Functions to perform robust epistasis detection in genome-wide association studies, as described in Slim et al. (2018) <doi:10.1101/442749>. The implemented methods identify pairwise interactions between a particular target variant and the rest of the genotype, using a propensity score approach. The propensity score models the linkage disequilibrium between the target and the rest of the genotype. All methods are penalized regression approaches, which differently incorporate the propensity score to only recover the synergistic effects between the target and the genotype.
| Version: | 1.0.2 |
| Depends: | R (≥ 3.4.0) |
| Imports: | matrixStats, DescTools, glmnet, SNPknock, parallel |
| Suggests: | foreach, iterators, precrec, doParallel, bigmemory, biglasso, testthat, knitr, rmarkdown, kableExtra |
| Published: | 2019-09-08 |
| Author: | Lotfi Slim [aut, cre], Clément Chatelain [ctb], Chloé-Agathe Azencott [ctb], Jean-Philippe Vert [ctb] |
| Maintainer: | Lotfi Slim <lotfi.slim at mines-paristech.fr> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| Materials: | README NEWS |
| CRAN checks: | epiGWAS results |
| Reference manual: | epiGWAS.pdf |
| Vignettes: |
Robust epistasis detection with epiGWAS |
| Package source: | epiGWAS_1.0.2.tar.gz |
| Windows binaries: | r-devel: epiGWAS_1.0.2.zip, r-release: epiGWAS_1.0.2.zip, r-oldrel: epiGWAS_1.0.2.zip |
| macOS binaries: | r-release: epiGWAS_1.0.2.tgz, r-oldrel: epiGWAS_1.0.2.tgz |
| Old sources: | epiGWAS archive |
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