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多弱工具变量下多变量孟德尔随机化的无偏估计与有效渐近推断AI 解读

Mendelian randomization (MR) is an instrumental variable (IV) approach to infer causal relationships between exposures and outcomes with genome-wide association studies (GWAS) summary data. However, the multivariable inverse-variance weighting (IVW) approach, which serves as the foundation for most MR approaches, cannot yield unbiased causal effect estimates in the presence of many weak IVs. To address this problem, we proposed the MR using Bias-corrected Estimating Equation…

利用 GWAS 汇总数据估计遗传高斯网络AI 解读

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…