The meta-analysis output from MR-MEGA

A meta-analysis results of genetic variants across all the genome. This approach uses genome-wide metrics of diversity between populations to derive axes of genetic variation via multi-dimensional scaling. Allelic effects of a variant across GWAS, weighted by their corresponding standard errors, can then be modelled in a linear regression framework, including the axes of genetic variation as covariates.

MRMEGAout

Format

A dataframe with 301 genetic variants and 17 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 of meta-regression.

se_i

std error of the effect of the i-th PC of 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_anceheter

chisq value of the heterogeneity due to different ancestry.

ndf_anceheter

the number of degrees of freedom of ancestral heterogeneity.

pvalue_anceheter

p-value of the ancestral 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.