Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors
Open accessAbstract
Finding individual-level data for adequately-powered Mendelian randomization analyses may be problematic. As publicly-available summarized data on genetic associations with disease outcomes from large consortia are becoming more abundant, use of published data is an attractive analysis strategy for obtaining precise estimates of the causal effects of risk factors on outcomes. We detail the necessary steps for conducting Mendelian randomization investigations using published data, and present novel statistical methods for combining data on the associations of multiple (correlated or uncorrelated) genetic variants with the risk factor and outcome into a single causal effect estimate. A two-sample analysis strategy may be employed, in which evidence on the gene-risk factor and gene-outcome associations are taken from different data sources. These approaches allow the efficient identification of risk factors that are suitable targets for clinical intervention from published data, although the ability to assess the assumptions necessary for causal inference is diminished. Methods and guidance are illustrated using the example of the causal effect of serum calcium levels on fasting glucose concentrations. The estimated causal effect of a 1 standard deviation (0.13 mmol/L) increase in calcium levels on fasting glucose (mM) using a single lead variant from the CASR gene region is 0.044 (95 % credible interval -0.002, 0.100). In contrast, using our method to account for the correlation between variants, the corresponding estimate using 17 genetic variants is 0.022 (95 % credible interval 0.009, 0.035), a more clearly positive causal effect.
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The inclusion of variants in high linkage disequilibrium might not contribute additional information in estimating the causal effect, and could even lead to biased estimates of standard errors if this correlation structure is not accounted for.
“The inclusion of variants in high linkage disequilibrium might not contribute additional information in estimating the causal effect, and could even lead to biased estimates of standard errors if this correlation structure is not accounted for.47”
From Strengthening the Reporting of Observational Studies in Epidemiology using Mendelian Randomisation (STROBE-MR): explanation and elaboration. (2021) · cites this paper as [47]
The main advantage of using summary data from GWAS consortia in two-sample Mendelian randomization is increased statistical power, particularly for testing effects on binary disease outcomes.
“The main advantage of using summary data from GWAS consortia in two-sample MR is the increased statistical power, particularly in relation to testing effects on binary disease outcomes.”
From Commentary: Two-sample Mendelian randomization: opportunities and challenges (2016) · cites this paper as [4]
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- Authors
- EPIC- InterAct Consortium, Burgess, S., Scott, R. A., Timpson, N. J., Davey Smith, G., & Thompson, S. G.
- Affiliations
- University of Cambridge (GB)
- University of Bristol (GB)
- Corresponding author
- Burgess, Stephen
- Venue
- European Journal of Epidemiology
- Published
- 2015-03-15
- Volume
- 30
- Issue
- 7
- Pages
- 543-552
- ISSN
- 0393-2990 (ISSN-L)
- 1573-7284
- PMC
- PMC4516908
- OpenAlex
- W2118743532
- Cited by
- 1,803
- References
- 37
- Citations by year
- 2026: 180 · 2025: 400 · 2024: 504 · 2023: 166 · 2022: 136 · 2021: 126 · 2020: 66 · 2019: 67 · 2018: 61 · 2017: 48 · 2016: 37 · 2015: 10 · 2014: 1
- FWCI
- 35.53
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- European Journal of Epidemiology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Apollo (University of Cambridge) (submittedVersion, cc-by)
- Explore Bristol Research (submittedVersion)
- Funders
- Wellcome Trust (100114)
- National Institute for Health and Care Research (NF-SI-0512-10165, NF-SI-0512-10135)
- British Heart Foundation (SP/08/007/23628, G0800270, CH/12/2/29428, RG/08/014/24067)
- Medical Research Council (MR/L003120/1, G0800270, MC_UU_12015/1)
- 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, two-sample Mendelian randomization, genetic association data, causal risk factors, serum calcium, fasting blood glucose, CASR gene
- MeSH terms
- Data Interpretation, Statistical, Humans, Random Allocation, Risk Factors, Sensitivity and Specificity, Genetic Variation, Genetic Predisposition to Disease, Mendelian Randomization Analysis
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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