scholarly journals Mendelian Randomization Analysis of Hemoglobin A1cas a Risk Factor for Coronary Artery Disease

Diabetes Care ◽  
2019 ◽  
Vol 42 (7) ◽  
pp. 1202-1208 ◽  
Author(s):  
Aaron Leong ◽  
Ji Chen ◽  
Eleanor Wheeler ◽  
Marie-France Hivert ◽  
Ching-Ti Liu ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Risk Factor ◽  
Coronary Artery ◽  
Mendelian Randomization ◽  
Mendelian Randomization Analysis ◽  
Artery Disease ◽  
Randomization Analysis

scholarly journals Mendelian randomization analysis to characterize causal association between coronary artery disease and COVID-19

2020 ◽  
Author(s):  
Lang Wu ◽  
Jingjing Zhu ◽  
Chong Wu
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Observational Studies ◽  
Mendelian Randomization ◽  
Causal Association ◽  
Mendelian Randomization Analysis ◽  
Log Odds ◽  
Individual Decisions ◽  
Artery Disease ◽  
Randomization Analysis

AbstractObservational studies have suggested that having coronary artery disease increases the risk of Coronavirus disease 2019 (COVID-19) susceptibility and severity, but it remains unclear if this association is causal. Inferring causation is critical to facilitate the development of appropriate policies and/or individual decisions to reduce the incidence and burden of COVID-19. We applied Two-sample Mendelian randomization analysis and found that genetically predicted CAD was significantly associated with higher risk of COVID-19: the odds ratio was 1.29 (95% confidence interval 1.11 to 1.49; P = 0.001) per unit higher log odds of having CAD.

scholarly journals Mendelian randomization analysis supports the causal role of dysglycaemia and diabetes in the risk of coronary artery disease

2015 ◽  
Vol 36 (23) ◽  
pp. 1454-1462 ◽  
Author(s):  
Stephanie Ross ◽  
Hertzel C. Gerstein ◽  
John Eikelboom ◽  
Sonia S. Anand ◽  
Salim Yusuf ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Mendelian Randomization ◽  
Causal Role ◽  
Mendelian Randomization Analysis ◽  
Artery Disease ◽  
Randomization Analysis

scholarly journals MR-Clust: Clustering of genetic variants in Mendelian randomization with similar causal estimates

2019 ◽  
Author(s):  
Christopher N Foley ◽  
Paul D W Kirk ◽  
Stephen Burgess
Keyword(s):  
Blood Pressure ◽  
Coronary Artery Disease ◽  
Risk Factor ◽  
Coronary Artery ◽  
Genetic Variants ◽  
Mendelian Randomization ◽  
Disease Risk ◽  
Causal Effect ◽  
Coronary Artery Disease Risk ◽  
Artery Disease

AbstractMotivationMendelian randomization is an epidemiological technique that uses genetic variants as instrumental variables to estimate the causal effect of a risk factor on an outcome. We consider a scenario in which causal estimates based on each variant in turn differ more strongly than expected by chance alone, but the variants can be divided into distinct clusters, such that all variants in the cluster have similar causal estimates. This scenario is likely to occur when there are several distinct causal mechanisms by which a risk factor influences an outcome with different magnitudes of causal effect. We have developed an algorithm MR-Clust that finds such clusters of variants, and so can identify variants that reflect distinct causal mechanisms. Two features of our clustering algorithm are that it accounts for uncertainty in the causal estimates, and it includes ‘null’ and ‘junk’ clusters, to provide protection against the detection of spurious clusters.ResultsOur algorithm correctly detected the number of clusters in a simulation analysis, outperforming the popular Mclust method. In an applied example considering the effect of blood pressure on coronary artery disease risk, the method detected four clusters of genetic variants. A hypothesis-free search suggested that variants in the cluster with a negative effect of blood pressure on coronary artery disease risk were more strongly related to trunk fat percentage and other adiposity measures than variants not in this cluster.Availability and ImplementationMR-Clust can be downloaded from https://github.com/cnfoley/[email protected] or [email protected] InformationSupplementary Material is included in the submission.

scholarly journals MR-Clust: clustering of genetic variants in Mendelian randomization with similar causal estimates

2020 ◽  
Author(s):  
Christopher N Foley ◽  
Amy M Mason ◽  
Paul D W Kirk ◽  
Stephen Burgess
Keyword(s):  
Blood Pressure ◽  
Coronary Artery Disease ◽  
Risk Factor ◽  
Coronary Artery ◽  
Genetic Variants ◽  
Mendelian Randomization ◽  
Disease Risk ◽  
Causal Effect ◽  
Coronary Artery Disease Risk ◽  
Artery Disease

Abstract Motivation Mendelian randomization is an epidemiological technique that uses genetic variants as instrumental variables to estimate the causal effect of a risk factor on an outcome. We consider a scenario in which causal estimates based on each variant in turn differ more strongly than expected by chance alone, but the variants can be divided into distinct clusters, such that all variants in the cluster have similar causal estimates. This scenario is likely to occur when there are several distinct causal mechanisms by which a risk factor influences an outcome with different magnitudes of causal effect. We have developed an algorithm MR-Clust that finds such clusters of variants, and so can identify variants that reflect distinct causal mechanisms. Two features of our clustering algorithm are that it accounts for differential uncertainty in the causal estimates, and it includes ‘null’ and ‘junk’ clusters, to provide protection against the detection of spurious clusters. Results Our algorithm correctly detected the number of clusters in a simulation analysis, outperforming methods that either do not account for uncertainty or do not include null and junk clusters. In an applied example considering the effect of blood pressure on coronary artery disease risk, the method detected four clusters of genetic variants. A post hoc hypothesis-generating search suggested that variants in the cluster with a negative effect of blood pressure on coronary artery disease risk were more strongly related to trunk fat percentage and other adiposity measures than variants not in this cluster. Availability and implementation MR-Clust can be downloaded from https://github.com/cnfoley/mrclust. Supplementary information Supplementary data are available at Bioinformatics online.

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