Software application profile: mrrobust—a tool for performing two-sample summary Mendelian randomization analyses
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Abstract
Motivation: In recent years, Mendelian randomization analysis using summary data from genome-wide association studies has become a popular approach for investigating causal relationships in epidemiology. The mrrobust Stata package implements several of the recently developed methods. Implementation: mrrobust is freely available as a Stata package. General features: The package includes inverse variance weighted estimation, as well as a range of median, modal and MR-Egger estimation methods. Using mrrobust, plots can be constructed visualizing each estimate either individually or simultaneously. The package also provides statistics such as IGX2, which are useful in assessing attenuation bias in causal estimates. Availability: The software is freely available from GitHub [https://raw.github.com/remlapmot/mrrobust/master/].
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MR GxE and MR GENIUS have extended and formalised concepts of using gene-environment interactions and enable the estimation of causal effects in more general settings.
“Two methods, MR GxE and MR GENIUS, have extended and formalised these concepts and enable the estimation of causal effects in more general settings.”
From Mendelian randomization (2022) · cites this paper as [76]
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- Authors
- Spiller, W., Davies, N. M., & Palmer, T. M.
- Affiliations
- University of Bristol (GB)
- Lancaster University (GB)
- Corresponding author
- Spiller, Wes
- Venue
- International Journal of Epidemiology
- Publisher
- Oxford University Press (OUP)
- Published
- 2018-09-12
- Volume
- 48
- Issue
- 3
- Pages
- 684-690
- ISSN
- 0300-5771 (ISSN-L)
- 1464-3685
- PMC
- PMC6659377
- OpenAlex
- W2952494466
- Cited by
- 116
- References
- 29
- Citations by year
- 2026: 4 · 2025: 5 · 2024: 10 · 2023: 10 · 2022: 17 · 2021: 29 · 2020: 18 · 2019: 16 · 2018: 6 · 2017: 1
- FWCI
- 10.55
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- International Journal of Epidemiology (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- Lancaster EPrints (Lancaster University) (submittedVersion)
- Bristol Research (University of Bristol) (submittedVersion, cc-by)
- Explore Bristol Research (submittedVersion, cc-by)
- Funders
- Medical Research Council
- MRC
- University of Bristol
- MRC Integrative Epidemiology Unit (MC UU 12013/1, MC UU 12013/9, MC UU 00011/1)
- Wellcome Trust (108902/B/15/Z)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Keywords
- Mendelian randomization, summary data, Stata package, median estimation, modal estimation, MR-Egger, attenuation bias, causal inference, genome-wide association study
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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