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Mendelian randomization: how genetics is pushing the boundaries of epidemiology to identify new causes of heart disease.

Published on Jan 1, 2013in Canadian Journal of Cardiology5.592
· DOI :10.1016/j.cjca.2012.09.014
George Thanassoulis30
Estimated H-index: 30
(MUHC: McGill University Health Centre)
Abstract
Abstract The past 10 years have seen a remarkable revolution in the genetics of cardiovascular (CV) disease. Although much work remains to bring these discoveries to the bedside, genetics has opened up remarkable possibilities in understanding the causes of CV disease through a relatively novel study design known as "Mendelian randomization." Akin to a randomized trial, Mendelian randomization is a genetic study design that takes advantage of the "randomization" of genetic information at birth to evaluate a potential causal relationship between a genetically determined biomarker and an outcome. By providing evidence for causal relationships, Mendelian randomization can improve our understanding of fundamental mechanisms in human disease, potentially accelerate the identification of bona fide drug targets, and ultimately improve the care of patients with CV disease. This review describes the concept and design of Mendelian randomization genetic studies, discusses their strengths and weaknesses, and presents recent examples of Mendelian randomization studies in the CV literature that have helped clarify the causal role of selected biomarkers in CV medicine.
  • References (29)
  • Citations (13)
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References29
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#1Svati H. ShahH-Index: 49
#2William E. KrausH-Index: 79
Last. Christopher B. NewgardH-Index: 98
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Cardiovascular diseases (CVDs) are the leading cause of death worldwide, but their molecular etiology remains poorly understood, in part because they develop slowly as a result of a mixture of genetic and environmental factors. Given the complex nature of CVD, molecular profiling of processes more “proximal” to the disease than genetic markers may have great promise in revealing both “form” (novel biomarkers with clinical potential) and “function” (mechanisms of disease development) of CVD. CVD ...
10 CitationsSource
#1Svati H. ShahH-Index: 49
#2William E. KrausH-Index: 79
Last. Christopher B. NewgardH-Index: 98
view all 3 authors...
Cardiovascular diseases (CVDs) are the leading cause of death worldwide, but their molecular etiology remains poorly understood, in part because they develop slowly as a result of a mixture of genetic and environmental factors. Given the complex nature of CVD, molecular profiling of processes more “proximal” to the disease than genetic markers may have great promise in revealing both “form” (novel biomarkers with clinical potential) and “function” (mechanisms of disease development) of CVD. CVD ...
159 CitationsSource
#1Benjamin F. Voight (UPenn: University of Pennsylvania)H-Index: 74
#2Gina M. Peloso (BU: Boston University)H-Index: 43
Last. Sekar KathiresanH-Index: 106
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Methods We performed two mendelian randomisation analyses. First, we used as an instrument a single nucleotide polymorphism (SNP) in the endothelial lipase gene (LIPG Asn396Ser) and tested this SNP in 20 studies (20 913 myocardial infarction cases, 95 407 controls). Second, we used as an instrument a genetic score consisting of 14 common SNPs that exclusively associate with HDL cholesterol and tested this score in up to 12 482 cases of myocardial infarction and 41 331 controls. As a positive con...
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#1Tom M. Palmer (UoB: University of Bristol)H-Index: 31
#2Debbie A. Lawlor (UoB: University of Bristol)H-Index: 118
Last. Jonathan A C Sterne (UoB: University of Bristol)H-Index: 91
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Mendelian randomisation analyses use genetic variants as instrumental variables (IVs) to estimate causal effects of modifiable risk factors on disease outcomes. Genetic variants typically explain a small proportion of the variability in risk factors; hence Mendelian randomisation analyses can require large sample sizes. However, an increasing number of genetic variants have been found to be robustly associated with disease-related outcomes in genome-wide association studies. Use of multiple inst...
194 CitationsSource
#1Nadeem Sarwar (University of Cambridge)H-Index: 21
#2Adam S. Butterworth (University of Cambridge)H-Index: 46
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#2Daniel I. Swerdlow (UCL: University College London)H-Index: 17
#3Michael V. HolmesH-Index: 32
Last. Juan P. Casas (Lond: University of London)H-Index: 69
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Findings In 40 studies including up to 133 449 individuals, an IL6R SNP (rs7529229) marking a non-synonymous IL6R variant (rs8192284; p.Asp358Ala) was associated with increased circulating log interleukin-6 concentration (increase per allele 9·45%, 95% CI 8·34–10·57) as well as reduced C-reactive protein (decrease per allele 8·35%, 95% CI 7·31–9·38) and fi brinogen concentrations (decrease per allele 0·85%, 95% CI 0·60–1·10). This pattern of eff ects was consistent with IL6R blockade from infusi...
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#1Robert Clarke (Clinical Trial Service Unit)H-Index: 135
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Last. Richard Peto (Clinical Trial Service Unit)H-Index: 164
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Background: Moderately elevated blood levels of homocysteine are weakly correlated with coronary heart disease (CHD) risk, but causality remains uncertain. When folate levels are low, the TT genotype of the common C677T polymorphism (rs1801133) of the methylene tetrahydrofolate reductase gene (MTHFR) appreciably increases homocysteine levels, so ‘‘Mendelian randomization’’ studies using this variant as an instrumental variable could help test causality. Methods and Findings: Nineteen unpublished...
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#2Eugene Braunwald (Brigham and Women's Hospital)H-Index: 212
In this review of heart disease, Nabel and Braunwald focus on two themes — coronary artery disease and myocardial infarction — and explain how our understanding has evolved over the past two centuries. The authors consider therapies that have led to improved survival.
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Identifying interesting relationships between pairs of variables in large data sets is increasingly important. Here, we present a measure of dependence for two-variable relationships: the maximal information coefficient (MIC). MIC captures a wide range of associations both functional and not, and for functional relationships provides a score that roughly equals the coefficient of determination ( R 2 ) of the data relative to the regression function. MIC belongs to a larger class of maximal infor...
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#1John F. Peden (Wellcome Trust Centre for Human Genetics)H-Index: 42
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Coronary artery disease (CAD) is the leading cause of death worldwide. Affected individuals cluster in families in patterns that reflect the sharing of numerous susceptibility genes. Genome-wide and large-scale gene-centric genotyping studies that involve tens of thousands of cases and controls have now mapped common disease variants to 34 distinct loci. Some coronary disease common variants show allelic heterogeneity or copy number variation. Some of the loci include candidate genes that imply ...
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Cited By13
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Mendelian randomization implemented through instrumental variable analysis is frequently discussed in causality and recently the number of applications on real data is increasing. However, there are very few discussions to address modern biomedical questions such as the integration of large scale omics in causality. While in the age of large omics, we face several hundred or thousands of components with little knowledge about the underlying structures, the focus of the field is on small scales a...
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