How humans can contribute to Mendelian randomization analyses.
Open accessAbstract
Mendelian randomization (MR) can be variously dated as 67,1 33,2 283 or 164 (amongst others) years old. It is clear that in recent times there has been an exponential increase in publications on MR, both theoretical and applied.5,6 In addition to papers focused on MR, MR analyses are increasingly seen in the set of follow-up analyses for a genome-wide association study (GWAS), together with the obligatory bioinformatic and functional evidence. The Internationl Journal of Epidemiology compiled a special issue on MR to accompany the second international MR conference in 2015.7 The present issue repeats this for the fourth international conference. The speed of progress in the field is reflected in both the content of the issues, and by the fact that the volume of material now exceeds the capacity of a single issue. Further compilations of MR papers will appear in subsequent issues. The initial extended exposition of MR in 2003 included examples of two-sample Mendelian randomization (MR),4 but the rise in popularity of the two-sample approach came with the development of methods for MR using publicly-available summary data.8–10 It is now increasingly easy to perform an MR analysis without personally collecting any data. This is not necessarily a negative point, as such analyses have several advantages: they are able to use large data resources for many disease outcomes, and they can be replicated by anyone having access to the same data. However, it is also increasingly easy to perform an MR analysis without any critical thought. The formula—conduct a genome-wide association study, take all genome-wide significant variants, perform a two-sample MR analysis—can certainly increase one’s publication count. But can such an analysis be considered a contribution to the scientific literature, as it could have been (and probably has been11) performed already by a machine in a large automated pipeline for large numbers of risk factors and outcomes? In this editorial, we discuss how subject-specific knowledge and methodological advances can elevate MR analyses from being simply the result of a recipe, and prevent papers being published that are essentially a compendium of numbers with little insight into causal mechanisms. MR investigations often use all available genetic variants that are associated with a given trait.12 While this is understandable from the perspective of power, as datasets increase in size and ever more associated variants are discovered, power is becoming less of a crucial factor. The law of diminishing returns means that additional variants will explain less and less variance in the risk factor of interest, and so the additional power from including these variants in the analysis is often not justified, particularly given what is becoming known about pleiotropy (discussed below). A potential advantage of using more and more variants is the potential to use more elaborate robust methods. However, whether it is better to use more variants and more complex methods, or a more curated set of variants and simpler methods, is still up for debate. For example, the recently proposed latent causal variable method presented the novel finding that body mass index (BMI) was not a clear cause of type 2 diabetes.13 Although a positive result was observed on exclusion of outliers from the analysis, this finding indicates that caution is needed in interpreting the results from such methods, and highlights the importance of data visualization. One approach that can potentially help is to use data on additional risk factors. Such data can be incorporated in many ways: to identify pleiotropic variants and exclude them from the analysis,14 to adjust for the potential effects of competing risk factors (such as in multivariable MR15,16), to identify genetic variants having a specific mechanism of association with the risk factor,17 or to find commonalities between clusters of variants having similar associations with the outcome.18 Such approaches are particularly important when there is heterogeneity between causal estimates based on different variants, and can help clarify why the heterogeneity occurs.19 Few MR analyses have a clean and unambiguous interpretation. Heterogeneity is not a complete barrier to causal inference, and provides an opportunity to understand why certain variants are associated (or more strongly associated) with the outcome than others. A related statistical approach to MR is colocalization20 (and the related HEIDI test21). This assesses whether the same variants are driving the genetic associations with the risk factor and with the outcome (suggesting a causal effect of the risk factor on the outcome), or whether there are different variants driving the two associations (suggesting that the association is spurious or pleiotropic). Colocalization is particularly important to test when the MR analysis is based on a single gene region, such as for a protein risk factor using cis-acting variants.22
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Childhood levels of vitamin D have been used as a negative control outcome for the effect of adulthood BMI; childhood BMI was shown to affect childhood vitamin D levels in a Mendelian randomization investigation, but adulthood BMI did not.
“For example, childhood levels of vitamin D have been used as a negative control outcome for the effect of adulthood BMI; childhood BMI was shown to affect childhood vitamin D levels in a Mendelian randomization investigation, but adulthood BMI did not 106 .”
From Guidelines for performing Mendelian randomization investigations (2020) · cites this paper as [106]
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- Authors
- Burgess, S., & Davey Smith, G.
- Affiliations
- University of Cambridge (GB)
- MRC Integrative Epidemiology Unit, School of Social and Community Medicine, Bristol, UK
- Corresponding author
- Burgess, Stephen
- Venue
- International Journal of Epidemiology
- Publisher
- Oxford University Press (OUP)
- Published
- 2019-06-01
- Volume
- 48
- Issue
- 3
- Pages
- 661-664
- ISSN
- 0300-5771 (ISSN-L)
- 1464-3685
- PMC
- PMC6739231
- OpenAlex
- W2963887614
- Cited by
- 20
- References
- 42
- Citations by year
- 2024: 1 · 2023: 2 · 2022: 2 · 2021: 6 · 2020: 7 · 2019: 2
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- International Journal of Epidemiology (publishedVersion, other-oa)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Bristol Research (University of Bristol) (submittedVersion)
- Funders
- Wellcome Trust (204623/Z/16/Z)
- National Institute for Health and Care Research (NF-SI-0617-10113)
- British Heart Foundation (RG/18/13/33946, RG/13/13/30194)
- Medical Research Council (MC_UU_00011/1)
- 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, causal inference, genetic variants, pleiotropy, causal mechanisms, research reproducibility, automated pipeline
- MeSH terms
- Humans, Genetic Predisposition to Disease, Genome-Wide Association Study, Mendelian Randomization Analysis
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