Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2017 Sep;103(18):1400-1407.
doi: 10.1136/heartjnl-2016-310605. Epub 2017 Jun 8.

Mendelian randomisation in cardiovascular research: an introduction for clinicians

Derrick A Bennett  1 , Michael V Holmes  1   2   3
Affiliations
Review

Mendelian randomisation in cardiovascular research: an introduction for clinicians

Derrick A Bennett et al. Heart. 2017 Sep.

Abstract

Understanding the causal role of biomarkers in cardiovascular and other diseases is crucial in order to find effective approaches (including pharmacological therapies) for disease treatment and prevention. Classical observational studies provide naïve estimates of the likely role of biomarkers in disease development; however, such studies are prone to bias. This has direct relevance for drug development as if drug targets track to non-causal biomarkers, this can lead to expensive failure of these drugs in phase III randomised controlled trials. In an effort to provide a more reliable indication of the likely causal role of a biomarker in the development of disease, Mendelian randomisation studies are increasingly used, and this is facilitated by the availability of large-scale genetic data. We conducted a narrative review in order to provide a description of the utility of Mendelian randomisation for clinicians engaged in cardiovascular research. We describe the rationale and provide a basic description of the methods and potential limitations of Mendelian randomisation. We give examples from the literature where Mendelian randomisation has provided pivotal information for drug discovery including predicting efficacy, informing on target-mediated adverse effects and providing potential new evidence for drug repurposing. The variety of the examples presented illustrates the importance of Mendelian randomisation in order to prioritise drug targets for cardiovascular research.

Keywords: Epidemiology; Genetics; Study design.

PubMed Disclaimer

Conflict of interest statement

Competing interests: None declared.

Figures

Figure 1
Figure 1
Examples of (A) confounding and (B) reverse causality in observational epidemiology. (A) The arrows denote the direction of proposed causality and the cross denotes that the postulated direct link between yellow teeth and lung cancer is false. (B) The arrows denote the direction of proposed causality and the cross denotes that the postulated direct link between C-reactive protein (CRP) and CHD is false and in fact the current evidence suggests that CHD raises levels of CRP (ie, the arrow goes in the opposite direction).
Figure 2
Figure 2
Example of regression dilution bias in observational epidemiology. The sizes of the boxes are inversely proportional to the amount of statistical information. The HRs are plotted on a natural logarithmic (doubling scale). The black boxes (and the black dotted line) show the association between mismeasured systolic blood pressure and CHD; the red boxes (and the red dotted line) shows the association between systolic blood pressure and CHD if systolic blood pressure was measured without error. This illustrates that the slope of the association is underestimated when an exposure that is subject to random measurement error is related to a disease outcome. SBP, systolic blood pressure.
Figure 3
Figure 3
Comparison of a conventional trial with a Mendelian randomisation study. This illustrates the analogy between a conventional randomised controlled trial and a Mendelian randomisation study. CV, cardiovascular.
Figure 4
Figure 4
Mendelian randomisation to test causality of a biomarker in disease: applied to LDL-cholesterol and risk of CHD. This example uses a genetic variant to estimate the causal relevance of LDL-C in CHD. Although for simplicity we use a single genetic variant, for a non-protein trait such as LDL-C, Mendelian randomisation should ideally employ multiple genetic variants in combination identified from genome-wide association studies of LDL-C as this more accurately reflects the underlying genetic architecture of the trait and thus gives a more reliable estimate for causality.(1) Association of LDLR SNP rs6511720 with LDL-C based on a meta-analysis of 137 818 participants reported by Ference et al JACC (2012); 60 2631–2639.(2) Association of rs651170 with CHD based on a meta-analysis of 77 041 CHD cases reported by Ference et al JACC (2012); 60 2631–2639(3) The causal estimate of LDL-C with CHD is found by taking the exponential of scaled value based on GX and GY to obtain the OR and its associated 95% CI. For this example a 0.19 mmol/L lower LDL-C (GX) was associated with a log OR (GY) of −0.1393 (that corresponds to an OR of 0.87=exp[−0.1393]). The causal estimate is required for a 0.25 mmol/L lower LDL-C so this can be obtained by 0.25 × [−0.1393/0.19]=−0.1833 exp(−0.1833)=0.83. The SE and CI are more challenging to calculate and the details are contained in Burgess et al’s Statistical Methods in Medical Research. SNP, single-nucleotide polymorphism.
Figure 5
Figure 5
Example of (A) horizontal pleiotropy and (B) vertical pleiotropy in a Mendelian randomisation study. The arrows denote the direction of proposed causality. In scenario (A), whether CHD is a consequence of telomere length or whether the association is confounded by an association of the genetic variants with cancer chemotherapy (which itself has deleterious effects to the cardiovascular system) is not known. Thus, the potential independent association of genetic variants with cancer therapy could represent a horizontally pleiotropic pathway and thus give an invalid causal estimate for Mendelian randomisation. In scenario (B), the SNPs associated with BMI are also associated with systolic blood pressure; however, this simply reflects a downstream effect of BMI (as BMI is recognised to causally affect blood pressure) and is likely on the pathway between BMI and risk of CHD. Thus, while the potential presence of horizontal pleiotropy in scenario (A) makes it unclear whether telomere length plays a causal role in CHD, in scenario (B) the vertical pleiotropy is informative of potential mechanisms from exposure through to disease. BMI, body mass index; SBP, systolic blood pressure; SNP, single-nucleotide polymorphism.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Heart Protection Study Collaborative Group. MRC/BHF heart protection study of cholesterol lowering with simvastatin in 20-536 high-risk individuals: a randomised placebocontrolled trial. The Lancet 2002;360:7–22. - PubMed
    1. PROGRESS Collaborative Group. Randomised trial of a perindopril-based blood-pressure-lowering regimen among 6105 individuals with previous stroke or transient ischaemic attack. The Lancet 2001;358:1033–41. - PubMed
    1. Wang H, Naghavi M, Allen C, et al. . Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980–2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet 2016;388:1459–544.10.1016/S0140-6736(16)31012-1 - DOI - PMC - PubMed
    1. Kassebaum NJ, Arora M, Barber RM, et al. . Global, regional, and national disability-adjusted life-years (DALYs) for 315 diseases and injuries and healthy life expectancy (HALE), 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet 2016;388:1603–58.10.1016/S0140-6736(16)31460-X - DOI - PMC - PubMed
    1. Black N. Why we need observational studies to evaluate the effectiveness of health care. BMJ 1996;312:1215–8. - PMC - PubMed

MeSH terms

-