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

By Gibran Hemani, Jie Zheng, B. Elsworth, K. Wade, Valeriia Haberland, Denis Baird, Charles Laurin, Stephen Burgess, J. Bowden, R. Langdon and 10 more. eLife, 2018

6,483 citations recorded by the source corpus.

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.

Subfields: Genetics

View Paper: https://www.semanticscholar.org/paper/fcb957b86148da2c27e6ee97d85589bf905f425f

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In Genetics

Citations in the corpus

Eliciting priors and relaxing the single causal variant assumption in colocalisation analyses (2019, background): “[6] However, the ubiq-uity of genetic effects on some measurable aspect of human physiology or health, which have prompted suggestions of an omnigenic model, [7] raise concerns that LD between causal variants can violate the MR assumption that the instrumental variable is only associated with the…”
Plasma proteomic associations with genetics and health in the UK Biobank (2023, methodology): “Data for these outcomes were extracted from the OpenGWAS project 72,73 .”
The MRC IEU OpenGWAS data infrastructure (2020, methodology): “Methodological advancements have been made in particular in Mendelian randomization (MR) 6 , genetic colocalisation 7–9 and genetic correlation 10,11 that require only GWAS summary data to explore the shared aetiology of two or more traits.”
Evaluating the relationship between circulating lipoprotein lipids and apolipoproteins with risk of coronary heart disease: A multivariable Mendelian randomisation analysis (2020, methodology): “MR analyses were conducted using the ‘TwoSampleMR’ R package [46].”
MetaboAnalyst 6.0: towards a unified platform for metabolomics data processing, analysis and interpretation (2024, methodology): “Based on the selections, the program searches for potential instrumental variables (i.e. SNPs) that are associated with both the metabolite from our large collections of the recent mGWAS studies ( 14 ) and the disease from the OpenGWAS database ( 17 ).”
Evaluating the potential role of pleiotropy in Mendelian randomization studies (2018, methodology): “It is also common to see leaveone-out analyses where the causal effect is re-estimated but sequentially omitting a particular instrument each time, to evaluate if any one variant is driving the analysis (8).”
Genetics of 35 blood and urine biomarkers in the UK Biobank (2021, neutral): “All MR calculations were done using TwoSampleMR, which was also used to perform trait munging 85 .”
Statistical inference in two-sample summary-data Mendelian randomization using robust adjusted profile score (2018, methodology): “, as implemented in the TwoSampleMR R package [28]).”
Insulin resistance: Genetic associations with depression and cognition in population based cohorts (2019, neutral): “For fasting glucose, fasting insulin, triglycerides, HDL, LDL, and total cholesterol we used significant GWAS hits available in the MR-Base GWAS catalogue (Hemani et al., 2018) (detailed in Supplementary Table S2).”
The genetic architecture of the human cerebral cortex (2020, methodology): “To determine whether these correlations were causal, we used MR (59) and LCV analyses (19).”
Mapping the human genetic architecture of COVID-19 (2021, neutral): “Differences between the observed genetic correlations of SARS-CoV-2 infection and COVID-19 severity were compared using a z score method 71 .”
Two-Sample Instrumental Variable Analyses Using Heterogeneous Samples (2017, background): “Another approach is to assume all the effect modifiers in the exposure- and outcome-equations are observed (Hernán and Robins, 2006, Wang and Tchetgen Tchetgen, 2018).”