lmmTest: Fit mixed model to methylation values in one genomic region

In lissettegomez/coMethDMR: Accurate identification of co-methylated and differentially methylated regions in epigenome-wide association studies

Description

Fit mixed model to methylation values in one genomic region

Usage

lmmTest(
  betaOne_df,
  pheno_df,
  contPheno_char,
  covariates_char,
  modelType = c("randCoef", "simple"),
  genome = c("hg19", "hg38"),
  arrayType = c("450k", "EPIC"),
  outLogFile = NULL
)

Arguments

betaOne_df	matrix of beta values for one genomic region, with row names = CpG IDs, column names = sample IDs
pheno_df	a data frame with phenotype and covariates, with variable Sample indicating sample IDs.
contPheno_char	character string of the main effect (a continuous phenotype) to be tested for association with methylation values in the region
covariates_char	character vector for names of the covariate variables
modelType	type of mixed model, can be randCoef for random coefficient mixed model, or simple for simple linear mixed model.
genome	Human genome of reference hg19 or hg38
arrayType	Type of array, can be "450k" or "EPIC"
outLogFile	Name of log file for messages of mixed model analysis

Details

This function implements a mixed model to test association between methylation values in a genomic region with a continuous phenotype.

When randCoef is selected, the model is

methylation M value ~ contPheno_char + covariates_char + (1|Sample) + (contPheno_char|CpG). The last term specifies random intercept and slope for each CpG.

When simple is selected, the model is

methylation M value ~ contPheno_char + covariates_char + (1|Sample)

In our simulation studies, we found both models are conservative, so p-values are estimated from normal distributions instead of t-distributions.

Value

A dataframe with one row for association result of one region: Estimate, StdErr, and pvalue for the association of methylation values in the genomic region tested vs. continuous phenotype contPheno_char

Examples

  data(betasChr22_df)

  CpGsChr22_char <- c(
    "cg02953382", "cg12419862", "cg24565820", "cg04234412", "cg04824771",
    "cg09033563", "cg10150615", "cg18538332", "cg20007245", "cg23131131",
    "cg25703541"
  )

  coMethCpGs <- CoMethSingleRegion(CpGsChr22_char, betasChr22_df)

  # test only the first co-methylated region
  coMethBeta_df <- betasChr22_df[coMethCpGs$CpGsSubregions[[1]], ]

  data(pheno_df)

  res <- lmmTest(
    betaOne_df = coMethBeta_df,
    pheno_df,
    contPheno_char = "stage",
    covariates_char = c("age.brain", "sex"),
    modelType = "randCoef",
    arrayType = "450k"
  )

lissettegomez/coMethDMR documentation built on April 25, 2021, 1:10 p.m.