MeTWAS: Train and predict gene's predictive model with mediators...

In bhattacharya-a-bt/MOSTWAS: Multi-Omic Strategies for Transcriptome-Wide Association Studies

Train and predict gene's predictive model with mediators using MeTWAS

Description

The function trains a predictive model of a given gene using top mediators as fixed effects and assesses in-sample performance with cross-validation.

Usage

MeTWAS(
  geneInt,
  snpObj,
  mediator,
  medLocs,
  covariates,
  dimNumeric,
  qtlFull,
  h2Pcutoff = 0.1,
  numMed = 5,
  seed = 1218,
  k = 5,
  cisDist = 1e+06,
  parallel = T,
  prune = F,
  ldThresh = 0.5,
  cores,
  verbose = T,
  R2Cutoff = 0.01,
  modelDir,
  tempFolder
)

Arguments

geneInt	character, identifier for gene of interest
snpObj	binsnp object, SNP dosages
mediator	data frame, mediator intensities
medLocs	data frame, MatrixEQTL locations for mediators
covariates	data frame, covariates
dimNumeric	numeric, number of numeric covariates
qtlFull	data frame, all QTLs (cis and trans) between mediators and genes
h2Pcutoff	numeric, P-value cutoff for heritability
numMed	integer, number of top mediators to include
seed	integer, random seed for splitting
k	integer, number of training-test splits
parallel	logical, TRUE/FALSE to run glmnet in parallel
prune	logical, TRUE/FALSE to LD prune the genotypes
ldThresh	numeric, LD threshold for PLINK pruning
cores	integer, number of parallel cores
verbose	logical, output everything
R2Cutoff	numeric, cutoff for model R2
modelDir	character, directory for saving models
tempFolder	character, directory of saving snp backing files

Value

final model for gene along with CV R2 and predicted values

bhattacharya-a-bt/MOSTWAS documentation built on April 6, 2023, 12:20 a.m.