Using Mendelian randomisation to assess causality in observational studies

ObjectiveMendelian randomisation (MR) is a technique that aims to assess causal effects of exposures on disease outcomes. The paper aims to present the main assumptions that underlie MR, the statistical methods used to estimate causal effects and how to account for potential violations of the key as...

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Bibliographic Details
Published in:BMJ mental health 2019-05, Vol.22 (2), p.67-71
Main Authors: Pagoni, Panagiota, Dimou, Niki L, Murphy, Neil, Stergiakouli, Evie
Format: Article
Language:English
Subjects:
Bipolar disorder
Body Mass Index
Causality
Consortia
Epidemiology
Genomes
Genomics
Humans
Mendelian Randomization Analysis - methods
Mental disorders
Mental Disorders - etiology
Mental Disorders - genetics
Mental health
Methods
Obesity
Observational studies
Observational Studies as Topic - methods
Obsessive compulsive disorder
Post traumatic stress disorder
Psychiatry
Schizophrenia
Schizophrenia and psychotic disorders
Statistics in Practice
Systematic review
Working groups
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Summary:ObjectiveMendelian randomisation (MR) is a technique that aims to assess causal effects of exposures on disease outcomes. The paper aims to present the main assumptions that underlie MR, the statistical methods used to estimate causal effects and how to account for potential violations of the key assumptions.MethodsWe discuss the key assumptions that should be satisfied in an MR setting. We list the statistical methodologies used in two-sample MR when summary data are available to estimate causal effects (ie, Wald ratio estimator, inverse-variance weighted and maximum likelihood method) and identify/adjust for potential violations of MR assumptions (ie, MR-Egger regression and weighted Median approach). We also present statistical methods and graphical tools used to evaluate the presence of heterogeneity.ResultsWe use as an illustrative example of a published two-sample MR study, investigating the causal association of body mass index with three psychiatric disorders (ie, bipolar disorder, schizophrenia and major depressive disorder). We highlight the importance of assessing the results of all available methods rather than each method alone. We also demonstrate the impact of heterogeneity in the estimation of the causal effects.ConclusionsMR is a useful tool to assess causality of risk factors in medical research. Assessment of the key assumptions underlying MR is crucial for a valid interpretation of the results.
ISSN:1362-0347
1468-960X
2755-9734
DOI:10.1136/ebmental-2019-300085