GenABEL, or *ABEL, is an umbrella name for a number of software packages aiming to facilitate statistical analyses of polymorphic genome data. It is a rich program set which now allows very flexible genome-wide association (GWA) analysis (GenABEL, ProbABEL, MixABEL, OmicABEL), meta-analysis (MetABEL), parallelization of GWA analyses (ParallABEL), management of very large files (DatABEL), and facilitates evaluation of prediction (PredictABEL).

Most likely, you only need one of the packages for your specific task. Figure out which one you need, install, and use! If you have questions, please refer to the "Support" section of this web-site.

The code for latest development versions of the packages is available from the GenABEL Project pages on R-forge or GitHub. There you can also subscribe to the mailing list with announcements of new or updated packages. The archives of the Announce mailing list can be found there as well.

For stable releases, use CRAN version for R packages or links provided at this website


Genome-wide association analysis for quantitative, binary and time-till-event traits


An R library for detecting compound heterozygote alleles in genome-wide association analysis


Meta-analysis of genome-wide SNP association results Genome-wide association analysis for quantitative, binary and time-till-event traits


More mixed models for genome-wide association analysis; experimenting with GSL, multiple input formats, iterator, parallelization through threads.


Rapid mixed-model based GWAS efficiently handling large datasets, and both single trait and multiple trait ("omics") analyses


Genome-wide association analysis of imputed data


Assess the performance of risk models for binary outcomes


Tool for Genome-Wide Association Studies for multiple observations on related individuals


Genome-wide variance heterogeneity analysis as a tool for identification of potentially interacting SNPs.


File-based access to large matrices stored on HDD in binary format


Generalized parallelization of Genome-Wide Association Studies