| stmgp-package {stmgp} | R Documentation |
Rapid and Accurate Genetic Prediction Modeling for Genome-Wide Association or Whole-Genome Sequencing Study Data
Description
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.
Details
The DESCRIPTION file: Index of help topics:
stmgeplink Smooth-threshold multivariate genetic
prediction (incorporating gene-environment
interactions) for genome-wide association or
whole-genome sequencing data in PLINK format
stmgp Smooth-threshold multivariate genetic
prediction
stmgp-package Rapid and Accurate Genetic Prediction Modeling
for Genome-Wide Association or Whole-Genome
Sequencing Study Data
stmgplink Smooth-threshold multivariate genetic
prediction for genome-wide association or
whole-genome sequencing data in PLINK format
Author(s)
Maintainer: Masao Ueki <uekimrsd@nifty.com>
References
Ueki M, Tamiya G, and for Alzheimer's Disease Neuroimaging Initiative. (2016) Smooth-thresholdmultivariate genetic prediction with unbiased model selection. Genet Epidemiol 40:233-43. <https://doi.org/10.1002/gepi.21958>
Ueki M. (2009) A note on automatic variable selection using smooth-threshold estimating equations. Biometrika 96:1005-11. <https://doi.org/10.1093/biomet/asp060>