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A free, open-source whole genome association analysis toolset, designed to perform a range of basic, large-scale analyses in a computationally efficient manner. The focus of PLINK is purely on analysis of genotype/phenotype data, so there is no support for steps prior to this (e.g. study design and planning, generating genotype or CNV calls from raw data). Through integration with gPLINK and Haploview, there is some support for the subsequent visualization, annotation and storage of results.


A simple, ready-to-use software which has been designed to analyze genetic-epidemiology studies of association using SNPs. Main capabilities include descriptive analysis, test for Hardy-Weinberg equilibrium and linkage disequilibrium. Analysis of association is based on linear or logistic regression according to the response variable (quantitative or binary disease status, respectively). Analysis of single SNPs: multiple inheritance models (co-dominant, dominant, recessive, over-dominant and log-additive), and analysis of interactions (gene-gene or gene-environment). Analysis of multiple SNPs: haplotype frequency estimation, analysis of association of haplotypes with the response, including analysis of interactions.

SCOPA / Software for COrrelated Phenotype Analysis

A reverse regression model for multiple correlated phenotypes. SCOPA has a number of key advantages: (i) the software can accommodate directly typed and imputed single-nucleotide polymorphism (SNPs), appropriately accounting for uncertainty in the imputation in the downstream association analysis; (ii) dissection of multivariate association signals is achieved through model selection to determine which phenotypes are jointly associated with the SNP; (iii) SCOPA association summary statistics can also be aggregated across genome-wide association study (GWAS) through fixed-effects meta-analysis, implemented in META-SCOPA, enabling application of reverse regression in large-scale international consortia efforts where individual-level genotype are phenotype data cannot be shared between studies.


A high-dimensional variable selection method for survival analysis by improving the existing variable selection methods in several aspects. First, we have developed a computationally feasible variable selection approach for high-dimensional survival analysis. Second, we have designed a random sampling scheme to improve the control of the false discovery rate. Finally, the proposed framework is flexible to accommodate complex data structures. Comparisons between the proposed method and the commonly used univariate and Lasso approaches for variable selection reveal that the proposed method yields fewer false discoveries.

SWGDT / Sliding Window-based Genotype Dependence Testing

Tests genotype dependence for genome-wide scan of susceptibility gene. SWGDT utilizes known causal gene information to proceed. It provides an alternative approach for integrating known disease gene information into the susceptibility gene mapping of human complex diseases. This tool can be applied to genome wide association study (GWAS) data for genome-wide susceptibility gene scan. It aims to assist users to identify novel disease genes that are likely to be missed by GWAS.


A cross-platform integrated graphical analysis tool for conducting epidemiological, single SNP and haplotype-based association analysis. SimHap GUI features a workflow interface that guides the user through each logical step of the analysis process, making it accessible to both novice and advanced users. This tool provides a seamless interface to the SimHap R package, while providing enhanced functionality such as sophisticated data checking, automated data conversion, and real-time estimations of haplotype simulation progress.


Contains classes and methods to help the analysis of whole genome association studies. SNPassoc utilizes S4 classes and extends haplo.stats R package to facilitate haplotype analyses. The package is useful to carry out most common analysis when performing whole genome association studies. These analyses include descriptive statistics and exploratory analysis of missing values, calculation of Hardy-Weinberg equilibrium, analysis of association based on generalized linear models (either for quantitative or binary traits), and analysis of multiple SNPs (haplotype and epistasis analysis). Permutation test and related tests (sum statistic and truncated product) are also implemented.


forum (1)
Calculates the power to detect single nucleotide polymorphism (SNP) association with a time to event outcome over a range of study design scenarios. SurvivalGWAS_Power enables analyses under a Cox proportional hazards model or Weibull regression model, and can account for treatment and SNP-treatment interaction effects. Simulated data sets can also be generated by SurvivalGWAS_Power to enable analyses with methods that are not currently supported by the power calculator, thereby increasing the flexibility of the software. It also addresses the need for flexible and user-friendly software for power calculations for genetic association studies of time to event outcomes, with particular design features of relevance in pharmacogenetics.


Performs rapid mixed-model based genome-wide association analysis. OmicABEL implements algorithms that address the problems of single- and multi-trait Mixed-Model based genome-wide association studies (GWAS). It includes CLAK-Chol, that can be useful for investigation of complex traits in very large samples and CLAK-Eig. It can serve for the investigation of genetic control of different omics. It also can be used for the identification of trans-expression quantitative trait loci (eQTLs).

covmodfdr / covariate-modulated local false discovery rate

A Bayesian approach to incorporate a set of important covariates into the fdr under a heteroscedastic model, where the probability of non-null status and the distribution of the test statistic under the non-null hypothesis are both modulated by covariates. The primary advantage of our methodology over traditional fdr methods is that two SNPs with the same z score can have different values of cmfdr if one is in a more enriched category than the other. Hence, by using SNP annotations to modulate fdr, more SNPs can be discovered for a given level of fdr control. In other words, methods such as cmfdr that break the exchangeability assumption are potentially more powerful than traditional fdr methods that assume exchangeability.


A hybrid approach that includes the principal components (PCs) of the genotype matrix as fixed effects in FaST-LMM Select. PC-Select leverages the advantages of the FaST-LMM Select framework while correcting for population stratification. The two main steps of FaST-LMM Select are ranking SNPs by linear regression P-values to form the genetic relationship matrix (GRM) with the top-ranked SNPs and then calculating association statistics in a mixed-model framework, using this GRM. We used the top five PCs as fixed effects in both of these steps. As a result, PC-Select yields noninflated test statistics in the presence of population stratification and maintains high power to detect causal SNPs.

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).

CGEN / Clinical GENetics

Provides a module for logistic regression analyses of single-nucleotide polymorphism (SNP) data. CGEN is a Bioconductor package for analysis of case-control studies in genetic epidemiology. It is designed to give the users flexibility of using a number of different methods for analysis of SNP-environment or SNP-SNP interactions. It also implements a number of different methods that can incorporate such independence constraints into analysis of interactions in the setting of both unmatched and matched case-control studies.

Puma / Penalized Unified Multiple-locus Association

Comprises a family of statistical methods designed to identify weak associations in genome-wide association studies that are not detectable by conventional analytical methods. Puma uses a regularized multiple regression in a penalized maximum likelihood framework using a generalized linear model in order to simultaneously consider tens to hundreds of thousands of genetic markers in a single statistical model. These methods are able to consider both case/control and continuous phenotypes and are optimized to efficiently handle very large datasets.

Bmagwa / Bayesian Model Averaging in Genome-wide Association Studies

Implements a method for computing posterior association probabilities of single-nucleotide polymorphisms (SNP) (and other quantities) in genome-wide association studies (GWAS) using Bayesian variable selection and model averaging. Bmagwa considers simultaneously all available variants for inclusion as predictors in a linear genotype-phenotype mapping and averages over the uncertainty in the variable selection. This leads to naturally interpretable summary quantities on the significances of the variants and their contribution to the genetic basis of the studied trait.

ICSNPathway / Identify candidate Causal SNPs and Pathways

Identifies candidate causal single nucleotide polymorphisms (SNPs) and their corresponding candidate causal pathways from genome-wide association study (GWAS). ICSNPathway is a web server that integrates linkage disequilibrium (LD) analysis, functional SNP annotation and pathway-based analysis (PBA). The software can contribute to improve GWAS data interpretation from variants to biological mechanisms to better guide future biological mechanism studies.

GENOVA / GENe OVerlap Analysis

Tests the hypothesis that variants nominally associated with a phenotype may be more likely to overlap genes than those not associated with a phenotype. For each clump, GENOVA determines whether it intersects a gene or not, and whether it is associated with the trait or not. It then uses logistic regression to determine the degree of overlap between gene-intersecting and trait-associated clumps, including as covariates potential confounders, namely the minor allele frequency for the SNPs in the clump, clump length and number of SNPs in the clump.