A cautionary note on the use of Mendelian randomization to infer causation in observational epidemiology

Mendelian randomization

Mendelian randomization (MR) is a term that applies to the use of genetic variation to address causal questions about how modifiable exposures influence different outcomes. The principles of MR are based on Mendel's laws of inheritance and instrumental variable estimation methods, which enable the inference of causal effects in the presence of unobserved confounding. In this Primer, we outline the principles of MR, the instrumental variable conditions underlying MR estimation and some of the methods used for estimation. We go on to discuss how the assumptions underlying an MR study can be assessed and give methods of estimation that are robust to certain violations of these assumptions. We give examples of a range of studies in which MR has been applied, the limitations of current methods of analysis and the outlook for MR in the future. The difference between the assumptions required for MR analysis and other forms of non-interventional epidemiological studies means that MR can be used as part of a triangulation across multiple sources of evidence for causal inference.

Using Mendelian randomization as the cornerstone for causal inference in epidemiology

Mendelian randomization (MR) is attracting considerable critical attention. This paper aimed to explore the characteristics of the publications of MR, to reach an insight in this field and prospect the future trend. A bibliometric analysis was performed to identify published MR-related research. The articles were selected from the Web of Science Core Collection database. Excel 2019, VOSviewer 1.6.9, and CiteSpace 5.7.R3 were used to analyze the information. A total of 1783 papers of MR were identified, and the first included literature appeared in 2003. A total of 2829 institutions from 72 countries participated in the relevant research, while the UK contributed to 852 articles and were in a leading position. The most productive institution was the University of Bristol, and Smith GD who has posted the most articles (n=202) was also from there. The Int J Epidemiol (100 publications, 6861 citations) was the most prolific and high citation journal. Related topics of frontiers will still focus on coronary heart disease, diabetes, cancer, psychiatric disorder, body mass index, and lifestyle factors. We summarized the publication information of MR-related literature from 2003 to 2020, including country and institution of origin, authors, and publication journal. We analyzed former research hotspots in the field of MR and predicted future areas of interest. Exposures and outcomes detected in this paper will be the hotspots and frontiers of research in the next few years.

Null association between serum 25-hydroxyvitamin D levels with allergic rhinitis, allergic sensitization and non-allergic rhinitis: A Mendelian randomization study

BACKGROUND

Previous observational studies have not found a conclusive association between serum 25-hydroxyvitamin D (25(OH)D) levels and allergic rhinitis (AR) or allergic sensitization (AS).

OBJECTIVE

To investigate a causal association between 25(OH)D levels with risk of AR and AS, using a two-sample Mendelian randomization (MR) approach.

METHODS

Seven single nucleotide polymorphisms (SNPs), previously shown to be associated with serum 25(OH)D levels, were identified as instrumental variables. The primary outcome was AR, and the secondary outcomes were AS and non-allergic rhinitis (NAR). The genome-wide association (GWA) summary statistics of the outcomes were obtained from two cohort studies (EAGLE Consortium and UK Biobank). An MR analysis with random-effects inverse-variance weighted method was performed as the primary analysis to estimate overall effect size (odds ratio [OR] and 95% confidence interval [CI]). Sensitivity analysis using weighted median method and MR-Egger regression method was conducted. A subgroup analysis based on 25(OH)D synthesis-related SNPs was further applied.

RESULTS

Serum 25(OH)D levels were not causally associated with risk of AR (OR: 0.960; 95% CI: 0.779-1.184), AS (OR: 1.059; 95% CI: 0.686 to 1.634) or NAR (OR: 0.937; 95% CI: 0.588-1.491). Subgroup analysis also showed null association between 25(OH)D synthesis-related SNPs and the outcomes. Sensitivity analyses yielded similar results.

CONCLUSIONS AND CLINICAL RELEVANCE

This MR study found no evidence supporting a causal association between serum 25(OH)D levels and risk of AR, AS and NAR in European-ancestry population. This argues against the previous postulation that vitamin D supplementation is effective in prevention of allergic diseases.

Within family Mendelian randomization studies

Mendelian randomization (MR) is increasingly used to make causal inferences in a wide range of fields, from drug development to etiologic studies. Causal inference in MR is possible because of the process of genetic inheritance from parents to offspring. Specifically, at gamete formation and conception, meiosis ensures random allocation to the offspring of one allele from each parent at each locus, and these are unrelated to most of the other inherited genetic variants. To date, most MR studies have used data from unrelated individuals. These studies assume that genotypes are independent of the environment across a sample of unrelated individuals, conditional on covariates. Here we describe potential sources of bias, such as transmission ratio distortion, selection bias, population stratification, dynastic effects and assortative mating that can induce spurious or biased SNP-phenotype associations. We explain how studies of related individuals such as sibling pairs or parent-offspring trios can be used to overcome some of these sources of bias, to provide potentially more reliable evidence regarding causal processes. The increasing availability of data from related individuals in large cohort studies presents an opportunity to both overcome some of these biases and also to evaluate familial environmental effects.

Diagnostics for Pleiotropy in Mendelian Randomization Studies: Global and Individual Tests for Direct Effects

Diagnosing pleiotropy is critical for assessing the validity of Mendelian randomization (MR) analyses. The popular MR-Egger method evaluates whether there is evidence of bias-generating pleiotropy among a set of candidate genetic instrumental variables. In this article, we propose a statistical method-global and individual tests for direct effects (GLIDE)-for systematically evaluating pleiotropy among the set of genetic variants (e.g., single nucleotide polymorphisms (SNPs)) used for MR. As a global test, simulation experiments suggest that GLIDE is nearly uniformly more powerful than the MR-Egger method. As a sensitivity analysis, GLIDE is capable of detecting outliers in individual variant-level pleiotropy, in order to obtain a refined set of genetic instrumental variables. We used GLIDE to analyze both body mass index and height for associations with colorectal cancer risk in data from the Genetics and Epidemiology of Colorectal Cancer Consortium and the Colon Cancer Family Registry (multiple studies). Among the body mass index-associated SNPs and the height-associated SNPs, several individual variants showed evidence of pleiotropy. Removal of these potentially pleiotropic SNPs resulted in attenuation of respective estimates of the causal effects. In summary, the proposed GLIDE method is useful for sensitivity analyses and improves the validity of MR.

Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator

Developments in genome-wide association studies and the increasing availability of summary genetic association data have made application of Mendelian randomization relatively straightforward. However, obtaining reliable results from a Mendelian randomization investigation remains problematic, as the conventional inverse-variance weighted method only gives consistent estimates if all of the genetic variants in the analysis are valid instrumental variables. We present a novel weighted median estimator for combining data on multiple genetic variants into a single causal estimate. This estimator is consistent even when up to 50% of the information comes from invalid instrumental variables. In a simulation analysis, it is shown to have better finite-sample Type 1 error rates than the inverse-variance weighted method, and is complementary to the recently proposed MR-Egger (Mendelian randomization-Egger) regression method. In analyses of the causal effects of low-density lipoprotein cholesterol and high-density lipoprotein cholesterol on coronary artery disease risk, the inverse-variance weighted method suggests a causal effect of both lipid fractions, whereas the weighted median and MR-Egger regression methods suggest a null effect of high-density lipoprotein cholesterol that corresponds with the experimental evidence. Both median-based and MR-Egger regression methods should be considered as sensitivity analyses for Mendelian randomization investigations with multiple genetic variants.

Beyond Mendelian randomization: how to interpret evidence of shared genetic predictors

OBJECTIVE

Mendelian randomization is a popular technique for assessing and estimating the causal effects of risk factors. If genetic variants which are instrumental variables for a risk factor are shown to be additionally associated with a disease outcome, then the risk factor is a cause of the disease. However, in many cases, the instrumental variable assumptions are not plausible, or are in doubt. In this paper, we provide a theoretical classification of scenarios in which a causal conclusion is justified or not justified, and discuss the interpretation of causal effect estimates.

RESULTS

A list of guidelines based on the 'Bradford Hill criteria' for judging the plausibility of a causal finding from an applied Mendelian randomization study is provided. We also give a framework for performing and interpreting investigations performed in the style of Mendelian randomization, but where the choice of genetic variants is statistically, rather than biologically motivated. Such analyses should not be assigned the same evidential weight as a Mendelian randomization investigation.

CONCLUSION

We discuss the role of such investigations (in the style of Mendelian randomization), and what they add to our understanding of potential causal mechanisms. If the genetic variants are selected solely according to statistical criteria, and the biological roles of genetic variants are not investigated, this may be little more than what can be learned from a well-designed classical observational study.

Testing concordance of instrumental variable effects in generalized linear models with application to Mendelian randomization

Instrumental variable regression is one way to overcome unmeasured confounding and estimate causal effect in observational studies. Built on structural mean models, there has been considerable work recently developed for consistent estimation of causal relative risk and causal odds ratio. Such models can sometimes suffer from identification issues for weak instruments. This hampered the applicability of Mendelian randomization analysis in genetic epidemiology. When there are multiple genetic variants available as instrumental variables, and causal effect is defined in a generalized linear model in the presence of unmeasured confounders, we propose to test concordance between instrumental variable effects on the intermediate exposure and instrumental variable effects on the disease outcome, as a means to test the causal effect. We show that a class of generalized least squares estimators provide valid and consistent tests of causality. For causal effect of a continuous exposure on a dichotomous outcome in logistic models, the proposed estimators are shown to be asymptotically conservative. When the disease outcome is rare, such estimators are consistent because of the log-linear approximation of the logistic function. Optimality of such estimators relative to the well-known two-stage least squares estimator and the double-logistic structural mean model is further discussed.

Methods for meta-analysis of individual participant data from Mendelian randomisation studies with binary outcomes

Mendelian randomisation is an epidemiological method for estimating causal associations from observational data by using genetic variants as instrumental variables. Typically the genetic variants explain only a small proportion of the variation in the risk factor of interest, and so large sample sizes are required, necessitating data from multiple sources. Meta-analysis based on individual patient data requires synthesis of studies which differ in many aspects. A proposed Bayesian framework is able to estimate a causal effect from each study, and combine these using a hierarchical model. The method is illustrated for data on C-reactive protein and coronary heart disease (CHD) from the C-reactive protein CHD Genetics Collaboration (CCGC). Studies from the CCGC differ in terms of the genetic variants measured, the study design (prospective or retrospective, population-based or case-control), whether C-reactive protein was measured, the time of C-reactive protein measurement (pre- or post-disease), and whether full or tabular data were shared. We show how these data can be combined in an efficient way to give a single estimate of causal association based on the totality of the data available. Compared to a two-stage analysis, the Bayesian method is able to incorporate data on 23% additional participants and 51% more events, leading to a 23-26% gain in efficiency.

Next generation analytic tools for large scale genetic epidemiology studies of complex diseases

Over the past several years, genome-wide association studies (GWAS) have succeeded in identifying hundreds of genetic markers associated with common diseases. However, most of these markers confer relatively small increments of risk and explain only a small proportion of familial clustering. To identify obstacles to future progress in genetic epidemiology research and provide recommendations to NIH for overcoming these barriers, the National Cancer Institute sponsored a workshop entitled "Next Generation Analytic Tools for Large-Scale Genetic Epidemiology Studies of Complex Diseases" on September 15-16, 2010. The goal of the workshop was to facilitate discussions on (1) statistical strategies and methods to efficiently identify genetic and environmental factors contributing to the risk of complex disease; and (2) how to develop, apply, and evaluate these strategies for the design, analysis, and interpretation of large-scale complex disease association studies in order to guide NIH in setting the future agenda in this area of research. The workshop was organized as a series of short presentations covering scientific (gene-gene and gene-environment interaction, complex phenotypes, and rare variants and next generation sequencing) and methodological (simulation modeling and computational resources and data management) topic areas. Specific needs to advance the field were identified during each session and are summarized.

Controlling Alcohol-Related Global Health Problems

Alcohol’s adverse public health impact includes disease, injury, violence, disability, social problems, psychiatric illness, drunk driving, drug use, unsafe sex, and premature death. Furthermore, alcohol is a confirmed human carcinogen. The International Agency for Research on Cancer concluded that alcohol causes cancer of the oral cavity, pharynx, larynx, esophagus, liver, colon—rectum, and breast. World Cancer Research Fund/American Institute for Cancer Research concluded that the evidence justifies recommending avoidance of consuming any alcohol, even in small quantities. Despite being responsible for 3.8% of global deaths (2 255 000 deaths) and 4.6% of global disability-adjusted life years in 2004, alcohol consumption is increasing rapidly in China and Asia. Contrary to the World Health Assembly’s call for global control action, Hong Kong has reduced wine and beer taxes to zero since 2008. An International Framework Convention on Alcohol Control is urgently needed. Increasing alcohol taxation and banning alcohol advertisement and promotion are among the most effective policies.

Mendelian randomization in the era of genomewide association studies

BACKGROUND

Observational epidemiology has been instrumental in identifying modifiable causes of common diseases, and, in turn, substantially impacting public health. Spurious associations in observational epidemiologic studies are most commonly caused by confounding due to social, behavioral, or environmental factors and can therefore be difficult to control. They may also be due to reverse causation-in which the phenotypic outcome subsequently influences an environmental exposure such that it is wrongly implicated in its pathogenesis-and selection bias. Randomized controlled trials are effective in dealing with the potential sources of error; however, their use is not always leveraged, for practical or ethical reasons.

CONTENT

An alternative method, mendelian randomization, entails the use of genetic variants as proxies for the environmental exposures under investigation. The power of mendelian randomization lies in its ability to avoid the often substantial confounding seen in conventional observational epidemiology. Underpinning mendelian randomization is the principle of the independent assortment of alleles during meiosis, which, importantly in this context, also implies that they are independent of those behavioral and environmental factors that confound epidemiologic studies. By selecting genetic variants that influence exposure patterns or are associated with an intermediate phenotype of the disease, one can effectively re-create a randomized comparison.

SUMMARY

In the past 4 years, genomewide association studies have yielded the first robust genetic associations with common diseases, which in turn should enable mendelian randomization to be even more informative, despite some limitations outlined in this review.

Usefulness of Mendelian randomization in observational epidemiology

Mendelian randomization refers to the random allocation of alleles at the time of gamete formation. In observational epidemiology, this refers to the use of genetic variants to estimate a causal effect between a modifiable risk factor and an outcome of interest. In this review, we recall the principles of a "Mendelian randomization" approach in observational epidemiology, which is based on the technique of instrumental variables; we provide simulations and an example based on real data to demonstrate its implications; we present the results of a systematic search on original articles having used this approach; and we discuss some limitations of this approach in view of what has been found so far.

Mendelian randomization in nutritional epidemiology

Nutritional epidemiology aims to identify dietary and lifestyle causes for human diseases. Causality inference in nutritional epidemiology is largely based on evidence from studies of observational design, and may be distorted by unmeasured or residual confounding and reverse causation. Mendelian randomization is a recently developed methodology that combines genetic and classical epidemiological analysis to infer causality for environmental exposures, based on the principle of Mendel's law of independent assortment. Mendelian randomization uses genetic variants as proxies for environmental exposures of interest. Associations derived from Mendelian randomization analysis are less likely to be affected by confounding and reverse causation. During the past 5 years, a body of studies examined the causal effects of diet/lifestyle factors and biomarkers on a variety of diseases. The Mendelian randomization approach also holds considerable promise in the study of intrauterine influences on offspring health outcomes. However, the application of Mendelian randomization in nutritional epidemiology has some limitations.

Mendelian randomisation and causal inference in observational epidemiology

Nuala Sheehan and colleagues describe how Mendelian randomization provides an alternative way of dealing with the problems of observational studies, especially confounding.

Relations of inflammatory biomarkers and common genetic variants with arterial stiffness and wave reflection

Inflammation causes vascular dysfunction and perpetuates proatherosclerotic processes. We hypothesized that a broad panel of inflammatory biomarkers and single nucleotide polymorphisms in inflammatory genes is associated with vascular stiffness. We assessed 12 circulating inflammatory biomarkers (C-reactive protein, fibrinogen, interleukin-6, intercellular adhesion molecule-1, lipoprotein-associated phospholipase-A2 [mass and activity], monocyte chemoattractant protein-1, myeloperoxidase, CD40 ligand, osteoprotegerin, P-selectin, and tumor necrosis factor receptor-II) in relation to tonometry variables (central pulse pressure, mean arterial pressure, forward pressure wave, reflected pressure wave, carotid-femoral pulse wave velocity, and augmentation index) measured in 2409 Framingham Heart Study participants (mean age: 60 years; 55% women; 13% ethnic/racial minorities). Single nucleotide polymorphisms (n=2195) in 240 inflammatory candidate genes were related to tonometry measures in 1036 white individuals. In multivariable analyses, biomarkers explained <1% of any tonometry measure variance. Applying backward elimination, markers related to tonometry (P<0.01) were as follows: tumor necrosis factor receptor-II (inversely) with mean arterial pressure; C-reactive protein (positively) and lipoprotein-associated phospholipase-A2 (inversely) with reflected pressure wave; and interleukin-6 and osteoprotegerin (positively) with carotid-femoral pulse wave velocity. In genetic association analyses, lowest P values (false discovery rate <0.50) were observed for rs10509561 (FAS), P=6.6x10(-5) for central pulse pressure and rs11559271 (ITGB2), P=1.1x10(-4) for mean arterial pressure. These data demonstrate that, in a community-based sample, circulating inflammatory markers tumor necrosis factor receptor-II (mean arterial pressure), C-reactive protein, lipoprotein-associated phospholipase-A2 activity (reflected pressure wave), interleukin-6, and osteoprotegerin (carotid-femoral pulse wave velocity) were significantly but modestly associated with measures of arterial stiffness and wave reflection. Additional studies are needed to determine whether variation in inflammatory marker genes is associated with tonometry measures.