  Genetic Gaussian network of multiple phenotypes constructed through the
genetic correlation matrix is informative for understanding their biological
dependencies. However, its interpretation may be challenging because the
estimated genetic correlations are biased due to estimation errors and
horizontal pleiotropy inherent in GWAS summary statistics. Here we introduce a
novel approach called Estimation of Genetic Graph (EGG), which eliminates the
estimation error bias and horizontal pleiotropy bias with the same techniques
used in multivariable Mendelian randomization. The genetic network estimated by
EGG can be interpreted as representing shared common biological contributions
between phenotypes, conditional on others, and even as indicating the causal
contributions. We use both simulations and real data to demonstrate the
superior efficacy of our novel method in comparison with the traditional
network estimators. R package EGG is available on
https://github.com/harryyiheyang/EGG.
