Packages

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

GenABEL

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

CollapsABEL

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

MetABEL

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

MixABEL

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

OmicABEL

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

ProbABEL

Genome-wide association analysis of imputed data

PredictABEL

Assess the performance of risk models for binary outcomes

RepeatABEL

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

VariABEL

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

DatABEL

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

ParallABEL

Generalized parallelization of Genome-Wide Association Studies