glmm.gei {MAGEE}

GLMM based single variant tests for gene-environment interactions

Description

Use a glmmkin class object from the null GLMM to perform single variant main effect score test, gene-environment interaction test, or joint test for association with genotypes in a GDS file .gds.

Usage

glmm.gei(null.obj, interaction, geno.file, outfile, bgen.samplefile = NULL, 
  interaction.covariates = NULL,  meta.output = FALSE,
  covar.center="interaction.covariates.only", geno.center=TRUE, 
  MAF.range = c(1e-7, 0.5), MAC.cutoff = 1, miss.cutoff = 1, RSQ.cutoff = 0,
  missing.method = "impute2mean", nperbatch=100, is.dosage = FALSE,
  ncores = 1, verbose = FALSE)

Arguments

null.obj

a class glmmkin object, returned by fitting the null GLMM using glmmkin( ).

interaction

a numeric or a character vector indicating the environmental factors. If a numeric vector, it represents which indices in the order of covariates are the environmental factors; if a character vector, it represents the variable names of the environmental factors.

geno.file

the full name of a GDS file (including the suffix .gds).

outfile

the output file name.

bgen.samplefile

path to the BGEN .sample file. Required when the BGEN file does not contain sample identifiers.

interaction.covariates

a numeric or a character vector indicating the interaction covariates. If a numeric vector, it represents which indices in the order of covariates are the interaction covariates; if a character vector, it represents the variable names of the interaction covariates.

meta.output

boolean value to modiy the output file.If TRUE, the GxE effect estimate and variance and covariance associated with the effect estimate are included in the output file. (default = FALSE)

covar.center

a character value for the centering option for covariates. Possible values are "none", "all", or "interaction.covariates.only". Generally, centering exposures and covariates to have mean 0 before creating interaction terms would make the genetic main effect easier to interpret. However, if a subsequent meta-analysis is expected, then the exposures of interest should not be centered because in that case the genetic main effect may have different interpretations across studies (default = "interaction.covariates.only").

geno.center

a logical switch for centering genotypes before tests. If TRUE, genotypes will be centered to have mean 0 before tests, otherwise raw values will be directly used in tests (default = TRUE).

MAF.range

a numeric vector of length 2 defining the minimum and maximum minor allele frequencies of variants that should be included in the analysis (default = c(1e-7, 0.5)).

MAC.cutoff

the minimum minor allele count allowed for a variant to be included (default = 1, including all variants).

miss.cutoff

the maximum missing rate allowed for a variant to be included (default = 1, including all variants).

RSQ.cutoff

the minimum Rsq value, defined as the ratio of observed and expected genotypic variance under Hardy-Weinberg equilibrium, allowed for a variant to be included (default = 0, including all variants).

missing.method

method of handling missing genotypes.Either "impute2mean" or "omit" (default = "impute2mean").

nperbatch

an integer for how many SNPs should be tested in a batch (default = 100). The computational time can increase dramatically if this value is either small or large. The optimal value for best performance depends on the user's system.

is.dosage

a logical switch for whether imputed dosage should be used from a GDS geno.file (default = FALSE).

ncores

a positive integer indicating the number of cores to be used in parallel computing (default = 1).

verbose

a logical switch for whether failed matrix inversions should be written to outfile.err for debugging (default = FALSE).

Value

NULL

Author(s)

Xinyu Wang, Han Chen, Duy Pham

References

Chen, H., Wang, C., Conomos, M.P., Stilp, A.M., Li, Z., Sofer, T., Szpiro, A.A., Chen, W., Brehm, J.M., Celedón, J.C., Redline, S., Papanicolaou, G.J., Thornton, T.A., Laurie, C.C., Rice, K. and Lin, X. (2016) Control forpopulation structure and relatedness for binary traits in genetic association studies via logistic mixed models. The American Journal of Human Genetics 98, 653-666.

Examples

  library(GMMAT)
  data(example)
  attach(example)

  model0 <- glmmkin(disease ~ age + sex, data = pheno, kins = GRM,
                    id = "id", family = binomial(link = "logit"))
                   
  if(requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools",
    quietly = TRUE)) {
    infile <- system.file("extdata", "geno.gds", package = "MAGEE")
    gds_outfile <- tempfile()
    glmm.gei(model0, interaction='sex', geno.file = infile, outfile = gds_outfile)
    unlink(gds_outfile)
  }
  infile <- system.file("extdata", "geno.bgen", package = "MAGEE")
  samplefile <- system.file("extdata", "geno.sample", package = "MAGEE")
  bgen_outfile <- tempfile()
  glmm.gei(model0, interaction='sex', geno.file = infile, outfile = bgen_outfile,
           bgen.samplefile = samplefile)
  unlink(bgen_outfile)