Research Article: Assessing causal relationships using genetic proxies for exposures: an introduction to Mendelian randomization

Date Published: November 03, 2017

Publisher: John Wiley and Sons Inc.

Author(s): Srinivasa Vittal Katikireddi, Michael J. Green, Amy E. Taylor, George Davey Smith, Marcus R. Munafò.


Studying the consequences of addictive behaviours is challenging, with understanding causal relationships from observational data being particularly difficult. For example, people who smoke or drink excessively are often systematically different from those who do not, are less likely to participate in research and may misreport their behaviours when they do. Furthermore, the direction of causation between an addictive behaviour and outcome may be unclear. Mendelian randomization (MR) offers potential solutions to these problems.

We describe MR’s principles and the criteria under which it is valid. We identify challenges and potential solutions in its application (illustrated using two applied examples) and describe methodological extensions in its application.

MR is subject to certain assumptions, and requires the availability of appropriate genetic data, large sample sizes and careful design and conduct. However, it has already been applied successfully to the addiction literature. The relationship between alcohol consumption (proxied by a variant in the ADH1B gene) and cardiovascular risk has been investigated, finding that alcohol consumption increases risk, with no evidence of a cardioprotective effect at moderate consumption levels. In addition, heaviness of smoking (proxied by a variant in the CHRNA5‐A3‐B4 gene cluster) and risk of depression and schizophrenia have been investigated, with no evidence of a causal effect of smoking on depression but some evidence of a causal effect on schizophrenia.

Mendelian randomization analyses are already producing robust evidence for addiction‐related practice and policy. As genetic variants associated with addictive behaviours are identified, the potential for Mendelian randomization analyses will grow. Methodological developments are also increasing its applicability.

Partial Text

Determining whether associations are causal is central to much addiction research but is challenging, with many observational associations unlikely to reflect causal relationships 1. Randomized controlled trials (RCTs), which support stronger causal inference, are not suited to all research questions—particularly as their external validity may be limited 2, 3, 4. Randomizing long‐term behaviours or environmental exposures in humans is unethical and impractical. Many causal questions, such as the long‐term consequences of consuming potentially harmful, addictive substances, cannot be answered with RCTs.

Traditional observational studies face three major threats to establishing whether or not an association is causal 12.

In MR, genetic variants are used as proxies for the exposure of interest, which helps to avoid some of the problems described above. MR is an example of instrumental variable (IV) analysis that has long been used by economists to study causal effects 21. Randomization in an experimental study might be considered the purest form of instrumental variable 22. An instrumental variable is a proxy for the exposure of interest. While the correlation between instrument and proxy does not need to be strong, a poor correlation can be problematic and is referred to as a weak instrument (discussed later). The instrument should be unrelated to confounders and should impact the outcome only through its effect on the exposure. In an unbiased RCT, allocation by randomization is associated with the exposure group and is independent of confounders, so that the only pathway between allocation group and the outcome is through the exposure (treatment). An example of instrumental variable analysis using observational data from the economics literature is the use of minimum legal drinking age within US states as an instrument to study the effect of youth drinking on health and social outcomes 23. Assumptions underpinning this approach include observed and unobserved state characteristics that influence youth drinking being uncorrelated with their minimum legal drinking age policy, and that associations between the policy and outcomes operate only via youth drinking. Both these assumptions can be questioned (e.g. the assumption of the instrument being independent of confounders may be invalid if states that reduced legal drinking ages earlier experienced greater alcohol‐related harm previously).

We now describe some of the most important challenges with MR and commonly used strategies for addressing them. At the outset, we note that the need to have genetic data available is an important potential limitation—if DNA has not already been collected it may not be feasible to do so.

As noted above, biological pleiotrophic effects (violating assumption 3) threaten the validity of MR. While there remain no definitive ways of addressing this, several approaches now exist to explore potential bias. A theory‐informed approach is to make potentially informative comparisons to check the plausibility of assumption 3. For example, looking throughout countries with differing cultural norms for alcohol consumption can help to establish whether biological pleiotropy exists. In East Asian countries women tend not to consume alcohol, so genetic variants related to alcohol consumption would be expected to be associated with alcohol‐related disease outcomes in men but not in women 60. Cho and colleagues confirmed this by fitting a statistical interaction between the genetic variant and sex when carrying out a MR analysis using a South Korean sample, thereby providing further evidence that alcohol is related causally to an adverse cardiovascular risk profile 61. Similarly, in Box 3 a lack of an association between the genetic variant and heart disease among non‐drinkers increases confidence that the association among drinkers is due to drinking. Knowledge about the biological function by which the genetic variant exerts an effect is very helpful, as it provides greater confidence that the effect is via the behaviour rather than another mechanism 62. Box 2 illustrates how biological understanding can inform assessments of whether condition 3 is met in the case of a genetic variant associated with smoking.

Analytical tools for MR research are developing and being refined rapidly. Bidirectional MR is an extension of the traditional design utilizing genetic markers for different but inter‐related outcomes to investigate the direction and magnitude of the causal effects. For example, the causal relationship between cannabis use and schizophrenia remains controversial. Gage and colleagues studied two sets of genetic variants, one related to cannabis initiation and one related to schizophrenia risk, to understand more clearly the direction of causation 67. They found that schizophrenia‐related genetic variants were related strongly to cannabis initiation while genetic variants linked to cannabis initiation were associated weakly with schizophrenia. The authors therefore concluded that ‘cannabis initiation increases the risk of schizophrenia, but the size of the causal effect is small’ and there is ‘stronger evidence that schizophrenia risk predicts cannabis initiation’. The use of multiple genetic instruments to investigate the direction of causality can be extended to investigate multiple mediating factors 68. In such ‘network’ MR, genetic instruments for each mediating factor to be investigated are required, and the genetic instruments must be independent of each other 69.

MR adds to the range of study designs available to understand the causal effects of behaviours on outcomes of interest. It helps address key limitations of traditional observational studies, including confounding and reverse causation, but selection bias could remain problematic. MR studies potentially allow researchers to produce more robust evidence on questions of immense relevance to policy and practice. They can provide strong evidence of causation, subject to necessary assumptions which benefit from an understanding of the underpinning biological processes. However, two of the three assumptions underpinning MR cannot be tested definitively. Furthermore, genetic variants known to be associated with behaviours of interest are required and genetic data from a large number of people, including those exhibiting the behaviour of interest, are needed. A range of other causal approaches to observational research are available, with differing underpinning assumptions; their use in combination can be particularly powerful 10, 73. We have provided a broad overview of the topic so that interested readers are able to read critically and interpret findings from MR studies. The use of genetic instruments for gaining causal understanding is already yielding important insights into addiction research and will probably advance the field substantially in the future.





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