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

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@article{2527d3a630d74b8283306961def3191d,
title = "The MR-Base platform supports systematic causal inference across the human phenome",
abstract = "Summary data from genome-wide association studies (GWAS) can be used to infer causal relationships between phenotypes, using an analytical strategy known as 2-sample Mendelian randomization (2SMR), bypassing the need for individual-level data. However, 2SMR methods are evolving rapidly and can be difficult to implement, while GWAS summary data required for 2SMR are often not systematically curated, undermining efficient implementation of the approach. To address these challenges, we developed MR-Base (http://www.mrbase.org): a platform that integrates a curated database of GWAS summary data for thousands of traits obtained from UK Biobank and trait-specific GWAS studies, with R packages and a web app that automates several analytical strategies for causal inference through 2SMR. The software includes many sensitivity analyses for assessing analytical assumptions. The database curates 24 billion SNP-trait associations from 1693 GWAS studies. Integrating data with software ensures more rigorous application of hypothesis-driven analyses, whilst enabling millions of potential causal relationships to be systematically and efficiently evaluated in phenome-wide association studies (PheWAS).",
author = "Gibran Hemani and Jie Zheng and Benjamin Elsworth and Kaitlin Wade and Valeriia Haberland and Denis Baird and Charles Laurin and Stephen Burgess and Jack Bowden and Ryan Langdon and Vanessa Tan and James Yarmolinsky and Hashem Shihab and Nicholas Timpson and David Evans and Caroline Relton and Richard Martin and {Davey Smith}, George and Tom Gaunt and Philip Haycock",
year = "2018",
month = "5",
day = "30",
doi = "10.7554/eLife.34408",
language = "English",
volume = "7",
journal = "eLife",
issn = "2050-084X",
publisher = "eLife Sciences Publications",

}

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TY - JOUR

T1 - The MR-Base platform supports systematic causal inference across the human phenome

AU - Hemani, Gibran

AU - Zheng, Jie

AU - Elsworth, Benjamin

AU - Wade, Kaitlin

AU - Haberland, Valeriia

AU - Baird, Denis

AU - Laurin, Charles

AU - Burgess, Stephen

AU - Bowden, Jack

AU - Langdon, Ryan

AU - Tan, Vanessa

AU - Yarmolinsky, James

AU - Shihab, Hashem

AU - Timpson, Nicholas

AU - Evans, David

AU - Relton, Caroline

AU - Martin, Richard

AU - Davey Smith, George

AU - Gaunt, Tom

AU - Haycock, Philip

PY - 2018/5/30

Y1 - 2018/5/30

N2 - Summary data from genome-wide association studies (GWAS) can be used to infer causal relationships between phenotypes, using an analytical strategy known as 2-sample Mendelian randomization (2SMR), bypassing the need for individual-level data. However, 2SMR methods are evolving rapidly and can be difficult to implement, while GWAS summary data required for 2SMR are often not systematically curated, undermining efficient implementation of the approach. To address these challenges, we developed MR-Base (http://www.mrbase.org): a platform that integrates a curated database of GWAS summary data for thousands of traits obtained from UK Biobank and trait-specific GWAS studies, with R packages and a web app that automates several analytical strategies for causal inference through 2SMR. The software includes many sensitivity analyses for assessing analytical assumptions. The database curates 24 billion SNP-trait associations from 1693 GWAS studies. Integrating data with software ensures more rigorous application of hypothesis-driven analyses, whilst enabling millions of potential causal relationships to be systematically and efficiently evaluated in phenome-wide association studies (PheWAS).

AB - Summary data from genome-wide association studies (GWAS) can be used to infer causal relationships between phenotypes, using an analytical strategy known as 2-sample Mendelian randomization (2SMR), bypassing the need for individual-level data. However, 2SMR methods are evolving rapidly and can be difficult to implement, while GWAS summary data required for 2SMR are often not systematically curated, undermining efficient implementation of the approach. To address these challenges, we developed MR-Base (http://www.mrbase.org): a platform that integrates a curated database of GWAS summary data for thousands of traits obtained from UK Biobank and trait-specific GWAS studies, with R packages and a web app that automates several analytical strategies for causal inference through 2SMR. The software includes many sensitivity analyses for assessing analytical assumptions. The database curates 24 billion SNP-trait associations from 1693 GWAS studies. Integrating data with software ensures more rigorous application of hypothesis-driven analyses, whilst enabling millions of potential causal relationships to be systematically and efficiently evaluated in phenome-wide association studies (PheWAS).

U2 - 10.7554/eLife.34408

DO - 10.7554/eLife.34408

M3 - Article

VL - 7

JO - eLife

JF - eLife

SN - 2050-084X

M1 - e34408

ER -