Yang Yihe, Lorincz-Comi Noah, Zhu Xiaofeng
Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH 44106, United States.
Biometrics. 2024 Oct 3;80(4). doi: 10.1093/biomtc/ujae148.
A genetic Gaussian network of multiple phenotypes, constructed through the inverse matrix of the genetic correlation matrix, is informative for understanding the biological dependencies of the phenotypes. However, its estimation may be challenging because the genetic correlation estimates are biased due to estimation errors and idiosyncratic 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 idiosyncratic pleiotropy bias with the same techniques used in multivariable Mendelian randomization. The genetic network estimated by EGG can be interpreted as shared common biological contributions between phenotypes, conditional on others. We use both simulations and real data to demonstrate the superior efficacy of our novel method in comparison with the traditional network estimators.
通过遗传相关矩阵的逆矩阵构建的多表型遗传高斯网络，有助于理解表型之间的生物学依赖性。然而，其估计可能具有挑战性，因为由于全基因组关联研究（GWAS）汇总统计中固有的估计误差和特异多效性，遗传相关估计存在偏差。在此，我们引入一种名为遗传图估计（EGG）的新方法，该方法使用多变量孟德尔随机化中相同的技术消除估计误差偏差和特异多效性偏差。EGG估计的遗传网络可以解释为在以其他表型为条件的情况下，各表型之间共享的共同生物学贡献。我们使用模拟数据和真实数据来证明我们的新方法与传统网络估计器相比具有更高的功效。
