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  • Review Article
  • Published:

Mendelian randomization in cardiometabolic disease: challenges in evaluating causality

Nature Reviews Cardiology volume 14, pages 577–590 (2017)Cite this article

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Key Points

  • Mendelian randomization (MR) is a powerful tool that utilizes genetic information to inform about the likely causal relevance of an exposure to an outcome

  • When performed rigorously, MR findings should be free from reverse causality bias, and only minimally affected by confounding

  • The number of MR studies has been increasing in the past decade, providing important new insights into disease aetiology

  • However, as MR studies become more common, and as increasingly complex gene-to-exposure and exposure-to-outcome relationships are investigated, reliable conduct and interpretation of MR analyses can be challenging

  • Potential solutions to aid the conduct and interpretation of MR studies can be derived, for example, through use of emerging statistical approaches to investigate potential genetic pleiotropy that can distort the findings

Abstract

Mendelian randomization (MR) is a burgeoning field that involves the use of genetic variants to assess causal relationships between exposures and outcomes. MR studies can be straightforward; for example, genetic variants within or near the encoding locus that is associated with protein concentrations can help to assess their causal role in disease. However, a more complex relationship between the genetic variants and an exposure can make findings from MR more difficult to interpret. In this Review, we describe some of these challenges in interpreting MR analyses, including those from studies using genetic variants to assess causality of multiple traits (such as branched-chain amino acids and risk of diabetes mellitus); studies describing pleiotropic variants (for example, C-reactive protein and its contribution to coronary heart disease); and those investigating variants that disrupt normal function of an exposure (for example, HDL cholesterol or IL-6 and coronary heart disease). Furthermore, MR studies on variants that encode enzymes responsible for the metabolism of an exposure (such as alcohol) are discussed, in addition to those assessing the effects of variants on time-dependent exposures (extracellular superoxide dismutase), cumulative exposures (LDL cholesterol), and overlapping exposures (triglycerides and non-HDL cholesterol). We elaborate on the molecular features of each relationship, and provide explanations for the likely causal associations. In doing so, we hope to contribute towards more reliable evaluations of MR findings.

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Figure 1: Instrumental variable analysis to generate causal estimates through Mendelian randomization.
Figure 2: Paradoxical scenarios in Mendelian randomization (MR).
Figure 3: Mendelian randomization (MR) using a genetic variant that associates with multiple biomarkers on separate pathways.
Figure 4: Mendelian randomization (MR) using a variant that disrupts normal function of the exposure.
Figure 5: Mendelian randomization of biomarkers on the same pathway.
Figure 6: Mendelian randomization of a time-dependent and cumulative exposure.
Figure 7: Mendelian randomization of overlapping exposures.

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Acknowledgements

M.A.-K. is supported by Biocenter Oulu and Sigrid Juselius Foundation, Finland. M.A.-K. and G.D.S. work in a unit that receives funding from the University of Bristol and UK Medical Research Council (MC_UU_12013/1). M.V.H. works in a unit that receives funds from the University of Oxford and the UK Medical Research Council. These funding bodies did not have a role in the study design, decision to publish, or preparation of the manuscript.

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Authors and Affiliations

  1. Medical Research Council Population Health Research Unit, University of Oxford, Roosevelt Drive, Oxford, OX3 7LF, UK

    Michael V. Holmes

  2. Nuffield Department of Population Health, Clinical Trial Service Unit & Epidemiological Studies Unit, University of Oxford, Big Data Institute Building, Old Road Campus, Roosevelt Drive, Oxford, OX3 7BN, UK

    Michael V. Holmes

  3. National Institute for Health Research, Oxford Biomedical Research Centre, Oxford University Hospital, Old Road, Oxford, OX3 7LE, UK

    Michael V. Holmes

  4. Medical Research Council Integrative Epidemiology Unit, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK

    Michael V. Holmes, Mika Ala-Korpela & George Davey Smith

  5. Computational Medicine, Faculty of Medicine, University of Oulu and Biocenter Oulu, University of Oulu, Aapistie 5A, Oulu, 90014, Finland

    Mika Ala-Korpela

  6. School of Social and Community Medicine, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK

    Mika Ala-Korpela & George Davey Smith

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All authors researched data for the article, and made substantial contributions to discussion of content, writing, reviewing, and editing the manuscript before submission.

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PowerPoint slides

Glossary

Randomized, controlled trials (RCTs)

An interventional study in which individuals are randomized to an 'exposed' arm (for example, an active drug) or to a comparator control arm (for example, placebo) to test the efficacy of an intervention on an outcome (such as disease risk). Such trials are considered the gold standard for asserting causal relationships of an exposure to disease risk.

Mendelian randomization

A genetic epidemiological approach that aims to quantify the causal relationship between an exposure and an outcome, by using the properties of the genome (randomized owing to Mendel's second law, and invariant) to minimize the issues of confounding and reverse causality that can undermine traditional observational epidemiology.

Vertical pleiotropy

The association of a genetic marker (for example, a single nucleotide polymorphism [SNP] in isolation, or a genetic instrument consisting of multiple SNPs) with more than one phenotype on the same biological pathway.

Horizontal pleiotropy

The association of a genetic marker with more than one phenotype on discrete biological pathways.

Genome-wide association study (GWAS)

The hypothesis-free investigation of hundreds of thousands to millions of genetic variants (typically single nucleotide polymorphisms) for their association with a phenotype to characterize the underlying genetic architecture of the phenotype.

Linkage disequilibrium

The nonrandom assortment of genetic variants, meaning that when linkage disequilibrium between a pair of variants is high (for example, as measured by a r2 value of >0.80), a single nucleotide polymorphism (SNP) can be used as a 'proxy' for another SNP in the absence of this second SNP being directly genotyped.

Unbalanced horizontal pleiotropy

When horizontal pleiotropy is such that alternative pathways from the genetic marker to disease can lead to distortion of the association of the exposure under investigation. Unbalanced horizontal pleiotrophy is a violation of the exclusion restriction assumption of an instrumental variable.

Instrumental variable

A variable used as a proxy for an exposure of interest that is not associated with confounders and only associates with an outcome through the exposure of interest

Friedewald equation

The estimation of LDL-cholesterol levels in the absence of its direct measurement, from total cholesterol, high-density lipoprotein cholesterol, and triglycerides

Two-sample Mendelian randomization

The single nucleotide polymorphism (SNP)-to-exposure estimate is obtained from a separate dataset to that of the SNP-to-outcome estimate.

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Holmes, M., Ala-Korpela, M. & Smith, G. Mendelian randomization in cardiometabolic disease: challenges in evaluating causality. Nat Rev Cardiol 14, 577–590 (2017). https://doi.org/10.1038/nrcardio.2017.78

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