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Review
. 2017 Oct;14(10):577-590.
doi: 10.1038/nrcardio.2017.78. Epub 2017 Jun 1.

Mendelian randomization in cardiometabolic disease: challenges in evaluating causality

Michael V Holmes  1   2   3   4 , Mika Ala-Korpela  4   5   6 , George Davey Smith  4   6
Affiliations
Review

Mendelian randomization in cardiometabolic disease: challenges in evaluating causality

Michael V Holmes et al. Nat Rev Cardiol. 2017 Oct.

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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Conflict of interest statement

Competing interests statement

The authors declare no competing interests.

Figures

None
Each diamond represents a single SNP plotted so that the SNP to exposure estimate is on the x-axis and the SNP to outcome estimate is on the y-axis. Filled diamonds = non-pleiotropic variants and open diamonds = pleiotropic variants. In MR using summary level data, the regression slope provides an estimation of the causal effect of the exposure on the outcome.
None
Each diamond represents a single SNP plotted so that the SNP to exposure estimate is on the x-axis and the SNP to outcome estimate is on the y-axis. Filled diamonds = non-pleiotropic variants and open diamonds = pleiotropic variants. In MR using summary level data, the regression slope provides an estimation of the causal effect of the exposure on the outcome.
Figure 1
Figure 1. Instrumental variable analysis to generate causal estimates through Mendelian randomization.
The three principles of instrumental variable analysis are: the instrumental variable (in this case a genetic variant either in isolation or in combination with other variants) must associate with the exposure; the instrumental variable must not associate with confounders that are either known or unknown (U); and there is no pathway from the single nucleotide polymorphism (SNP) to disease that does not include the exposure of interest. This figure is a schematic representation and should not be interpreted as a formal directed acyclic graph.
Figure 2
Figure 2. Paradoxical scenarios in Mendelian randomization.
a | An example of MR using a pleiotropic variant. The genetic variant associates with multiple biomarkers on separate biological pathways. Generating separate causal estimates for biomarkers 1 and 2 is invalid as they ascribe the same single nucleotide polymorphism (SNP)–disease effect to each biomarker. Furthermore, if only one of the biomarkers is causal, then using the SNP to make causal inferences on the non-causal biomarker can generate an erroneous conclusion. b | An example of MR using a variant that disrupts normal function of the exposure. Possession of the genetic variant might lead to increased concentration of the exposure (for example, owing to impaired clearance), but paradoxically lead to an increased risk of disease (if the normal function of the biomarker would be protective of disease), or vice versa if the normal function of the biomarker increases risk of the disease. c | An example of MR involving biomarkers on the same pathway, in which the genetic variant encodes an enzyme that metabolizes a substrate into a metabolite. If the substrate and metabolite have contrasting roles in the development of diseases, this discrepancy might lead to complexity in the interpretation of findings. d | An example of MR involving a time-dependent exposure. If the biomarker is causal for disease only during a critical time period, MR might show evidence of a protective effect. However, intervening on the biomarker during the noncritical time period will not alter risk of disease. e | An example of MR of a cumulative exposure, in which the exposure is causal for disease, but has a long latency. For example, the disease might typically present after decades of exposure-induced subclinical disease development. f | An example of MR involving overlapping traits. MR of overlapping biomarkers can lead to paradoxical findings as the overlapping nature of the traits might lead to a diminution of their apparent causal effect on multivariate analyses. These figures are schematic representations and should not be interpreted as formal directed acyclic graphs. U, unknown.
Figure 3
Figure 3. Mendelian randomization using a genetic variant that associates with multiple biomarkers on separate pathways.
a | Using single nucleotide polymorphisms (SNPs) in PPM1K (which encodes a mitochondrial phosphatase that activates branched-chain α-keto acid dehydrogenase [BCKD], responsible for the rate-limiting step of metabolism of the branched-chain amino acids) to infer causality of three separate amino acids yields an erroneous conclusion, as this inference ascribes a causal estimate to each amino acid from the same PPM1K– diabetes association that is scaled to the PPM1K–amino acid estimate (TABLE 2). The three amino acids are initially catabolized prior to the enzymatic action of BCKD. b | Using SNPs in APOE to infer causality of C-reactive protein (CRP) yields an erroneous conclusion, as the SNP is pleiotropic for CRP and LDL cholesterol (LDL-C). These figures are schematic representations and should not be interpreted as formal directed acyclic graphs. CHD, coronary heart disease.
Figure 4
Figure 4. Mendelian randomization using a variant that disrupts normal function of the exposure.
a | Reduced hepatic uptake of HDL particles through the scavenger receptors leads to the accumulation of circulating HDL cholesterol (HDL-C) and increased risk of coronary heart disease (CHD). However, this observation does not indicate that HDL-C is harmful, but provides some support for the notion that appropriate function of reverse cholesterol transport might be beneficial to cardiovascular health. b | A variant in IL6R leads to reduced membrane-bound IL-6, which in turn results in increased levels of circulating IL-6, disruption of classical IL-6 signalling, reduced C-reactive protein (CRP) levels, and a reduction in risk of CHD. These figures are schematic representations and should not be interpreted as formal directed acyclic graphs.
Figure 5
Figure 5. Mendelian randomization of biomarkers on the same pathway.
a | Individuals who are homozygous for the ALDH2*1 variant can consume normal amounts of alcohol without symptoms of flushing and nausea, which can lead to increased alcohol intake and subsequent high blood pressure. However, because acetaldehyde is efficiently cleared by aldehyde dehydrogenase (ALDH2), the risk of oesophageal cancer is low. b | Individuals who are heterozygous for ALDH2*2 are likely to consume lower amounts of alcohol than those who are homozygous for the ALDH2*1 variant, given their symptoms of moderate flushing. This lower alcohol consumption leads to lower blood pressure. Reduced functioning of ALDH2 leads to increased acetaldehyde levels, which in turn results in increased risk of oesophageal cancer. c | Individuals homozygous for ALDH2*2 consume almost no alcohol, given the severe symptoms and, therefore, blood pressure levels are expected to be lower than in those who are heterozygous for ALDH2*2 or homozygous for the ALDH2*1 variant. Similarly, acetaldehyde levels in individuals homozygous for ALDH2*2 are also expected to be lower than in individuals heterozygous for the ALDH2*2 variant, resulting in lower risk of oesophageal cancer (and similar to or lower than carriers of ALDH2*1*1, depending on alcohol consumption). These figures are schematic representations and should not be interpreted as formal directed acyclic graphs.
Figure 6
Figure 6. Mendelian randomization of a time dependent and cumulative exposure.
a | The genetic variant-to-disease (multiple sclerosis) association reflects lifetime associations (including causal effects mediated through vitamin D that only occur during adolescence). Therefore, Mendelian randomization might provide evidence of a causal effect when this effect actually only occurs during a critical time period. b | The genetic variant alters the heparin-binding domain of extracellular superoxide dismutase (ecSOD), meaning that it cannot bind to the external membrane of endothelial cells, and cannot prevent nitric oxide (NO) from being degraded by superoxide anions. Less NO results in vasoconstriction and increased risk of coronary heart disease (CHD). c | Genetic variants instrumenting LDL cholesterol (LDL-C) have large effects on risk of CHD. Given that CHD is a disease that develops over decades, the effect estimates are equivalent to the estimates that would be derived from lifelong lowering of LDL-cholesterol levels. These figures are schematic representations and should not be interpreted as formal directed acyclic graphs.
Figure 7
Figure 7. Mendelian randomization of overlapping exposures.
As triglycerides (TGs) overlap with non-HDL cholesterol (non-HDL-C), adjusting the association of TGs with risk of coronary heart disease (CHD) for non-HDL-C diminishes the causal effect of TGs to null. By contrast, non-HDL-C contains the entire cascade of apolipoprotein B-containing lipoproteins, including intermediate-density lipoprotein cholesterol and LDL cholesterol, meaning that an association persists between non-HDL-C and CHD on adjustment for TGs. The attenuation of the TG–CHD association does not provide any information about the causality of TG, because it adjusts for an overlapping trait. This figure is a schematic representation and should not be interpreted as a formal directed acyclic graph.
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