Mendelian Randomization With Refined Instrumental Variables From Genetic Score Improves Accuracy and Reduces Bias
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 method...
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Published in | Frontiers in genetics Vol. 12; p. 618829 |
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Main Authors | , , , , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
Switzerland
Frontiers Media S.A
17.03.2021
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Subjects | |
Online Access | Get full text |
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Summary: | 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. |
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Bibliography: | Edited by: Yaozhong Liu, Tulane University, United States This article was submitted to Statistical Genetics and Methodology, a section of the journal Frontiers in Genetics These authors have contributed equally to this work Reviewed by: Yuehua Cui, Michigan State University, United States; Paola Sebastiani, Boston University, United States Senior author |
ISSN: | 1664-8021 1664-8021 |
DOI: | 10.3389/fgene.2021.618829 |