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SKAT / SNP-set (Sequence) Kernel Association Test

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A SNP-set (e.g., a gene or a region) level test for association between a set of rare (or common) variants and dichotomous or quantitative phenotypes. SKAT aggregates individual score test statistics of SNPs in a SNP set and efficiently computes SNP-set level p-values, e.g. a gene or a region level p-value, while adjusting for covariates, such as principal components to account for population stratification. SKAT also allows for power/sample size calculations for designing for sequence association studies.

DoEstRare / Density-oriented Estimation for Rare variant positions

Allows to detect both global enrichment in rare alleles and localized clusters of disease-risk rare variants (DRVs). The DoEstRare statistic consists in comparing simultaneously the mutation position densities, estimated by kernel method, and the overall average allele frequencies between cases and controls. A weight system in the computation of average allele frequencies is incorporated to better discriminate neutral from causal variants (deleterious or protective).

RVTESTS / Rare Variant TESTS

A flexible software package for genetic association studies. RVTESTS is designed to support unrelated individual or related (family-based) individuals. Both quantitative trait and binary trait are supported. It includes a variety of association tests (e.g. single variant score test, burden test, variable threshold test, SKAT test, fast linear mixed model score test). RVTESTS also provides useful companion features for annotating sequence variants, integrating bioinformatics databases, performing data quality control and sample selection.

FREGAT / Family REGional Association Tests

An R package that can handle family and population samples and implements a wide range of region-based association methods including burden tests, functional linear models, and kernel machine-based regression. FREGAT can be used in genome/exome-wide region-based association studies of quantitative traits and candidate gene analysis. FREGAT offers many useful options to empower its users and increase the effectiveness and applicability of region-based association analysis.


A package to automate the process of binning rare variants into groups for statistical association analysis using a biological knowledge-driven framework. BioBin collapses variants into biological features such as genes, pathways, evolutionary conserved regions, protein families, regulatory regions, and others based on user-designated parameters. BioBin provides the infrastructure to create complex and interesting hypotheses in an automated fashion thereby circumventing the necessity for advanced and time consuming scripting.


Provides autosomal rare variant tests for quantitative traits. RVPedigree is a complete framework for analysis of association between quantitative traits and region based genetic variation in datasets containing related and/ or unrelated individuals. These methods allow the genetic variants to either increase or decrease the phenotype, by assuming that the variant effects are random. This package also implements a computational speed tricks to allow for faster estimation of significance.


An interactive program that integrates generation of rare variant genotype/phenotype data and evaluation of association methods using a unified platform. Variant data are generated for gene regions using forward-time simulation that incorporates realistic population demographic and evolutionary scenarios. Phenotype data can be obtained for both case–control and quantitative traits. SimRare has a user-friendly interface that allows for easy entry of genetic and phenotypic parameters. Novel rare variant association methods implemented in R can also be imported into SimRare, to evaluate their performance and compare results, e.g. power and Type I error, with other currently available methods both numerically and graphically.

MARV / Multi-phenotype Analysis of Rare Variants

Provides analyses of all phenotype combinations within one run and calculates the Bayesian Information Criterion to facilitate model selection. MARV is based on a method that collapses rare variants within a genomic region and models the proportion of minor alleles in the rare variants on a linear combination of multiple phenotypes. It allows rapid identification of rare variant effects on multiple phenotypes, thus paving the way for novel discoveries and insights into biology of complex traits.


Provides statistical power analysis and sample size estimation for sequence-based association studies. SEQPower generates sequencing data using forward-time simulation while incorporating demographic and natural selection parameters. The tool aids epidemiologists in determining the best study design, sample size and statistical tests for sequence-based association studies. It provides biostatisticians with a platform to fairly compare rare variant (RV) association methods and to validate and assess novel association tests.


A computer program for the analysis and interpretation of genomics data with an emphasis on understanding the genetic basis of biomedical traits. In MAMBA, statistical methods for the analysis of multiple study designs including case-control, continuous trait, cross-disorder, and cross-phenotype analysis are implemented. Three approaches for ASE analysis are also available in MAMBA, using a Bayesian model comparison framework and computation done via a Gibbs sampler: Independent Tissue Model (ITM), Grouped Tissue Model (GTM) and the Hierarchical Grouped Tissue Model (GTM*).


A command-line program which implements score statistics for detecting disease associations with rare variants in sequencing studies. The mutation information is aggregated across multiple variant sites of a gene through a weighted linear combination and then related to disease phenotypes through appropriate regression models. The weights can be constant or dependent on allele frequencies and phenotypes. The association testing is based on score statistics. The allele-frequency threshold can be fixed or variable. Statistical significance can be assessed by using asymptotic normal approximation or resampling.

qMSAT / quality-weighted Multivariate Score Association Test

Allows powerful association tests between complex traits and multiple rare variants under the presence of sequencing errors. qMSAT is a procedure that can increase power over existing methods under moderate sample sizes and relatively low coverage. This tool utilizes a quality weighted multivariate regression model to incorporate sequencing qualities at each individual genotype. It is robust towards the inclusion of noncausal variants and variants having effects with different magnitudes and directions.

FFBSKAT / Fast Family-Based Sequence Kernel Association Test

An efficient tool for region/gene association analysis of quantitative traits in samples of related individuals. This software implements a score-based variance component test to assess the association of a given set of single nucleotide polymorphisms with a continuous phenotype. We compared the performance of our software with that of two existing software for family-based sequence kernel association testing, namely, ASKAT and famSKAT, using the Genetic Analysis Workshop 17 family sample. Results demonstrate that FFBSKAT is several times faster than other available programs. In addition, the calculations of the three-compared software were similarly accurate. The FFBSKAT package is fast, user-friendly, and provides an easy-to-use method to perform whole-exome kernel machine-based regression association analysis of quantitative traits in samples of related individuals.

CCRaVAT / Case-Control Rare Variant Analysis Tool

Allows large-scale analysis of low frequency and rare polymorphisms. CCRaVAT is an analysis tool for investigating low frequency and rare variant associations in genome-wide association study (GWAS) and resequencing data. This method analyzes case and control data and investigates significance using Pearson’s chi-squared and Fisher’s exact tests. It also allows users to generate empirical p-values by permuting case-control status a predefined number of times.

famSKAT / family-based SKAT

An extension of sequence kernel association test (SKAT) that can be applied to data with familial correlation. famSKAT has a different test statistic and null distribution compared to SKAT, but is equivalent to SKAT when there is no familial correlation. We demonstrate that famSKAT is a general and flexible variance component score test approach, which is equivalent to SKAT when the familial variance component is set to 0. It can be applied to quantitative traits with unknown or known heritability. Compared with famBT, famSKAT is advantageous in power when the proportion of causal SNPs in a genomic region is small, and when not all causal SNPs have the same direction of effects. As expected, famBT outperforms famSKAT when the proportion of causal SNPs is greater than or equal to 50% and all these SNPs have positive effects, but the performance of famSKAT in these scenarios is still satisfactory. In real data analysis, when we do not have sufficient a priori information about the proportion of causal SNPs or the directions of effects, famSKAT would be a better choice over famBT.

KATE / Kernel Association Test Extended

Analyses the association of rare and low-frequency variantsKATE is a method that uses the Allele Match (AM) kernel to design a continuous line. The software employs hierarchical clustering and kernel-based association tests to analyze the effects of low-frequency and rare variants on quantitative characteristics within a chromosomal region. It also groups individuals into separate clusters and tests the specific effects of the clusters via ANOVA.

ARIEL / Accumulation of Rare variants Integrated and Extended Locus-specific test

Analyses the effects of rare variants within a specific locus. ARIEL introduces an extension to account for uncertainty in the form of variant quality scores. It extends a regression framework collapsing method and has the underlying assumption that variants within the functional unit analysed are all protective or all risk-causing. The derivation of this method is based on the analysis of non-consensus variant quality scores.