The environment-adjusted meta-analysis output from env-MR-MEGA

The environment-adjusted meta-analysis results of genetic variants across all the genome. Considering the impact of environmental exposures that differ across GWAS, the environment-adjusted meta-regression model is build upon the MR-MEGA meta-regression framework by adding study-level environmental covariates. This allows us to identify genetic variants that associated with the disease or trait while adjusting for differing environmentl exposures between cohort.

envMRMEGAout

Format

A dataframe with 301 genetic variants and 27 variables:

MARKERNAME

unique marker identification across input files.

beta0

effect of the intercept of meta-regression.

se0

std error of the intercept of meta-regression.

beta_i

effect of the i-th PC or i-th environment covariate of environment-adjusted meta-regression.

se_i

std error of the effect of the i-th PC or i-th environment covariate of environment-adjusted meta-regression.

chisq_association

chisq value of the association.

ndf_association

the number of degrees of freedom of the association.

pvalue_association

p-value of the association.

chisq_heter

chisq value of the ancestral and environmental heterogeneity.

ndf_heter

the number of degrees of freedom of ancestral and environmental heterogeneity.

pvalue_heter

p-value of ancestral and environmental heterogeneity.

chisq_residual

chisq value of the residual heterogeneity.

ndf_residual

the number of degrees of freedom of the residual heterogeneity.

pvalue_residual

p-value of the residual heterogeneity.

logBF

log of Bayesian Factors.

chisq_env

chisq value of environmental heterogeneity.

ndf_env

the number of degrees of freedom of environmental heterogeneity.

pvalue_env

p-value of environmental heterogeneity.

chisq_PC

chisq value of ancestral heterogeneity.

ndf_PC

the number of degrees of freedom of ancestral heterogeneity.

pvalue_PC

p-value of ancestral heterogeneity.