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The MR-Base platform supports systematic causal inference across the human phenome

Results from genome-wide association studies (GWAS) can be used to infer causal relationships between phenotypes, using a strategy known as 2-sample Mendelian randomization (2SMR) and bypassing the need for individual-level data. However, 2SMR methods are evolving rapidly and GWAS results are often...

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Autores principales: Hemani, Gibran, Zheng, Jie, Elsworth, Benjamin, Wade, Kaitlin H, Haberland, Valeriia, Baird, Denis, Laurin, Charles, Burgess, Stephen, Bowden, Jack, Langdon, Ryan, Tan, Vanessa Y, Yarmolinsky, James, Shihab, Hashem A, Timpson, Nicholas J, Evans, David M, Relton, Caroline, Martin, Richard M, Davey Smith, George, Gaunt, Tom R, Haycock, Philip C
Formato: Online Artículo Texto
Lenguaje:English
Publicado: eLife Sciences Publications, Ltd 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5976434/
https://www.ncbi.nlm.nih.gov/pubmed/29846171
http://dx.doi.org/10.7554/eLife.34408
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author Hemani, Gibran
Zheng, Jie
Elsworth, Benjamin
Wade, Kaitlin H
Haberland, Valeriia
Baird, Denis
Laurin, Charles
Burgess, Stephen
Bowden, Jack
Langdon, Ryan
Tan, Vanessa Y
Yarmolinsky, James
Shihab, Hashem A
Timpson, Nicholas J
Evans, David M
Relton, Caroline
Martin, Richard M
Davey Smith, George
Gaunt, Tom R
Haycock, Philip C
author_facet Hemani, Gibran
Zheng, Jie
Elsworth, Benjamin
Wade, Kaitlin H
Haberland, Valeriia
Baird, Denis
Laurin, Charles
Burgess, Stephen
Bowden, Jack
Langdon, Ryan
Tan, Vanessa Y
Yarmolinsky, James
Shihab, Hashem A
Timpson, Nicholas J
Evans, David M
Relton, Caroline
Martin, Richard M
Davey Smith, George
Gaunt, Tom R
Haycock, Philip C
author_sort Hemani, Gibran
collection PubMed
description Results from genome-wide association studies (GWAS) can be used to infer causal relationships between phenotypes, using a strategy known as 2-sample Mendelian randomization (2SMR) and bypassing the need for individual-level data. However, 2SMR methods are evolving rapidly and GWAS results are often insufficiently curated, undermining efficient implementation of the approach. We therefore developed MR-Base (http://www.mrbase.org): a platform that integrates a curated database of complete GWAS results (no restrictions according to statistical significance) with an application programming interface, web app and R packages that automate 2SMR. The software includes several sensitivity analyses for assessing the impact of horizontal pleiotropy and other violations of assumptions. The database currently comprises 11 billion single nucleotide polymorphism-trait associations from 1673 GWAS and is updated on a regular basis. Integrating data with software ensures more rigorous application of hypothesis-driven analyses and allows millions of potential causal relationships to be efficiently evaluated in phenome-wide association studies.
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spelling pubmed-59764342018-06-04 The MR-Base platform supports systematic causal inference across the human phenome Hemani, Gibran Zheng, Jie Elsworth, Benjamin Wade, Kaitlin H Haberland, Valeriia Baird, Denis Laurin, Charles Burgess, Stephen Bowden, Jack Langdon, Ryan Tan, Vanessa Y Yarmolinsky, James Shihab, Hashem A Timpson, Nicholas J Evans, David M Relton, Caroline Martin, Richard M Davey Smith, George Gaunt, Tom R Haycock, Philip C eLife Computational and Systems Biology Results from genome-wide association studies (GWAS) can be used to infer causal relationships between phenotypes, using a strategy known as 2-sample Mendelian randomization (2SMR) and bypassing the need for individual-level data. However, 2SMR methods are evolving rapidly and GWAS results are often insufficiently curated, undermining efficient implementation of the approach. We therefore developed MR-Base (http://www.mrbase.org): a platform that integrates a curated database of complete GWAS results (no restrictions according to statistical significance) with an application programming interface, web app and R packages that automate 2SMR. The software includes several sensitivity analyses for assessing the impact of horizontal pleiotropy and other violations of assumptions. The database currently comprises 11 billion single nucleotide polymorphism-trait associations from 1673 GWAS and is updated on a regular basis. Integrating data with software ensures more rigorous application of hypothesis-driven analyses and allows millions of potential causal relationships to be efficiently evaluated in phenome-wide association studies. eLife Sciences Publications, Ltd 2018-05-30 /pmc/articles/PMC5976434/ /pubmed/29846171 http://dx.doi.org/10.7554/eLife.34408 Text en © 2018, Hemani et al http://creativecommons.org/licenses/by/4.0/ http://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited.
spellingShingle Computational and Systems Biology
Hemani, Gibran
Zheng, Jie
Elsworth, Benjamin
Wade, Kaitlin H
Haberland, Valeriia
Baird, Denis
Laurin, Charles
Burgess, Stephen
Bowden, Jack
Langdon, Ryan
Tan, Vanessa Y
Yarmolinsky, James
Shihab, Hashem A
Timpson, Nicholas J
Evans, David M
Relton, Caroline
Martin, Richard M
Davey Smith, George
Gaunt, Tom R
Haycock, Philip C
The MR-Base platform supports systematic causal inference across the human phenome
title The MR-Base platform supports systematic causal inference across the human phenome
title_full The MR-Base platform supports systematic causal inference across the human phenome
title_fullStr The MR-Base platform supports systematic causal inference across the human phenome
title_full_unstemmed The MR-Base platform supports systematic causal inference across the human phenome
title_short The MR-Base platform supports systematic causal inference across the human phenome
title_sort mr-base platform supports systematic causal inference across the human phenome
topic Computational and Systems Biology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5976434/
https://www.ncbi.nlm.nih.gov/pubmed/29846171
http://dx.doi.org/10.7554/eLife.34408
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