushijia/GIGSEAhttps://pubmed.ncbi.nlm.nih.gov/30010968GSARUses GWAS and eQTL to infer differential gene expression and interrogate gene set enrichment for the trait-associated SNPs. By incorporating expression data it naturally accounts for factors such as gene size, gene boundary, SNP distal regulation and multiple-marker regulation
GAUSShttps://github.com/diptavo/GAUSShttps://pubmed.ncbi.nlm.nih.gov/33730541GSARTests for any self-contained association between a phenotype and a gene-set and produces a p-value for the association.
GSA-SNP2https://sourceforge.net/projects/gsasnp2https://pubmed.ncbi.nlm.nih.gov/29562348GSAC/C++A method for pathway enrichment analysis of GWAS P-value data. It accepts also gene-wise p-values (obtained from other methods) and outputs pathway gene sets ‘enriched’ with genes associated with the given phenotype
VSEAMShttps://github.com/ollyburren/vseamshttps://pubmed.ncbi.nlm.nih.gov/25170024GSARA non-parametric SNP set enrichment method to test for enrichment of GWAS signals in functionally defined loci using P-values
dmGWAShttps://bioinfo.uth.edu/dmGWAS/https://www.ncbi.nlm.nih.gov/pubmed/21045073/GSAExecutableA dense module searching method to identify candidate subnetworks or genes for complex diseases by integrating PPI network. Extensively searches for subnetworks enriched with low P-value genes.
iGSE4GWAShttp://gsea4gwas.psych.ac.cnhttps://pubmed.ncbi.nlm.nih.gov/20435672/GSAwebDetects pathways associated with traits by applying an improved gene set enrichment analysis. Implements also a follow-up functional analysis for SNPs in trait-associated pathways identified. Uses LD and putative functional annotation from Ensembl,ENCODE and eQTLs
Enrichrhttps://maayanlab.cloud/Enrichrhttps://pubmed.ncbi.nlm.nih.gov/27141961/GSAwebA gene set search engine that enables the querying of hundreds of thousands of annotated gene sets. Enrichr uniquely integrates knowledge from many high-profile projects to provide synthesized information about mammalian genes and gene sets.
SNPratio testhttps://sourceforge.net/projects/snpratiotest/https://pubmed.ncbi.nlm.nih.gov/19620097/GSAPerlCompares the proportion of significant to all SNPs within genes that are part of a pathway and computes an empirical P-value based on comparisons to ratios in datasets where the assignment of case/control status has been randomized.
g:Profilerhttps://biit.cs.ut.ee/gprofiler/gosthttps://pubmed.ncbi.nlm.nih.gov/37144459/GSAweb/RIintegrates many databases, including Gene Ontology, KEGG and TRANSFAC, to provide a comprehensive and in-depth analysis of gene lists. It also provides interactive and intuitive user interfaces and supports ordered queries and custom statistical backgrounds, among other settings.
DAVIDhttps://david.ncifcrf.gov/https://pubmed.ncbi.nlm.nih.gov/35325185/GSAweb/RAn enrichment tool with functionalities for different types of omics data including GWAS. It accepts gene or SNP-list as input and provide API ensuring interoperability. For analysis it uses ORA and GSEA
WebGestalthttp://www.webgestalt.org/https://pubmed.ncbi.nlm.nih.gov/31114916/GSAweb/RAn enrichment tool with functionalities for different types of omics data including GWAS. It accepts gene or SNP-list as input and provide API ensuring interoperability. For analysis it uses ORA, GSEA and Network Topology-based Analysis
PANTHERhttp://www.pantherdb.org/https://pubmed.ncbi.nlm.nih.gov/33290554/GSAwebAn enrichment tool with functionalities for different types of omics data including GWAS. It accepts gene or SNP-list as input and provide API ensuring interoperability. For analysis it uses ORA and GSEA
deTShttps://cran.r-project.org/web/packages/deTS/index.htmlhttps://pubmed.ncbi.nlm.nih.gov/30824912/GSAweb/RPerforms tissue-specific enrichment analysis (TSEA) for detecting tissue-specific genes and for enrichment test of different forms of query data.
DESEhttps://pmglab.top/pcgahttps://pubmed.ncbi.nlm.nih.gov/31694669/GSAwebDetects the causal tissues of complex  traits according to selective expression of disease-associated genes
PAPAhttps://sourceforge.net/projects/papav1/files/https://pubmed.ncbi.nlm.nih.gov/26568630GSAC/C++A flexible tool for pleiotropic pathway analysis utilizing GWAS summary results
GEMBhttps://github.com/cochran4/GEMBhttps://pubmed.ncbi.nlm.nih.gov/33034635GSAMatlabA method that combines gene weights from model predictions and gene ranks from genome-wide association studies into a weighted gene-set test
GENOMICperhttps://cran.r-project.org/web/packages/genomicper/index.htmlhttps://pubmed.ncbi.nlm.nih.gov/22973544/GSARUses SNP association p-values and permutes them by rotation with respect to the genomic locations. The joint gene p-values are calculated using Fisher’s combination test and pathways’ association tested using the hypergeometric test
GWABhttps://www.inetbio.org/gwab/https://pubmed.ncbi.nlm.nih.gov/28449091GSAwebTrait-associated genes with sub-threshold significance score can be rescued by network connections to other significant candidates
Infernohttp://inferno.lisanwanglab.org/https://pubmed.ncbi.nlm.nih.gov/30113658GSAweb/PythonMethod which integrates diverse functional genomics data sources to identify causal noncoding variants. Characterizes the relevant tissue contexts, target genes, and downstream biological processes affected by functional variants.Uses COLOC, WebGestalt, LDSC and MetaXcan.
Mergeomicshttp://mergeomics.research.idre.ucla.eduhttps://pubmed.ncbi.nlm.nih.gov/34048577GSAweb/RWeb server which uses summary statistics of multi-omics association studies (GWAS, EWAS, TWAS, PWAS, etc) and performs correction for LD, GSEA, meta-analysis and identification of essential regulators of disease-associated pathways and networks
GenToShttps://github.com/genepi-freiburg/gentoshttps://pubmed.ncbi.nlm.nih.gov/27612175GSAJavaCalculates an appropriate statistical significance threshold and then searches for trait-associated variants in summary statistics from human GWAS
aSPUhttps://cran.r-project.org/web/packages/aSPU/https://pubmed.ncbi.nlm.nih.gov/27592708GSA/Gene-BasedRPerforms adaptive gene-based test and pathway-based test for association analysis of multiple traits. The tests are adaptive at both the SNP- and trait-levels, thus maintaining high power across a wide range of situations. The methods can be applied to mixed types of traits, and to Z-statistics or P-values
snpGeneSetshttps://www.umc.edu/SoPH/Departments-and-Faculty/Data-Science/Research/Services/Software.html/https://www.ncbi.nlm.nih.gov/pubmed/27807048/GSA/Gene-BasedRIntegrates local genomic annotation databases and provides genome-wide annotation for SNP, Gene and gene sets. It aims to support interpretation of GWAS results and performing post-analysis
PascalXhttps://github.com/BergmannLab/PascalXhttps://pubmed.ncbi.nlm.nih.gov/37137228GSA/Gene-BasedPythonProvides fast and accurate mapping of SNP-wise GWAS data. It allows for scoring genes and annotated gene sets for enrichment signals based on data from, both, single GWAS and pairs of GWAS
PASCALhttps://www2.unil.ch/cbg/index.php?title=Pascalhttps://pubmed.ncbi.nlm.nih.gov/26808494GSA/Gene-BasedJavaComputes gene and pathway scores from SNP-phenotype associations. For gene score computation, implements analytic and efficient numerical solutions to calculate test statistics. For pathway scoring, it uses a modified Fisher method
MAGMAhttps://ctg.cncr.nl/software/magmahttps://pubmed.ncbi.nlm.nih.gov/25885710/GSA/Gene-BasedC/C++Uses p-values and performs gene-based and gene-set analysis as well as meta-analysis
FUMAhttps://fuma.ctglab.nlhttps://pubmed.ncbi.nlm.nih.gov/29184056/GSA/Gene-BasedwebAn integrative web-based platform using information from multiple biological resources to facilitate functional annotation of GWAS results, gene prioritization and interactive visualization. It accommodates positional, expression quantitative trait loci (eQTL) and chromatin interaction mappings, and provides gene-based, pathway and tissue enrichment results.
chromMAGMAhttps://github.com/lawrenson-lab/chromMAGMA-publichttps://pubmed.ncbi.nlm.nih.gov/35777959GSA/Gene-BasedRΜethod to identify candidate risk genes based on the presence of risk variants within noncoding regulatory elements
SOJOhttps://github.com/zhenin/sojohttps://pubmed.ncbi.nlm.nih.gov/29198721/Fine-mappingRPenalized selection operator for jointly analyzing multiple variants (SOJO) within each mapped locus on the basis of LASSO regression derived from summary association statistics
PolyFun/PolyLochttps://github.com/omerwe/polyfunhttps://pubmed.ncbi.nlm.nih.gov/33199916Fine-mappingPythonEstimates prior causal probabilities for SNPs, which can then be used by fine-mapping methods like SuSiE or FINEMAP. It can aggregate polygenic data from across the entire genome and hundreds of functional annotations.
SusieRhttps://github.com/stephenslab/susieRhttps://pubmed.ncbi.nlm.nih.gov/35853082Fine-mappingRPerforms variable selection in multiple regression using a Bayesian version of stepwise selection approach, and is particularly well-suited to settings where some of the variables are highly correlated
FINEMAPhttp://www.christianbenner.comhttps://pubmed.ncbi.nlm.nih.gov/26773131Fine-mappingExecutableApplies a shotgun stochastic search algorithm and can identify causal SNPs, estimate their effect sizes and the heritability contribution of causal SNPs in genomic regions associated with complex traits
flashfmhttps://jennasimit.github.io/flashfm/https://pubmed.ncbi.nlm.nih.gov/34686674Fine-mappingRUses summary statistics to jointly fine-map genetic associations for several related quantitative traits in a Bayesian framework that leverages information between the traits
MsCAVIARhttps://github.com/nlapier2/MsCAVIARhttps://pubmed.ncbi.nlm.nih.gov/34543273Fine-mappingC/C++A method for fine-mapping by leveraging information from multiple studies. One important application area is trans-ethnic fine mapping.
CAVIARBFhttps://bitbucket.org/Wenan/caviarbf/src/master/https://pubmed.ncbi.nlm.nih.gov/25948564Fine-mappingC/C++A fine-mapping method that combines CAVIAR with Bayesian inference using marginal test statistics
PICS2https://pics2.ucsf.eduhttps://pubmed.ncbi.nlm.nih.gov/33624747Fine-mappingwebProbabilistic Identification of Causal SNPs is a fine-mapping tool for determining the likelihood that each SNP in LD with a reported index SNP is a true causal polymorphism
ANNOREhttps://github.com/vafisher/AnnoREhttps://pubmed.ncbi.nlm.nih.gov/34302344Fine-mappingRUses local LD structure and functional annotation, accross many categories, to prioritize the most plausible causal SNPs. It is based on multiple regression with differential shrinkage via random effects
JOINTSUMhttps://github.com/yangq001/conditionalhttps://pubmed.ncbi.nlm.nih.gov/32275709Fine-mappingRA simple and general approach based on conditional analysis of a locus on multiple traits, overcoming the shortcomings of other methods.
HAPRAPhttp://apps.biocompute.org.uk/haprap/https://pubmed.ncbi.nlm.nih.gov/27591082Fine-mappingPythonAn empirical iterative method, that enables fine mapping using haplotype information from an individual-level reference panel.
GSRhttps://github.com/li-lab-mcgill/GSRhttps://pubmed.ncbi.nlm.nih.gov/32817676Fine-mappingPythonDetects causal gene sets for complex traits using gene score regression while accounting for gene-to-gene correlations. It can operate either on GWAS summary statistics or gene expression
RSShttps://github.com/stephenslab/rsshttps://pubmed.ncbi.nlm.nih.gov/29399241Fine-mappingMatlabIt is a generally-applicable framework for multiple-SNP analysis. Uses a “Regression with Summary Statistics” (RSS) likelihood, which relates the multiple regression coefficients to univariate regression results
JAMhttps://github.com/pjnewcombe/R2BGLiMShttps://pubmed.ncbi.nlm.nih.gov/27027514Fine-mappingRBayesian penalized regression that accounts for SNP correlation and finds SNPs that best explain the complete joint pattern of marginal effects
PAINTORhttps://github.com/gkichaev/PAINTOR_V3.0https://pubmed.ncbi.nlm.nih.gov/27663501/Fine-mappingC/C++Integrates functional genomic data with association strength from potentially multiple populations (or traits) to prioritize variants for follow-up analysis
DAPhttps://github.com/xqwen/daphttps://pubmed.ncbi.nlm.nih.gov/27236919/Fine-mappingC/C++Deterministic approximation of posteriors enables highly efficient and accurate joint enrichment analysis and identification of multiple causal variants
fgwashttps://github.com/joepickrell/fgwashttps://pubmed.ncbi.nlm.nih.gov/24702953/Fine-mappingC/C++Integrates functional genomic information into a GWAS
echocolatoRhttps://github.com/RajLabMSSM/echolocatoRhttps://pubmed.ncbi.nlm.nih.gov/34529038/Fine-mappingRIntegrates a diverse suite of statistical and functional fine-mapping tools to identify, test enrichment in, and visualize high-confidence causal consensus variants in any phenotype.
RiVIERA-betahttps://github.com/yueli-compbio/RiVIERA-betahttps://pubmed.ncbi.nlm.nih.gov/27407109/Fine-mappingRBayesian fine-mapping using Epigenomic Reference Annotation
BEATRICEhttps://github.com/sayangsep/Beatrice-Finemappinghttps://pubmed.ncbi.nlm.nih.gov/36993396/Fine-mappingPythonCombines a hierarchical Bayesian model with a deep learning-based inference procedure
XMAPhttps://github.com/YangLabHKUST/XMAPhttps://pubmed.ncbi.nlm.nih.gov/37898663Fine-mappingRA variational EM method for cross-population fine-mapping by leveraging genetic diversity and accounting for confounding bias
FINMOMhttps://vkarhune.github.io/finimom/https://pubmed.ncbi.nlm.nih.gov/37348543Fine-mappingRA Bayesian method that allows for multiple causal variants using product inverse-moment prior which is a natural prior distribution to model non-null effects in finite samples
CARMAhttps://github.com/Iuliana-Ionita-Laza/CARMAhttps://pubmed.ncbi.nlm.nih.gov/37169873Fine-mappingRBayesian model that allows flexible specification of the prior distribution, joint modeling of summary statistics and functional annotations, and accounting for discrepancies between summary statistics and external LD
AHIUThttps://figshare.com/articles/dataset/AHIUT/6615470https://pubmed.ncbi.nlm.nih.gov/29959179Fine-mappingRAn intersection-union test based on a joint/conditional regression model with all the SNPs in a locus to infer AH
BVS-PICAhttps://onlinelibrary.wiley.com/action/downloadSupplement?doi=10.1111%2Fbiom.12620&file=biom12620-sup-0001-SuppData.ziphttps://pubmed.ncbi.nlm.nih.gov/27858978Fine-mappingRBayesian variable selection for classifying genomic class level associations
RHOGEhttps://github.com/bogdanlab/RHOGEhttps://pubmed.ncbi.nlm.nih.gov/28238358/Genetic correlationREstimates the genetic correlation between two complex traits as a function of predicted gene expression effect
LDSChttps://github.com/bulik/ldsc/https://pubmed.ncbi.nlm.nih.gov/25642630Genetic correlationPythonDistinguishes polygenicity from bias by examining the relationship between test statistics and LD score. Used allso for estimating heritability and genetic correlation
HDLhttps://github.com/zhenin/HDLhttps://pubmed.ncbi.nlm.nih.gov/32601477/Genetic correlationRA likelihood-based method for estimating genetic correlation. Compared to LD Score regression (LDSC), It reduces the variance of a genetic correlation estimate by about 60%
PCGC-shttps://github.com/omerwe/PCGCshttps://pubmed.ncbi.nlm.nih.gov/29979983Genetic correlationPythonIt is an adaptation of stratified LD score regression (S-LDSC) for case-control studies. It can estimate genetic heritability, genetic correlation and functional enrichment.
PhenoSpDhttps://github.com/MRCIEU/PhenoSpDhttps://pubmed.ncbi.nlm.nih.gov/30165448Genetic correlationRUses LDSC to estimate phenotypic correlations and then performs correction of multiple testing using the spectral decomposition of matrices
GNOVAhttps://github.com/xtonyjiang/GNOVAhttps://pubmed.ncbi.nlm.nih.gov/29220677Genetic correlationPythonA method that calculates annotation-stratified covariance between arbitrary number of traits and enables researchers to dissect both the shared and distinct genetic architecture across traits
LPMhttps://github.com/mingjingsi/LPMhttps://pubmed.ncbi.nlm.nih.gov/31860024Genetic correlationRA latent probit model that can integrate functional annotations. It is scalable to hundreds of annotations and phenotypes
Popcornhttps://github.com/brielin/Popcornhttps://pubmed.ncbi.nlm.nih.gov/27321947/Genetic correlationPythonMethod for estimating the transethnic genetic correlation: the correlation of causal-variant effect sizes at SNPs common in populations
LOGOdetecthttps://github.com/ghm17/LOGODetecthttps://pubmed.ncbi.nlm.nih.gov/33795679/Genetic correlationRA tool to identify small segments that harbor local genetic correlation between two traits
cc-GWAShttps://github.com/wouterpeyrot/CCGWAShttps://pubmed.ncbi.nlm.nih.gov/33686288Genetic correlationRTool for case-case association testing of two different disorders
DONUTShttps://github.com/qlu-lab/DONUTShttps://pubmed.ncbi.nlm.nih.gov/34131076Genetic correlationRA statistical framework that can estimate direct and indirect genetic effects at the SNP level and calculate genetic correlation between traits
SUPERGNOVAhttps://github.com/qlu-lab/SUPERGNOVAhttps://pubmed.ncbi.nlm.nih.gov/34493297Genetic correlationPythonExtension of GNOVA to identify global and local genetic correlations that could provide new insights into the shared genetic basis of many phenotypes
GECKOhttps://github.com/borangao/GECKOhttps://pubmed.ncbi.nlm.nih.gov/33395406Genetic correlationRMethod  based on composite likelihood for estimating genetic and environmental covariances
LAVAhttps://ctg.cncr.nl/software/lavahttps://pubmed.ncbi.nlm.nih.gov/35288712/Genetic correlationRAn integrated framework for local genetic correlation analysis that can also evaluate local heritabilities and analyze conditional genetic relations between several phenotypes using partial correlation and multiple regression
JaSPUhttps://github.com/kaskarn/JaSPUhttps://pubmed.ncbi.nlm.nih.gov/33949650Pleiotropy (overlapped)JuliaEvaluates the effect of SNPs across k traits using z-scores from previous regression analyses. It performs simulations to produce p-values, using the empirical multivariate-normal distribution of null z-scores.
HIPOhttps://github.com/gqi/hipohttps://pubmed.ncbi.nlm.nih.gov/30289880/Pleiotropy (overlapped)RPerforms heritability informed power optimization for conducting multi-trait association analysis
MTARhttps://github.com/baolinwu/MTARhttps://pubmed.ncbi.nlm.nih.gov/30476000Pleiotropy (overlapped)RUses principal component (PC)-based association test which has optimal power when the underlying multi-trait signal can be captured by the first PC. Performs an adaptive test by optimally weighting the PC-based test and the omnibus chi-square test to achieve robust performance
PAThttps://github.com/koditaraszka/pathttps://pubmed.ncbi.nlm.nih.gov/36342933Pleiotropy (overlapped)PythonThe pleiotropic association test (PAT) is used for joint analysis of multiple traits. Uses the decomposition of phenotypic covariation into genetic and environmental components to create a likelihood ratio test statistic for each genetic variant.
TATEShttps://ctg.cncr.nl/software/https://pubmed.ncbi.nlm.nih.gov/23359524Pleiotropy (overlapped)FORTRANTrait-based Association Test that uses Extended Simes procedure combines the p-values obtained in standard univariate GWAS to acquire one trait-based p-value, while correcting for correlations between components
CONFIThttps://github.com/lgai/CONFIThttps://pubmed.ncbi.nlm.nih.gov/29949991Pleiotropy (overlapped)PythonThe method estimates the degree of shared effects between traits from the data. The test statistic is a sum of the relative likelihoods for each alternate configuration.
cFDRhttps://github.com/jamesliley/cFDR-common-controlshttps://pubmed.ncbi.nlm.nih.gov/25658688Pleiotropy (overlapped)RCalculates an upper bound on the expected false discovery rate (FDR) across a set of SNPs whose p-values for two diseases are both less than two disease-specific threshold
USAThttps://github.com/RayDebashree/USAThttps://pubmed.ncbi.nlm.nih.gov/26638693Pleiotropy (overlapped)RUses a data-adaptive weighted score-based test statistic for testing association of multiple continuous phenotypes with a single SNP
bmasshttps://github.com/mturchin20/bmasshttps://pubmed.ncbi.nlm.nih.gov/31596850Pleiotropy (overlapped)RBayesian multivariate analysis of GWAS data using univariate association summary statistics
ACAhttps://sites.bu.edu/fhspl/publications/approximate-conditional-analysis/https://pubmed.ncbi.nlm.nih.gov/33510268Pleiotropy (overlapped)RRelies on an approximate conditional phenotype analysis. The traits covariance may be estimated either from a subset of the phenotypic data; or from published studies.
TWThttps://github.com/bschilder/ThreeWayTesthttps://pubmed.ncbi.nlm.nih.gov/37027223Pleiotropy (overlapped)RUses the correlation coefficients between Wald statistics obtained from linear regression  with covariates. Then, a test is applied by integrating three-level information including the intrinsic genetic structure, pleiotropy, and the potential information combinations
EBMMThttps://github.com/Vivian-Liu-Wei64/EBMMThttps://pubmed.ncbi.nlm.nih.gov/35192735Pleiotropy (overlapped)RUses the eigen higher criticism and the eigen Berk-Jones testing procedures to test the association between SNPs and multiple correlated traits. Then uses the aggregated Cauchy association test .
Pleihttps://github.com/yangq001/Pleihttps://pubmed.ncbi.nlm.nih.gov/28971959Pleiotropy (overlapped)RA procedure that can be applied for both marginal analysis and conditional analysis. Uses the union-intersection testing methods, but in addition to the likelihood ratio test, it also applies generalized estimating equations under the working independence model
p_ACThttp://csg.sph.umich.edu/boehnke/p_act.phphttps://pubmed.ncbi.nlm.nih.gov/17966093/Pleiotropy (overlapped)RA method of computing P values adjusted for correlated tests that attains the accuracy of permutation or simulation-based tests in much less computation time
SHAHERhttps://github.com/Sodbo/shared_heredityhttps://pubmed.ncbi.nlm.nih.gov/36292579Pleiotropy (overlapped)RIt is based on the construction of a linear combination of traits by maximizing the proportion of its genetic variance explained by the shared genetic factors.
MTAGhttps://github.com/omeed-maghzian/mtaghttps://pubmed.ncbi.nlm.nih.gov/29292387Pleiotropy (overlapped)PythonA method for joint analysis of GWAS of different traits, using a weigthed sum and LDSC
PLEIOhttps://github.com/cuelee/pleiohttps://pubmed.ncbi.nlm.nih.gov/33352115Pleiotropy (overlapped)PythonA framework to map and interpret pleiotropic loci in a joint analysis of multiple diseases and complex traits. It maximizes power by systematically accounting for genetic correlations and heritabilities of the traits using LDSC.
MSKAThttps://github.com/baolinwu/MSKAThttps://pubmed.ncbi.nlm.nih.gov/30239606Pleiotropy (overlapped)RVarious types of multi-trait SNP-set association tests (variance component test, burden test and adaptive test), and efficient numerical calculation of P-values
multiSKAThttps://github.com/diptavo/MultiSKAThttps://pubmed.ncbi.nlm.nih.gov/30298564/Pleiotropy (overlapped)RA general framework for testing pleiotropic effects of rare variants on multiple continuous phenotypes using multivariate kernel regression. Many existing tests are equivalent to specific choices of parameters within this framework
MTARhttps://cran.r-project.org/web/packages/MTARhttps://pubmed.ncbi.nlm.nih.gov/32503972Pleiotropy (overlapped)RJoint analysis of association summary statistics between multiple rare variants and different traits. Leverages the genome-wide genetic correlation to inform the degree of gene-level effect heterogeneity across traits.
MGAShttp://pmglab.top/kgg/https://pubmed.ncbi.nlm.nih.gov/25431328/Pleiotropy (overlapped)JavaA multivariate gene-based association test by extended Simes procedure (MGAS), that allows gene-based testing of multivariate phenotypes
iMAPhttps://github.com/biostatpzeng/iMAPhttps://pubmed.ncbi.nlm.nih.gov/29635306Pleiotropy (overlapped)RPerforms integrative mapping of pleiotropic association and functional annotations using penalized Gaussian mixture models. Uses a multinomial logistic regression model
graphGPA2https://dongjunchung.github.io/GGPA2/https://pubmed.ncbi.nlm.nih.gov/37501720Pleiotropy (overlapped)RBayesian graphical model which allows to integrate functional annotations with GWAS datasets for multiple phenotypes within a unified framework
MTAFShttps://github.com/Qiaolan/MTAFShttps://pubmed.ncbi.nlm.nih.gov/37237036Pleiotropy (overlapped)RAn efficient and robust adaptive method for multi-trait analysis of GWAS.
PDRhttps://github.com/jballard28/PDRhttps://pubmed.ncbi.nlm.nih.gov/35477001Pleiotropy (overlapped)MatlabPleiotropic decomposition regression using method of moments to identify shared components and their underlying genetic variants
PLACOhttps://github.com/RayDebashree/PLACOhttps://pubmed.ncbi.nlm.nih.gov/33290408Pleiotropy (overlapped)RImplements a variant-level formal statistical test of pleiotropy of two traits inspired from mediation analysis.
HOPShttps://github.com/rondolab/HOPShttps://pubmed.ncbi.nlm.nih.gov/31653226Pleiotropy (overlapped)RAllows to compute the horizontal pleiotropy score by removing correlations between traits caused by vertical pleiotropy and normalizing effect sizes across all traits
MAIUPhttps://github.com/biostatpzeng/MAIUPhttps://pubmed.ncbi.nlm.nih.gov/34571531Pleiotropy (overlapped)RTest constructed based on the traditional intersection–union test with two sets of independent P-values as input and follows a novel idea that was originally proposed under the high-dimensional mediation analysis framework
GPAhttps://github.com/dongjunchung/GPAhttps://pubmed.ncbi.nlm.nih.gov/2539367Pleiotropy (independent)RUses the EM algorithm to integrate pleiotropy and functional annotation (eQTL etc)
EPShttps://github.com/gordonliu810822/EPShttps://pubmed.ncbi.nlm.nih.gov/27153687/Pleiotropy (independent)MatlabAn Empirical Bayes approach to integrating Pleiotropy and Tissue-Specific information (EPS) for prioritizing risk genes
PolarMorphismhttps://github.com/UMCUGenetics/PolarMorphismhttps://pubmed.ncbi.nlm.nih.gov/35758773Pleiotropy (independent)RThe method is based on a transform from Cartesian to polar coordinates. Analyzes multiple related phenotypes and reports (per SNP) the degree of ‘sharedness’ across them, its overall effect size, as well as p-values
FactorGohttps://github.com/mancusolab/FactorGohttps://pubmed.ncbi.nlm.nih.gov/37879338Pleiotropy (independent)PythonA scalable variational factor analysis model used to identify and characterize pleiotropic components. Works well in capturing latent pleiotropic factors across phenotypes while at the same time being computationally efficient
gwas-pwhttps://github.com/joepickrell/gwas-pwhttps://pubmed.ncbi.nlm.nih.gov/27182965/Pleiotropy (independent)RA tool for jointly analysing two GWAS to identify loci that influence both traits. Instead of using two P-value thresholds to identify variants that influence both traits, the algorithm learns reasonable thresholds from the data
UNITYhttps://github.com/bogdanlab/UNITYhttps://pubmed.ncbi.nlm.nih.gov/29949958Pleiotropy (independent)PythonA Bayesian framework for estimating the proportion of causal variants shared between a pair of complex traits
sumDAGhttps://github.com/chunlinli/sumdaghttps://pubmed.ncbi.nlm.nih.gov/38045347Pleiotropy (independent)RConstructs a phenotype network by assuming a Gaussian linear structure model embedding a directed acyclic graph
GCPBayeshttps://github.com/CESP-ExpHer/GCPBayes-Pipelinehttps://pubmed.ncbi.nlm.nih.gov/37416786Pleiotropy (independent)RPipeline to perform cross-phenotype gene-set analysis between two traits using GCPBayes
combGWAShttps://github.com/LiangyingYin/CombGWAShttps://pubmed.ncbi.nlm.nih.gov/34050728Pleiotropy (independent)RA statistical framework to uncover susceptibility variants for comorbid disorders and calculate genetic correlations
JASShttps://gitlab.pasteur.fr/statistical-genetics/jasshttps://pubmed.ncbi.nlm.nih.gov/32002517Pleiotropy (independent)web/PythonIncorporates various joint tests such as the omnibus approach and weighted sum of Z-score tests while offering data cleaning and harmonization, fast derivation of joint statistics, and optimized data management process
IMRPhttps://github.com/XiaofengZhuCase/IMRPhttps://pubmed.ncbi.nlm.nih.gov/33226062MRRPerforms Iterative MR and Pleiotropy analysis to simultaneously search for horizontal pleiotropic variants and estimate causal effect
CShttps://github.com/xue-hr/Causal_Directionhttps://pubmed.ncbi.nlm.nih.gov/33137120MRRInfers causal direction between two traits in the presence of horizontal pleiotropy
MR2https://github.com/lb664/MR2/https://pubmed.ncbi.nlm.nih.gov/37419091MRRPerforms MR for multiple outcomes to identify exposures that cause more than one outcome or, conversely, exposures that exert their effect on distinct responses
MRmixhttps://github.com/gqi/MRMixhttps://pubmed.ncbi.nlm.nih.gov/31028273/MRRMR analysis using an underlying mixture model incorporating a fraction of the genetic instruments to have direct effect on the outcome (horizontal pleiotropy)
MR-PRESSOhttps://github.com/rondolab/MR-PRESSOhttps://pubmed.ncbi.nlm.nih.gov/29686387/MRRAllows for the evaluation of horizontal pleiotropy in multi-instrument MR
MV-MRhttps://mrcieu.github.io/software/mvmr-r-package/https://pubmed.ncbi.nlm.nih.gov/30535378/MRRPerforms multivariable MR analyses, including heterogeneity statistics for assessing instrument strength and validity
MR-BMAhttps://github.com/verena-zuber/demo_AMDhttps://pubmed.ncbi.nlm.nih.gov/31911605/MRRBayesian algorithm to perform risk factor selection in multivariable MR
MRhttps://cran.r-project.org/web/packages/MendelianRandomization/index.htmlhttps://pubmed.ncbi.nlm.nih.gov/31953392/MRRSeveral standard methods (simple and weighted median, IVW, and MR-Egger) for performing MR analyses with summary data
pIVWhttps://cran.r-project.org/web/packages/mr.pivw/index.htmlhttps://pubmed.ncbi.nlm.nih.gov/35942938MRRAn extension to IVW that accounts for weak instruments and balanced horizontal pleiotropy simultaneously
MR-APSShttps://github.com/YangLabHKUST/MR-APSShttps://pubmed.ncbi.nlm.nih.gov/35787050/MRRPerforms MR accounting for both pleiotropy and sample structure (which includes population stratification, cryptic relatedness, and sample overlap)
BWMRhttps://github.com/jiazhao97/BWMRhttps://pubmed.ncbi.nlm.nih.gov/31593215/MRRBayesian methods for MR
TwoSampleMRhttps://mrcieu.github.io/TwoSampleMR/https://pubmed.ncbi.nlm.nih.gov/29149188/MRRStandard methods for performing MR. It uses the IEU GWAS database and to the MR-Base web app
MRbasehttps://www.mrbase.orghttps://pubmed.ncbi.nlm.nih.gov/29846171/MRwebA database and analytical platform for Mendelian randomization. It is coupled to TwoSampleMR and to MRC IEU OpenGWAS database.
LCVhttps://github.com/lukejoconnor/LCVhttps://pubmed.ncbi.nlm.nih.gov/30374074/MRREstimates causal associations between traits avoiding confounding by genetic correlation. Uses LDSC
MRcMLhttps://github.com/xue-hr/MRcMLhttps://pubmed.ncbi.nlm.nih.gov/34214446MRRUses constrained maximum likelihood and model averaging, that is robust to invalid IVs with uncorrelated or correlated pleiotropic effects
MR-LDPhttps://github.com/QingCheng0218/MR.LDPhttps://pubmed.ncbi.nlm.nih.gov/33575584MRRProbabilistic model for MR in the presence of LD and to account for horizontal pleiotropy
MR-Corr2https://github.com/QingCheng0218/MR.Corr2https://pubmed.ncbi.nlm.nih.gov/34499127MRRBayesian approach that uses the orthogonal projection to reparameterize the bivariate normal distribution for effects of variants on exposure and horizontal pleiotropy
MR.CUEhttps://github.com/QingCheng0218/MR.CUEhttps://pubmed.ncbi.nlm.nih.gov/36310177MRREstimates causal effect while identifying IVs with correlated horizontal pleiotropy and accounting for estimation uncertainty
TS_LMMhttps://github.com/mingding-hsph/TS_LMMhttps://pubmed.ncbi.nlm.nih.gov/37162968MRRPerforms two-stage linear mixed model for MVMR that accounts for variance of summary statistics not only in outcome, but also in all of the risk factors
MRlaphttps://github.com/n-mounier/MRlap/https://pubmed.ncbi.nlm.nih.gov/37036286MRRSimultaneously considers weak instrument bias and winner’s curse while accounting for potential sample overlap and corrects IVW-MR
MRCIhttps://github.com/zpliu/MRCIhttps://pubmed.ncbi.nlm.nih.gov/36854672MRREstimates reciprocal causation between two phenotypes simultaneously using reference LD information
BiDirectCausalhttps://github.com/xue-hr/BiDirectCausalhttps://pubmed.ncbi.nlm.nih.gov/35576237MRRInfers possibly bi-directional causal effects between two traits
LHC-MRhttps://github.com/LizaDarrous/lhcMRhttps://pubmed.ncbi.nlm.nih.gov/34907193MRREstimates bi-directional causal effects, direct heritabilities, and confounder effects while accounting for sample overlap
MRBEEhttps://github.com/noahlorinczcomi/MRBEEhttps://pubmed.ncbi.nlm.nih.gov/37066391MRRMultivariable MR method capable of simultaneously removing measurement error bias and identifying horizontal pleiotropy
MVMR-cMLhttps://github.com/ZhaotongL/MVMR-cMLhttps://pubmed.ncbi.nlm.nih.gov/36948188MRRAn efficient and robust MVMR method based on constrained maximum likelihood (cML)
adOMICshttps://github.com/lshen/adomicshttps://pubmed.ncbi.nlm.nih.gov/36094096MRPythonUsed to investigate the causal effects of multiple omics biomarkers on an outcome. The method first tests the effect of each omics biomarker on the outcome separately using an MR method and then combines the p-values using various methods
OMRhttps://github.com/wanglu205/OMRhttps://pubmed.ncbi.nlm.nih.gov/34379090MRRMR method that uses all GWAS SNPs for causal inference. The method accommodates the commonly encountered horizontal pleiotropy effects and relies on a composite likelihood framework for scalable computation
JAM-MRhttps://github.com/pjnewcombe/R2BGLiMShttps://pubmed.ncbi.nlm.nih.gov/34155684MRRPerforms variable selection and causal effect estimation in MR as an extension of the JAM algorithm
hJAMhttps://cran.r-project.org/web/packages/hJAM/https://pubmed.ncbi.nlm.nih.gov/33404048MRRA two-stage hierarchical model that unifies the framework of MR and TWAS and can be applied to correlated instruments and multiple intermediates
MR.RAPShttps://github.com/qingyuanzhao/mr.rapshttps://pubmed.ncbi.nlm.nih.gov/31298269MRRA three-sample genome-wide design with many independent genetic instruments across the whole genome. The method is efficient with many weak genetic instruments and robust to balanced and/or sparse pleiotropy
MRPEAhttps://sourceforge.net/projects/mrpea/files/https://pubmed.ncbi.nlm.nih.gov/28334273MRRA pathway association MR analysis approach, which was capable of correcting the genetic confounding effects of environmental exposures, using data of environmental exposures
Sherlockhttp://sherlock.ucsf.eduhttps://pubmed.ncbi.nlm.nih.gov/23643380/ColocalizationwebIt uses a database of eQTL n different tissues to identify patterns in GWAS that match those for specific genes. information from both cis- and trans- eQTL SNPs
COLOChttps://github.com/chr1swallace/colochttps://pubmed.ncbi.nlm.nih.gov/34587156ColocalizationRAllows evidence for association at multiple causal variants to be evaluated simultaneously, whilst separating the statistical support for each variant conditional on the causal signal being considered.
eCAVIARhttps://github.com/fhormoz/caviarhttps://pubmed.ncbi.nlm.nih.gov/27866706ColocalizationC/C++Colocalization of GWAS and eQTL with a probabilistic method that accounts for more than one causal variant in any given locus
molochttps://github.com/clagiamba/molochttps://pubmed.ncbi.nlm.nih.gov/29579179/ColocalizationRMultiple-trait-coloc, a Bayesian statistical framework that integrates GWAS summary data with multiple molecular QTL data to identify regulatory effects at GWAS risk loci
POEMColochttps://github.com/AbbVie-ComputationalGenomics/POEMColochttps://pubmed.ncbi.nlm.nih.gov/34000989ColocalizationRAn approximation to the coloc method that can be applied when limited summary statistics are available
HyPrColochttps://github.com/cnfoley/hyprcolochttps://pubmed.ncbi.nlm.nih.gov/33536417ColocalizationRAn efficient deterministic Bayesian algorithm that can detect colocalization across vast numbers of traits simultaneously
SparkINFERNOhttps://bitbucket.org/wanglab-upenn/SparkINFERNOhttps://pubmed.ncbi.nlm.nih.gov/32330239ColocalizationPythonA scalable bioinformatics pipeline characterizing non-coding GWAS. It prioritizes causal variants underlying  association signals and reports relevant regulatory elements, tissue contexts and plausible target genes
ColocQuiaLhttps://github.com/bvoightlab/ColocQuiaLhttps://pubmed.ncbi.nlm.nih.gov/35894642/ColocalizationRPipeline that provides a framework to perform eQTL and sQTL colocalization analyses at scale across the genome with COLOC
MSGhttps://github.com/yingji15/MSG_publichttps://pubmed.ncbi.nlm.nih.gov/35771864ColocalizationRA multidimensional splicing gene approach
LocusFocushttps://locusfocus.research.sickkids.ca/https://pubmed.ncbi.nlm.nih.gov/33090994/ColocalizationwebA web-based tool that tests colocalization using the Simple Sum method to identify the most relevant genes and tissues for a particular locus in the presence of high LD and/or allelic heterogeneity
ShareProhttps://github.com/zhwm/SharePro_colochttps://www.biorxiv.org/content/10.1101/2023.07.24.550431v1ColocalizationPythonTakes an effect group-level approach to integrate LD modelling and colocalization assessment to account for multiple causal variants in colocalization analysis
pwCoCohttps://github.com/jwr-git/pwcocohttps://pubmed.ncbi.nlm.nih.gov/32895551/ColocalizationPythonA fast tool that integrates methods from GCTA-COJO and the coloc R package
ezQTLhttps://analysistools.cancer.gov/ezqtl/#/homehttps://pubmed.ncbi.nlm.nih.gov/35643189/Colocalizationweb/RPerforms data quality control for variants matched between different datasets, LD visualization, and two-trait colocalization analyses using two state-of-the-art methodologies (eCAVIAR and HyPrColoc)
PESCAhttps://github.com/huwenboshi/pescahttps://pubmed.ncbi.nlm.nih.gov/32442408ColocalizationC/C++Uses ancestry-matched estimates of LD to infer genome-wide proportions of population-specific and shared causal variants for a single trait in two populations. These estimates are then used as priors in an empirical Bayes framework to localize and test for enrichment of population-specific/shared causal variants in regions of interest
LLRhttps://github.com/gordonliu810822/LLRhttps://pubmed.ncbi.nlm.nih.gov/28961754ColocalizationMatlabA latent low-rank approach to colocalizing genetic risk variants in multiple GWAS and phenotypes
SS2https://github.com/FanWang0216/SimpleSum2Colocalizationhttps://pubmed.ncbi.nlm.nih.gov/35065708ColocalizationRIntegrates GWAS summary statistics with eQTL summary statistics across any number of gene-by-tissue pairs, is applicable when there are overlapping participants in the two studies
TScMLhttps://github.com/xue-hr/TScMLhttps://pubmed.ncbi.nlm.nih.gov/37808547TWASRA robust and efficient inferential method to account for both hidden confounding and some invalid IVs via two-stage constrained maximum likelihood, an extension of 2SLS
GSMRhttps://yanglab.westlake.edu.cn/software/gsmr/https://pubmed.ncbi.nlm.nih.gov/29335400/TWASRGeneralised Summary-data-based Mendelian Randomisation method that tests for a putative causal association between two phenotypes based on a multi-SNP model
SMRhttps://yanglab.westlake.edu.cn/software/smr/#Overviewhttps://pubmed.ncbi.nlm.nih.gov/27019110/TWASExecutableIntegrates GWAS with eQTL to identify genes whose expression levels are associated with a complex trait because of pleiotropy
PMRhttps://github.com/yuanzhongshang/PMRhttps://pubmed.ncbi.nlm.nih.gov/32737316/TWASREfficient inference of 2SMR in TWAS. It can account for correlated instruments and horizontal pleiotropy, and provides accurate estimates of causal effect and horizontal pleiotropy effect. It can be applied in single traits as well as multiple correlated outcome traits
TWAS/FUSIONhttp://gusevlab.org/projects/fusion/https://pubmed.ncbi.nlm.nih.gov/26854917/TWASRintegrates gene expression measurements with summary association statistics from large-scale genome-wide association studies (GWAS) to identify genes whose cis-regulated expression is associated to complex traits
TWAS-aSPUwww.wuchong.org/TWAS.htmlhttps://pubmed.ncbi.nlm.nih.gov/28893853TWASRImplements the so-called sum of powered score (SPU)  which includes sum and SSU as special cases
S-PrediXcanhttps://github.com/hakyimlab/MetaXcanhttps://pubmed.ncbi.nlm.nih.gov/29739930TWASPythonA method that seeks to capture the effects of gene expression variation on human phenotypes
iFunMedhttps://github.com/mcrojo/iFunMedhttps://pubmed.ncbi.nlm.nih.gov/31328826TWASRA mediation model that utilizes functional annotation and statistics from GWAS and eQTL. It enables identification of SNPs that are associated with phenotypical changes through direct phenotype-genotype and/or indirect phenotype-genotype through gene expression effect
UTMOSThttps://github.com/Joker-Jerome/UTMOSThttps://pubmed.ncbi.nlm.nih.gov/30804563/TWASPythonFramework that combines multiple single-tissue associations into a powerful metric to quantify the overall gene-trait association
TIGARhttps://github.com/yanglab-emory/TIGARhttps://pubmed.ncbi.nlm.nih.gov/31230719/TWASPythonTranscriptome-Wide Association Studies (TWAS) by training gene expression imputation models by nonparametric Bayesian Dirichlet Process Regression (DPR) and Elastic-Net (PrediXcan) methods with reference transcriptomic panels
fQTLhttps://github.com/ypark/fqtlhttps://www.biorxiv.org/content/10.1101/107623v1TWASRA multi-tissue, multivariate model for mapping expression quantitative trait loci and predicting gene expression
CoMM-S*https://github.com/gordonliu810822/CoMMhttps://pubmed.ncbi.nlm.nih.gov/34616426TWASRUses variational Bayesian EM algorithm and a likelihood ratio test to integrate GWAS data with eQTL to assess expression-trait association
TisCoMMhttps://github.com/XingjieShi/TisCoMMhttps://pubmed.ncbi.nlm.nih.gov/32978944TWASRLeverages the co-regulation of genetic variations across different tissues explicitly via a probabilistic model. Apart from prioritizing gene-trait associations, it also detects the tissue-specific role of candidate target genes in complex traits
Primohttps://github.com/kjgleason/Primohttps://pubmed.ncbi.nlm.nih.gov/32912334TWASRA method for integrative analysis of multiple sets of xQTL data (eQTL, pQTL etc) from different cellular conditions or studies. It examines association patterns of SNPs to complex and omics traits accounting for LD,  heterogeneity and sample correlations
OPERAhttps://github.com/wuyangf7/OPERAhttps://pubmed.ncbi.nlm.nih.gov/37601976TWASC/C++A method that jointly analyzes GWAS and multi-omics xQTL data to enhance the identification of molecular phenotypes associated with complex traits through shared causal variants
FOCUShttps://github.com/mancusolab/ma-focushttps://pubmed.ncbi.nlm.nih.gov/30926970/TWASPythonSoftware to fine-map transcriptome-wide association study statistics at genomic risk regions. The software outputs a credible set of genes to explain observed genomic risk
BAGEAhttps://github.com/dlampart/bageahttps://pubmed.ncbi.nlm.nih.gov/32516306TWASRA variational Bayes framework to model cis-eQTLs using directed and undirected genomic annotations
SUMMIThttps://github.com/ChongWuLab/SUMMIThttps://pubmed.ncbi.nlm.nih.gov/36284135TWASRImproves the expression prediction model accuracy and the power of TWAS by using a large eQTL summary-level dataset, penalized regression and Cauchy Combination Test
sCCAhttps://github.com/fenghelian/sCCA-ACAT_TWAShttps://pubmed.ncbi.nlm.nih.gov/33831007TWASRIntegrates multiple tissues in the TWAS using sparse canonical correlation analysis and an aggregate Cauchy association test
HMAThttps://github.com/biostatpzeng/HMAThttps://pubmed.ncbi.nlm.nih.gov/33615361TWASRMethod which aggregates TWAS association evidence obtained across multiple gene expression prediction models by leveraging the harmonic mean P-value combination
BGW-TWAShttps://github.com/yanglab-emory/BGW-TWAShttps://pubmed.ncbi.nlm.nih.gov/32961112TWASC/C++Bayesian TWAS method that leverages both cis- and trans-eQTL based on Bayesian variable selection regression
ARCHIEhttps://github.com/diptavo/ARCHIEhttps://pubmed.ncbi.nlm.nih.gov/35882830TWASRA summary statistic based sparse canonical correlation analysis method to identify sets of gene-expressions trans-regulated by sets of known trait-related genetic variants
JEPEGMIX2‐Phttps://github.com/Chatzinakos/JEPEGMIX2-Phttps://pubmed.ncbi.nlm.nih.gov/32954640TWASExecutableA fast TWAS pathway method that has uses a large and diverse reference panel and is applicable to ethnically mixed-cohorts
PTWAShttps://github.com/xqwen/ptwashttps://pubmed.ncbi.nlm.nih.gov/32912253TWASExecutableProbabilistic TWAS  method which applies principles from instrumental variables analysis and takes advantage of probabilistic eQTL annotations
sMiSThttps://research.fredhutch.org/hsu/en/software.htmlhttps://pubmed.ncbi.nlm.nih.gov/32833970TWASRMixed effects score test that tests for the total effect of both the effect of the mediator by imputing genetically predicted gene expression
MRLocushttps://thelovelab.github.io/mrlocus/https://pubmed.ncbi.nlm.nih.gov/33872308TWASRA method for Bayesian estimation of the gene-to-trait effect from eQTL and GWAS data for loci with evidence of allelic heterogeneity. Makes use of a colocalization step applied to each nearly-LD-independent eQTL, followed by an MR analysis step across eQTLs
TWMRhttps://github.com/eleporcu/TWMRhttps://pubmed.ncbi.nlm.nih.gov/31341166TWASRMR integrating GWAS and eQTL data reveals genetic determinants of complex and clinical traits
Mr.MtRobinhttps://github.com/kjgleason/Mr.MtRobinhttps://pubmed.ncbi.nlm.nih.gov/33834509TWASRMulti-tissue transcriptome-wide MR method that uses multi-tissue eQTL analyses as input and a reverse regression random slope mixed model to infer whether a gene is associated with a complex trait
S-MultiXcanhttps://github.com/hakyimlab/MetaXcan?tab=readme-ov-filehttps://pubmed.ncbi.nlm.nih.gov/29739930/TWASPythonIt integrates summary statistics from multiple single-tissue transcriptome-wide association studies (TWAS) to identify genes whose expression is associated with complex traits.
GWAS Datasets (Kontou & Bagos, BioData Mining 2024)