Mendelian Randomization With Refined Instrumental Variables From Genetic Score Improves Accuracy and Reduces Bias.

Lijuan Lin,Ying Zhu,Hui Huang,Xuesi Dong,Yi Li,David C Christiani,Xin Chen,Sipeng Shen,Ruyang Zhang,Yongyue Wei,Liangmin Wei,Feng Chen

doi:10.3389/fgene.2021.618829

Abstract

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 constructs the genetic score (GS) by a linear combination of the multiple IVs using the multiple regression model, which was applied in medical researches broadly. However, 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 integrating multiple genetic variants based on summarized results, rather than individual data. Compared 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 also 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 and 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.

Highlights

Observational studies have long been utilized to detect associations between the exposures of interest and the risk of complex diseases
We proposed an improved MR approach, MR-RIVER, to combine summarized results of multiple Instrumental variable (IV) into a single GS and to estimate the unbiased causal effect of a risk factor on an outcome
MR-RIVER provides a novel way to refine the effect size of genetic variants account for the correlation based on summary data and makes it efficient to perform summarized data genetic score MR when the correlation between IVs are unignorable

Summary

Introduction

Observational studies have long been utilized to detect associations between the exposures of interest and the risk of complex diseases. The estimated effects are typically biased and causality cannot be directly inferred because of unobserved confounders or reverse causality (Ebrahim and Davey Smith, 2008). Double-blind randomized controlled trials with perfect adherence, which use randomization allocation to avoid potential confounding, are often considered as the gold standard to infer causality (Bothwell et al, 2016). Logistical difficulties limit the use in real-world studies. Instrumental variable (IV) analysis provides unbiased causal estimates in the presence of observed and unobserved confounders under certain assumptions (Burgess et al, 2017). Because human germline genetic variants usually form at fertilization and remain unchanged after birth (Ference et al, 2019), they are less likely to be correlated with the environmental or clinical factors but can be correlated with susceptibility to these factors that are associated with outcomes and are ideal candidates for IVs

Methods

Results

Conclusion