stmgp-package | R Documentation |
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.
The DESCRIPTION file:
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Maintainer: Masao Ueki <uekimrsd@nifty.com>
Ueki M, Tamiya G, 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>
Ueki M, Fujii M, Tamiya G, for Alzheimer's Disease Neuroimaging Initiative and the Alzheimer's Disease Metabolomics Consortium (2019) Quick assessment for systematic test statistic inflation/deflation due to null model misspecifications in genome-wide environment interaction studies. PLoS ONE 14:e0219825. <https://doi.org/10.1371/journal.pone.0219825>
Ueki M, Tamiya G, for Alzheimer's Disease Neuroimaging Initiative (2021) mooth-threshold multivariate genetic prediction incorporating gene-nvironment interactions. G3 Genes|Genomes|Genetics 11:jkab278. <https://doi.org/10.1093/g3journal/jkab278>
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