stmgp: Rapid and Accurate Genetic Prediction Modeling for Genome-Wide Association or Whole-Genome Sequencing Study Data

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:	1.0.4
Depends:	MASS
Published:	2021-07-18
DOI:	10.32614/CRAN.package.stmgp
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

Documentation:

Reference manual:

stmgp.pdf

Downloads:

Package source:	stmgp_1.0.4.tar.gz
Windows binaries:	r-devel: stmgp_1.0.4.zip, r-release: stmgp_1.0.4.zip, r-oldrel: stmgp_1.0.4.zip
macOS binaries:	r-release (arm64): stmgp_1.0.4.tgz, r-oldrel (arm64): stmgp_1.0.4.tgz, r-release (x86_64): stmgp_1.0.4.tgz, r-oldrel (x86_64): stmgp_1.0.4.tgz
Old sources:	stmgp archive

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