Lab Github: https://github.com/leelabsg
SAIGE: SAIGE is an R-package for GWAS and rare-variant test with adjusting for sample relatedness and case-control imbalance. It can analyze very large sample data (ex. ~400,000 samples in UKBiobank) and produce accurate p-values by using saddlepoint approximation.
For the UKBiobank analysis results, see Resources page
SKAT: SKAT is an R-package for rare variant association analysis. It can carry out burden tests, SKAT, SKAT-O, and combined tests of common and rare variants with adjusting for covariates and kinship. For binary traits, it can calculate p-values using resampling and asymptotic-based adjustment methods. It also has functions for sample size and power calculations.
MetaSKAT: MetaSKAT is an R package for gene-based meta-analysis across studies. It can carry out a meta-analysis of SKAT, SKAT-O, and burden tests with individual-level genotype data or gene-level summary statistics.
iECAT: iECAT is an R-package to test for single variant and gene/region-based associations using external control samples.
SPAtest: SPAtest is an R-package to perform score test for associations between genetic variants and binary traits using saddlepoint approximation. The methods implemented in the package (FastSPA) can accurately calculate p-values even when the case-control ratio is extremely unbalanced.
Download (CRAN): link
JointScoreTest: JointScoreTest is an R-package to perform a joint test of fixed and random effects in the Generalized linear mixed model framework.
Download Package Source: link
dSVA: dSVA is an R-package to identify hidden factors in high-dimensional biomedical data.
Download (CRAN): link
TransMeta & TransMetaRare: TransMeta is an R-package to compute single SNP p-values of trans-ethnic meta-analysis using a kernel-based random effect model. This is an early version, and we will keep updating it. We have recently extended it to gene-based rare-variant test (Transmeta-rare). The packages can be downloaded from the following github.
EigenCorr: EigenCorr is an R-package to compute p-values of principal components (PCs) based on EigenCorr1, EigenCorr2 and Tracy-Widom methods. You need PCs, outcome phenotypes and all eigenvalues to run EigenCorr.