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.

翻译：通过遗传相关矩阵构建的多表型遗传高斯网络有助于理解其生物学依赖关系。然而，由于GWAS汇总统计量固有的估计误差和水平多效性，估计的遗传相关存在偏倚，因此该网络的解释可能具有挑战性。本文提出一种名为遗传图估计（EGG）的新方法，该方法利用与多变量孟德尔随机化相同的技术消除估计误差偏倚和水平多效性偏倚。经EGG估计的遗传网络可解释为表型间条件于其他表型时共享的共同生物学贡献，甚至可指示因果贡献。我们通过模拟和真实数据证明，与传统网络估计方法相比，新方法具有显著优越性。R包EGG详见https://github.com/harryyiheyang/EGG。