Mendelian randomisation studies of type 2 diabetes: future prospects
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KeywordsDiabetes Genetic association Mendelian randomisation
The integration of principles and procedures underlying population genetics and epidemiology provides a potential research framework to make causal inferences about the association between a risk factor and disease [1, 2]. In an epidemiological context, numerous biomarkers have been linked to the development of type 2 diabetes. In this issue of Diabetologia, Herder and colleagues assess the prospective association between circulating levels of macrophage migration inhibitory factor and risk of type 2 diabetes . Their results provide suggestive statistical evidence for a sex-specific association between this biomarker and risk of type 2 diabetes. This observation is consistent with the current paradigm that inflammation plays a role in the pathogenesis of insulin resistance and type 2 diabetes .
The association might be causal. Alternatively, given the observational nature of epidemiological research, it may also be completely explained by confounding. In this scenario, the relationship is explained by a third factor that is associated with the biomarker and disease risk. That is, the biomarker is not independently or causally linked to disease risk. Epidemiological approaches generally use multivariable statistical analysis to reduce confounding. However, because of residual confounding (as a result of measurement error) and unknown confounders, standard observational epidemiology cannot resolve whether an observed association has a causal basis. The conceptual ambiguity of any disease mechanism (specifically, defining mediators and confounders) also limits statistical modelling to reduce confounding. Furthermore, the observed association may be due to reverse association/causation—that is, the relationship between biomarker and disease could be the result of undiagnosed or early disease rather than the risk factor, and thus a consequence of disease rather than a cause.
Mendelian randomisation studies, like other genetic epidemiological studies, require reliable identification of statistical associations between genetic variants and biomarkers and disease risk . Importantly, the principal requirement is the ability of Mendelian randomisation studies to detect an association of equivalent magnitude to that predicted by a proportional change in the relevant trait or risk factor. Given that individual genetic variants are likely to explain only a very small proportion of the variation in a biomarker trait or risk factor, these assessments will require very large sample sizes, which are probably beyond the scale of existing studies or collaborations in type 2 diabetes genetics . This statistical limitation is compounded by random measurement error in the assessment of the magnitude of the associations among the genetic variant, biomarker and disease risk, which can lead to attenuated association signals. To help overcome issues of statistical resolution, investigators have opted to use meta-analysis . However, using this approach for Mendelian randomisation triangulation has several limitations, which are generic to this strategy . Heterogeneity among studies (resulting from, for example, potential gene–gene or gene–environment interactions) may distort assessments of the magnitude of the associations . This potential limitation may be exacerbated when different studies are used for data aggregation for each side of the Mendelian randomisation triangle (Fig. 1).
It is therefore likely that international collaborative frameworks for Mendelian randomisation studies of type 2 diabetes will be needed to achieve the required level of statistical precision. For example, reproducible statistical associations between genetic variants and risk of type 2 diabetes show an effect size of around 10–30% increased risk per allele . Under a log-additive model, around 15,000 cases and 15,000 controls are required to detect a per allele risk of 10% (with an α-level of 1 × 10−4 and 90% power) for a relatively common genetic variant (20% allele frequency). The association between genetic variants and type 2 diabetes risk may be even smaller for those variants which also show reproducible associations with relevant biomarkers . Thus, it is likely that Mendelian randomisation studies will need much greater statistical resolution. To facilitate this requirement, improved statistical approaches for meta-analysing data across studies are in development . However, data pooling strategies have distinct advantages in this context, allowing enhanced data harmonisation and statistical testing, including haplotype analysis and imputational methods to increase the comparability of markers across studies.
There are other caveats. Genetic confounding and biological compensation may also limit inferences in Mendelian randomisation studies. Biological compensation, also referred to as canalisation, is defined as a developmental and physiological adaptation to a genetic difference, and could lead to inconsistencies in the magnitude of the triangulated associations . This physiological adaptation, among other important differences, distinguishes Mendelian randomisation studies from randomised controlled trials, which are not susceptible to this limitation . Importantly, the pleiotropic nature (multiple biological effects) of some genetic variants may produce confounded associations between genetic variants and phenotypes. Undetected population stratification (confounding), where genetic differences and trait associations may result from underlying differences in ancestry, may also distort the magnitude of gene–biomarker and gene–disease associations. By contrast, correlation among genetic variants (linkage disequilibrium) can be used to examine associations among these variants, traits and diseases, without directly ascertaining functional variants. However, in the same context, correlation with pleiotropic genetic variants, for example, could distort associations.
Despite these limitations, Mendelian randomisation provides a potential research framework to assess causal links between biological and environmental phenotypes and disease risk. These studies, when correctly performed, will provide insights into aetiological mechanisms and causality, informing potential therapeutic and preventative strategies.
The authors would like to thank an anonymous reviewer for insightful comments on this manuscript.
Duality of interest
The authors declare that there is no duality of interest associated with this manuscript.
- 3.Herder C, Klopp N, Baumert J et al (2008) Effect of macrophage migration inhibitory factor (MIF) gene variants and MIF serum concentrations on the risk of type 2 diabetes: results from the MONICA/KORA Augsburg Case–Cohort Study, 1984–2002. Diabetologia DOI 10.1007/s00125-007-0800-3
- 5.Lawlor DA, Harbord RM, Sterne JA, Timpson N, Davey Smith G (2007) Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Stat Med DOI 10.1002/sim.3034