MAGEE (version 1.4.3)

glmm.gei.meta: GLMM based meta-analysis of single variant tests for gene-environment interactions

Description

Use a glmmkin class object from the null GLMM to perform meta-analysis of 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.meta(files, outfile, interaction, SNPID = rep("SNPID", length(files)), 
              CHR = rep("CHR", length(files)), POS = rep("POS", length(files)), 
              Non_Effect_Allele = rep("Non_Effect_Allele", length(files)), 
              Effect_Allele = rep("Effect_Allele", length(files)))

Value

a data frame containing the following:

SNPID

SNP name.

CHR

chromosome.

POS

physical position.

Non_Effect_Allele

non_effect allele frequency.

Effect_Allele

effect allele frequency.

N_Samples

number of samples.

AF

allele frequency.

Beta_Marginal

coefficient estimate for the marginal genetic effect.

SE_Beta_Marginal

standard error of the marginal genetic effect.

P_Value_Marginal

marginal effect score test p-value.

Beta_G

coefficient estimate for the genetic main effect.

Beta_G-*

coefficient estimate for the interaction terms.

SE_Beta_G

model-based standard error associated with the the genetic main effect.

SE_Beta_G-*

mdel-based standard error associated with any GxE or interaction covariate terms.

Cov_Beta_G_G-*

model-based covariance between the genetic main effect and any GxE or interaction covariate terms.

P_Value_Interaction

gene-environment interaction test p-value.

P_Value_Joint

joint test p-value.

Arguments

files

tab or space delimited plain text files (or compressed files that can be recognized by the R function read.table) with at least the following columns: SNPID, CHR, POS, Non_Effect_Allele, Effect_Allele, N_Samples, AF, Beta_Marginal, SE_Beta_Marginal, P_Value_Marginal, Beta_G, Beta_G_sex, SE_Beta_G, SE_Beta_G_sex, Cov_Beta_G_G.sex, P_Value_Interaction, P_Value_Joint. Generally, if each study performs score tests using genotypes in PLINK binary PED format or GDS format, the score test output from glmm.score can be directly used as input files.

outfile

the output file name.

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.

SNPID

a character vector of SNPID column names in each input file. The length and order must match the length and order of files (default = rep("SNPID", length(files))).

CHR

a character vector of CHR column names in each input file. The length and order must match the length and order of files (default = rep("CHR", length(files))).

POS

a character vector of POS column names in each input file. The length and order must match the length and order of files (default = rep("POS", length(files))).

Non_Effect_Allele

a character vector of Non_Effect_Allele column names in each input file. The length and order must match the length and order of files (default = rep("Non_Effect_Allele", length(files))).

Effect_Allele

a character vector of Effect_Allele column names in each input file. The length and order must match the length and order of files (default = rep("Effect_Allele", length(files))).

Author

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

# \donttest{
  infile1 <- system.file("extdata", "meta1.txt", package = "MAGEE")
  infile2 <- system.file("extdata", "meta2.txt", package = "MAGEE")
  infile3 <- system.file("extdata", "meta3.txt", package = "MAGEE")
  infile4 <- system.file("extdata", "meta4.txt", package = "MAGEE")
  infile5 <- system.file("extdata", "meta5.txt", package = "MAGEE")
  outfile <- tempfile()
  glmm.gei.meta(files = c(infile1, infile2, infile3, infile4, infile5),
		outfile = outfile, interaction="sex")
  unlink(outfile)
# }