Combining Mendelian randomization and network deconvolution for inference of causal networks with GWAS summary data.
Mendelian randomization (MR) has been increasingly applied for causal inference with observational data by using genetic variants as instrumental variables (IVs). However, the current practice of MR has been largely restricted to investigating the total causal effect between two traits, while it wou...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2023-05-01
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| Series: | PLoS Genetics |
| Online Access: | https://doi.org/10.1371/journal.pgen.1010762 |
| _version_ | 1797802690441379840 |
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| author | Zhaotong Lin Haoran Xue Wei Pan |
| author_facet | Zhaotong Lin Haoran Xue Wei Pan |
| author_sort | Zhaotong Lin |
| collection | DOAJ |
| description | Mendelian randomization (MR) has been increasingly applied for causal inference with observational data by using genetic variants as instrumental variables (IVs). However, the current practice of MR has been largely restricted to investigating the total causal effect between two traits, while it would be useful to infer the direct causal effect between any two of many traits (by accounting for indirect or mediating effects through other traits). For this purpose we propose a two-step approach: we first apply an extended MR method to infer (i.e. both estimate and test) a causal network of total effects among multiple traits, then we modify a graph deconvolution algorithm to infer the corresponding network of direct effects. Simulation studies showed much better performance of our proposed method than existing ones. We applied the method to 17 large-scale GWAS summary datasets (with median N = 256879 and median #IVs = 48) to infer the causal networks of both total and direct effects among 11 common cardiometabolic risk factors, 4 cardiometabolic diseases (coronary artery disease, stroke, type 2 diabetes, atrial fibrillation), Alzheimer's disease and asthma, identifying some interesting causal pathways. We also provide an R Shiny app (https://zhaotongl.shinyapps.io/cMLgraph/) for users to explore any subset of the 17 traits of interest. |
| first_indexed | 2024-03-13T05:10:31Z |
| format | Article |
| id | doaj.art-f9c233ee4bdb41009c7a80ff195af144 |
| institution | Directory Open Access Journal |
| issn | 1553-7390 1553-7404 |
| language | English |
| last_indexed | 2024-03-13T05:10:31Z |
| publishDate | 2023-05-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Genetics |
| spelling | doaj.art-f9c233ee4bdb41009c7a80ff195af1442023-06-16T05:30:55ZengPublic Library of Science (PLoS)PLoS Genetics1553-73901553-74042023-05-01195e101076210.1371/journal.pgen.1010762Combining Mendelian randomization and network deconvolution for inference of causal networks with GWAS summary data.Zhaotong LinHaoran XueWei PanMendelian randomization (MR) has been increasingly applied for causal inference with observational data by using genetic variants as instrumental variables (IVs). However, the current practice of MR has been largely restricted to investigating the total causal effect between two traits, while it would be useful to infer the direct causal effect between any two of many traits (by accounting for indirect or mediating effects through other traits). For this purpose we propose a two-step approach: we first apply an extended MR method to infer (i.e. both estimate and test) a causal network of total effects among multiple traits, then we modify a graph deconvolution algorithm to infer the corresponding network of direct effects. Simulation studies showed much better performance of our proposed method than existing ones. We applied the method to 17 large-scale GWAS summary datasets (with median N = 256879 and median #IVs = 48) to infer the causal networks of both total and direct effects among 11 common cardiometabolic risk factors, 4 cardiometabolic diseases (coronary artery disease, stroke, type 2 diabetes, atrial fibrillation), Alzheimer's disease and asthma, identifying some interesting causal pathways. We also provide an R Shiny app (https://zhaotongl.shinyapps.io/cMLgraph/) for users to explore any subset of the 17 traits of interest.https://doi.org/10.1371/journal.pgen.1010762 |
| spellingShingle | Zhaotong Lin Haoran Xue Wei Pan Combining Mendelian randomization and network deconvolution for inference of causal networks with GWAS summary data. PLoS Genetics |
| title | Combining Mendelian randomization and network deconvolution for inference of causal networks with GWAS summary data. |
| title_full | Combining Mendelian randomization and network deconvolution for inference of causal networks with GWAS summary data. |
| title_fullStr | Combining Mendelian randomization and network deconvolution for inference of causal networks with GWAS summary data. |
| title_full_unstemmed | Combining Mendelian randomization and network deconvolution for inference of causal networks with GWAS summary data. |
| title_short | Combining Mendelian randomization and network deconvolution for inference of causal networks with GWAS summary data. |
| title_sort | combining mendelian randomization and network deconvolution for inference of causal networks with gwas summary data |
| url | https://doi.org/10.1371/journal.pgen.1010762 |
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