Mendelian Randomization With Refined Instrumental Variables From Genetic Score Improves Accuracy and Reduces Bias - Lin, Lijuan; Zhang, Ruyang; Huang, Hui; Zhu, Ying; Li, Yi; Dong, Xuesi; Shen, Sipeng; Wei, Liangmin; Chen, Xin; Christiani, David C.; Wei, Yongyue; Chen, Feng (Frontiers in Genetics, 2021)
Mendelian randomization (MR) can estimate the causal effect for a risk factor on a complex disease using genetic variants as instrument variables (IVs). A variety of generalized MR methods have been proposed to integrate results arising from multiple IVs in order to increase power. One of the methods construct the genetic score (GS) by a linear combination of the multiple IVs using multiple regression model, which was applied in the medical researches broadly. However, this GS based MR requires individual level data which greatly limit its application in clinical research. We propose an alternative method called Mendelian Randomization with Refined Instrumental Variable from Genetic Score (MR-RIVER) to construct a genetic IV by summarized results, rather than individual data, of multiple genetic variants. Comparing with inverse-variance weighted (IVW) and generalized summary-data-based Mendelian randomization (GSMR), MR-RIVER maintained the type I error, while possessing more statistical power than the competing methods. MR-RIVER presented smaller biases and mean squared errors, compared to the IVW and GSMR. We further applied the proposed method to estimate the effects of blood metabolites on educational attainment, by integrating results from several publicly available resources. MR-RIVER provided robust results under different LD prune criteria, identified three metabolites associated with years of schooling, and additional 15 metabolites with indirect mediation effects through butyrylcarnitine. MR-RIVER, which extends score-based MR to summarized results in lieu of individual data and incorporates multiple correlated IVs, provided a more accurate and powerful means for the discovery of novel risk factors.