Rapidly build accurate genetic prediction models for genome-wide association or whole-genome sequencing study data by smooth-threshold multivariate genetic prediction (STMGP) method. Variable selection is performed using marginal association test p-values with an optimal p-value cutoff selected by Cp-type criterion. Quantitative and binary traits are modeled respectively via linear and logistic regression models. A function that works through PLINK software (Purcell et al. 2007 <doi:10.1086/519795>, Chang et al. 2015 <doi:10.1186/s13742-015-0047-8>) <https://www.cog-genomics.org/plink2> is provided. Covariates can be included in regression model.
| Version: | 1.0.3 |
| Depends: | MASS |
| Published: | 2020-01-10 |
| Author: | Masao Ueki |
| Maintainer: | Masao Ueki <uekimrsd at nifty.com> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| SystemRequirements: | PLINK must be installed |
| Materials: | README NEWS |
| CRAN checks: | stmgp results |
| Reference manual: | stmgp.pdf |
| Package source: | stmgp_1.0.3.tar.gz |
| Windows binaries: | r-devel: stmgp_1.0.3.zip, r-release: stmgp_1.0.3.zip, r-oldrel: stmgp_1.0.3.zip |
| macOS binaries: | r-release: stmgp_1.0.3.tgz, r-oldrel: stmgp_1.0.3.tgz |
| Old sources: | stmgp archive |
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