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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...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8044958/ https://www.ncbi.nlm.nih.gov/pubmed/33868364 http://dx.doi.org/10.3389/fgene.2021.618829 |
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author | 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 |
author_facet | 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 |
author_sort | Lin, Lijuan |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8044958 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-80449582021-04-15 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 Front Genet Genetics 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. Frontiers Media S.A. 2021-03-17 /pmc/articles/PMC8044958/ /pubmed/33868364 http://dx.doi.org/10.3389/fgene.2021.618829 Text en Copyright © 2021 Lin, Zhang, Huang, Zhu, Li, Dong, Shen, Wei, Chen, Christiani, Wei and Chen. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Genetics 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 Mendelian Randomization With Refined Instrumental Variables From Genetic Score Improves Accuracy and Reduces Bias |
title | Mendelian Randomization With Refined Instrumental Variables From Genetic Score Improves Accuracy and Reduces Bias |
title_full | Mendelian Randomization With Refined Instrumental Variables From Genetic Score Improves Accuracy and Reduces Bias |
title_fullStr | Mendelian Randomization With Refined Instrumental Variables From Genetic Score Improves Accuracy and Reduces Bias |
title_full_unstemmed | Mendelian Randomization With Refined Instrumental Variables From Genetic Score Improves Accuracy and Reduces Bias |
title_short | Mendelian Randomization With Refined Instrumental Variables From Genetic Score Improves Accuracy and Reduces Bias |
title_sort | mendelian randomization with refined instrumental variables from genetic score improves accuracy and reduces bias |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8044958/ https://www.ncbi.nlm.nih.gov/pubmed/33868364 http://dx.doi.org/10.3389/fgene.2021.618829 |
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