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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| Format: | Article |
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Frontiers Media S.A.
2021-03-01
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| Series: | Frontiers in Genetics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2021.618829/full |
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| author | Lijuan Lin Ruyang Zhang Ruyang Zhang Ruyang Zhang Ruyang Zhang Hui Huang Ying Zhu Yi Li Xuesi Dong Xuesi Dong Sipeng Shen Liangmin Wei Xin Chen David C. Christiani David C. Christiani David C. Christiani Yongyue Wei Yongyue Wei Yongyue Wei Yongyue Wei Feng Chen Feng Chen Feng Chen Feng Chen |
| author_facet | Lijuan Lin Ruyang Zhang Ruyang Zhang Ruyang Zhang Ruyang Zhang Hui Huang Ying Zhu Yi Li Xuesi Dong Xuesi Dong Sipeng Shen Liangmin Wei Xin Chen David C. Christiani David C. Christiani David C. Christiani Yongyue Wei Yongyue Wei Yongyue Wei Yongyue Wei Feng Chen Feng Chen Feng Chen Feng Chen |
| author_sort | Lijuan Lin |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-14T19:01:36Z |
| format | Article |
| id | doaj.art-51e236529d8b4de389c59fdbbef12aa2 |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-12-14T19:01:36Z |
| publishDate | 2021-03-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Genetics |
| spelling | doaj.art-51e236529d8b4de389c59fdbbef12aa22022-12-21T22:50:57ZengFrontiers Media S.A.Frontiers in Genetics1664-80212021-03-011210.3389/fgene.2021.618829618829Mendelian Randomization With Refined Instrumental Variables From Genetic Score Improves Accuracy and Reduces BiasLijuan Lin0Ruyang Zhang1Ruyang Zhang2Ruyang Zhang3Ruyang Zhang4Hui Huang5Ying Zhu6Yi Li7Xuesi Dong8Xuesi Dong9Sipeng Shen10Liangmin Wei11Xin Chen12David C. Christiani13David C. Christiani14David C. Christiani15Yongyue Wei16Yongyue Wei17Yongyue Wei18Yongyue Wei19Feng Chen20Feng Chen21Feng Chen22Feng Chen23Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, ChinaDepartment of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, ChinaChina International Cooperation Center for Environment and Human Health, School of Public Health, Nanjing Medical University, Nanjing, ChinaJiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, ChinaState Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, ChinaDepartment of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, ChinaDepartment of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, ChinaDepartment of Biostatistics, University of Michigan, Ann Arbor, MI, United StatesDepartment of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, ChinaDepartment of Epidemiology and Biostatistics, School of Public Health, Southeast University, Nanjing, ChinaDepartment of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, ChinaDepartment of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, ChinaDepartment of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, ChinaChina International Cooperation Center for Environment and Human Health, School of Public Health, Nanjing Medical University, Nanjing, ChinaDepartment of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, United StatesDivision of Pulmonary and Critical Care Medicine, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United StatesDepartment of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, ChinaChina International Cooperation Center for Environment and Human Health, School of Public Health, Nanjing Medical University, Nanjing, ChinaJiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, ChinaState Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, ChinaDepartment of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, ChinaChina International Cooperation Center for Environment and Human Health, School of Public Health, Nanjing Medical University, Nanjing, ChinaJiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, ChinaState Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, ChinaMendelian 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.https://www.frontiersin.org/articles/10.3389/fgene.2021.618829/fullMendelian randomizationmultiple correlated instrumental variablesgenetic scoremetabolomicseducational attainment |
| spellingShingle | Lijuan Lin Ruyang Zhang Ruyang Zhang Ruyang Zhang Ruyang Zhang Hui Huang Ying Zhu Yi Li Xuesi Dong Xuesi Dong Sipeng Shen Liangmin Wei Xin Chen David C. Christiani David C. Christiani David C. Christiani Yongyue Wei Yongyue Wei Yongyue Wei Yongyue Wei Feng Chen Feng Chen Feng Chen Feng Chen Mendelian Randomization With Refined Instrumental Variables From Genetic Score Improves Accuracy and Reduces Bias Frontiers in Genetics Mendelian randomization multiple correlated instrumental variables genetic score metabolomics educational attainment |
| 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 | Mendelian randomization multiple correlated instrumental variables genetic score metabolomics educational attainment |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2021.618829/full |
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