Genetically determined atrial fibrillation and risk of stroke: a Mendelian randomization study

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
J Fill ◽  
A Fokina ◽  
G Klappacher

Abstract Background Based on observational evidence, atrial fibrillation is a well-established risk factor of stroke to be considered for antithrombotic treatment in presence of additional clinical conditions derived from multivariate risk models. Although biologically plausible, it however still is unknown whether this association is causal and confined to the embolic stroke subtype. Purpose Our objective was to explore whether genetically determined manifestation of atrial fibrillation was associated with stroke and its etiologic subtypes by conducting a 2-sample Mendelian randomization (MR) study on publicly available summary statistics from GWAS consortia. Methods Genetic instruments for atrial fibrillation were obtained from the AFGen Consortium comprising 17,931 cases and 115,142 controls. Their associations with stroke and stroke subtypes were evaluated in the MEGASTROKE genome-wide association study data set (67 162 cases; 454 450 controls) applying inverse variance–weighted meta-analysis, weighted-median analysis, Mendelian randomization–Egger regression, and multivariable Mendelian randomization. The dataset of Nielsen et al. comprising a total of 60,620 cases with atrial fibrillation and 970,216 controls of European ancestry from six contributing studies was used as an independent validation sample. Genetic instruments for atrial fibrillation were further tested for association with etiologically related traits by using publicly available genome-wide association study data. Results Genetic predisposition to atrial fibrillation was associated with higher risk of any stroke (beta coefficient [b] ± standard error [se] = 0.22±0.04; P=0.0001), any ischemic stroke (b ± se = 0.24±0.05; P=0.0003), and cardioembolic stroke (b ± se = 0.76±0.10; P<0.0001), but not with small-vessel stroke or large artery stroke, see figure. Analyses in the validation sample showed similar associations (any stroke: b ± se = 0.19±0.04; P<0.0001; any ischemic stroke: b ± se = 0.21±0.04; P<0.0001; cardioembolic stroke: b ± se = 0.82±0.13; P<0.0001). Genetically determined atrial fibrillation was further weakly associated with chronic kidney disease (b ± se = 0.10±0.04; P=0.0261), but not with coronary artery disease and myocardial infarction or any other available phenotype. Conclusions Genetic predisposition to atrial fibrillation is associated with higher risk of any stroke, mainly driven by the ischemic and cardioembolic subtypes. In contrast, large artery and small-vessel strokes did not exhibit a causal relationship with atrial fibrillation. Funding Acknowledgement Type of funding source: Public hospital(s). Main funding source(s): Medical University of Vienna, Austria

2018 ◽  
Vol 14 (5) ◽  
pp. e1006105 ◽  
Author(s):  
Aaditya V. Rangan ◽  
Caroline C. McGrouther ◽  
John Kelsoe ◽  
Nicholas Schork ◽  
Eli Stahl ◽  
...  

2016 ◽  
Vol 47 (5) ◽  
pp. 971-980 ◽  
Author(s):  
S. H. Gage ◽  
H. J. Jones ◽  
S. Burgess ◽  
J. Bowden ◽  
G. Davey Smith ◽  
...  

BackgroundObservational associations between cannabis and schizophrenia are well documented, but ascertaining causation is more challenging. We used Mendelian randomization (MR), utilizing publicly available data as a method for ascertaining causation from observational data.MethodWe performed bi-directional two-sample MR using summary-level genome-wide data from the International Cannabis Consortium (ICC) and the Psychiatric Genomics Consortium (PGC2). Single nucleotide polymorphisms (SNPs) associated with cannabis initiation (p < 10−5) and schizophrenia (p < 5 × 10−8) were combined using an inverse-variance-weighted fixed-effects approach. We also used height and education genome-wide association study data, representing negative and positive control analyses.ResultsThere was some evidence consistent with a causal effect of cannabis initiation on risk of schizophrenia [odds ratio (OR) 1.04 per doubling odds of cannabis initiation, 95% confidence interval (CI) 1.01–1.07, p = 0.019]. There was strong evidence consistent with a causal effect of schizophrenia risk on likelihood of cannabis initiation (OR 1.10 per doubling of the odds of schizophrenia, 95% CI 1.05–1.14, p = 2.64 × 10−5). Findings were as predicted for the negative control (height: OR 1.00, 95% CI 0.99–1.01, p = 0.90) but weaker than predicted for the positive control (years in education: OR 0.99, 95% CI 0.97–1.00, p = 0.066) analyses.ConclusionsOur results provide some that cannabis initiation increases the risk of schizophrenia, although the size of the causal estimate is small. We find stronger evidence that schizophrenia risk predicts cannabis initiation, possibly as genetic instruments for schizophrenia are stronger than for cannabis initiation.


2011 ◽  
Vol 131 (1-3) ◽  
pp. 43-51 ◽  
Author(s):  
Jingchun Chen ◽  
Grace Lee ◽  
Ayman H. Fanous ◽  
Zhongming Zhao ◽  
Peilin Jia ◽  
...  

2018 ◽  
Vol 19 (1) ◽  
pp. 303-327 ◽  
Author(s):  
Stephen Burgess ◽  
Christopher N. Foley ◽  
Verena Zuber

An observational correlation between a suspected risk factor and an outcome does not necessarily imply that interventions on levels of the risk factor will have a causal impact on the outcome (correlation is not causation). If genetic variants associated with the risk factor are also associated with the outcome, then this increases the plausibility that the risk factor is a causal determinant of the outcome. However, if the genetic variants in the analysis do not have a specific biological link to the risk factor, then causal claims can be spurious. We review the Mendelian randomization paradigm for making causal inferences using genetic variants. We consider monogenic analysis, in which genetic variants are taken from a single gene region, and polygenic analysis, which includes variants from multiple regions. We focus on answering two questions: When can Mendelian randomization be used to make reliable causal inferences, and when can it be used to make relevant causal inferences?


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