A novel Mendelian randomization method with binary risk factor and outcome

2021 ◽  
Author(s):  
Philip H. Allman ◽  
Inmaculada Aban ◽  
Dustin M. Long ◽  
Stephen L. Bridges ◽  
Vinodh Srinivasasainagendra ◽  
...  
Keyword(s):  
Risk Factor ◽  
Mendelian Randomization ◽  
Randomization Method

scholarly journals Contextualizing selection bias in Mendelian randomization: how bad is it likely to be?

2018 ◽  
Vol 48 (3) ◽  
pp. 691-701 ◽  
Author(s):  
Apostolos Gkatzionis ◽  
Stephen Burgess
Keyword(s):  
Risk Factor ◽  
Selection Bias ◽  
Simulation Study ◽  
Cardiovascular Mortality ◽  
Mendelian Randomization ◽  
Inverse Probability Weighting ◽  
Probability Weighting ◽  
Inverse Probability ◽  
Type 1 Error

Abstract Background Selection bias affects Mendelian randomization investigations when selection into the study sample depends on a collider between the genetic variant and confounders of the risk factor–outcome association. However, the relative importance of selection bias for Mendelian randomization compared with other potential biases is unclear. Methods We performed an extensive simulation study to assess the impact of selection bias on a typical Mendelian randomization investigation. We considered inverse probability weighting as a potential method for reducing selection bias. Finally, we investigated whether selection bias may explain a recently reported finding that lipoprotein(a) is not a causal risk factor for cardiovascular mortality in individuals with previous coronary heart disease. Results Selection bias had a severe impact on bias and Type 1 error rates in our simulation study, but only when selection effects were large. For moderate effects of the risk factor on selection, bias was generally small and Type 1 error rate inflation was not considerable. Inverse probability weighting ameliorated bias when the selection model was correctly specified, but increased bias when selection bias was moderate and the model was misspecified. In the example of lipoprotein(a), strong genetic associations and strong confounder effects on selection mean the reported null effect on cardiovascular mortality could plausibly be explained by selection bias. Conclusions Selection bias can adversely affect Mendelian randomization investigations, but its impact is likely to be less than other biases. Selection bias is substantial when the effects of the risk factor and confounders on selection are particularly large.

scholarly journals A Mendelian randomization study identified obesity as a causal risk factor of uterine endometrial cancer in Japanese

2020 ◽  
Vol 111 (12) ◽  
pp. 4646-4651
Author(s):  
Tatsuo Masuda ◽  
Kotaro Ogawa ◽  
Yoichiro Kamatani ◽  
Yoshinori Murakami ◽  
Tadashi Kimura ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
Risk Factor ◽  
Mendelian Randomization ◽  
Causal Risk Factor ◽  
Uterine Endometrial Cancer

scholarly journals Inferring Causal Relationships Between Risk Factors and Outcomes from Genome-Wide Association Study Data

2018 ◽  
Vol 19 (1) ◽  
pp. 303-327 ◽  
Author(s):  
Stephen Burgess ◽  
Christopher N. Foley ◽  
Verena Zuber
Keyword(s):  
Risk Factor ◽  
Genetic Variants ◽  
Genome Wide Association Study ◽  
Mendelian Randomization ◽  
Single Gene ◽  
Study Data ◽  
Causal Inferences ◽  
Genome Wide ◽  
Multiple Regions ◽  
Polygenic Analysis

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?

scholarly journals Smoking and COVID-19: A two-sample Mendelian randomization study

2021 ◽  
Author(s):  
Bing-Kun Zheng ◽  
Na Li
Keyword(s):  
Observational Studies ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Dose Effect ◽  
Genetic Studies ◽  
Host Genetics ◽  
Causal Risk Factor ◽  
Increased Risk ◽  
Summary Data ◽  
Randomization Method

AbstractEvidence from observational studies suggested that smokers are at increased risk of coronavirus disease 2019 (COVID-19). We aimed to assess the causal effect of smoking on risk for COVID-19 susceptibility and severity using two-sample Mendelian randomization method. Smoking-associated variants were selected as instrument variables from two largest genetic studies. The latest summary data of COVID-19 that shared on Jan 18, 2021 by the COVID-19 Host Genetics Initiative was used. The present Mendelian randomization study provided genetic evidence that smoking was a causal risk factor for COVID-19 susceptibility and severity. In addition, there may be a dose-effect relationship between smoking and COVID-19 severity.

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 An efficient and robust approach to Mendelian randomization with measured pleiotropic effects in a high-dimensional setting

Biostatistics ◽  
2020 ◽  
Author(s):  
Andrew J Grant ◽  
Stephen Burgess
Keyword(s):  
Risk Factor ◽  
Genetic Variants ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Robust Estimator ◽  
Simulation Studies ◽  
Individual Level ◽  
Large Numbers ◽  
Level Data ◽  
Randomization Analysis

Summary Valid estimation of a causal effect using instrumental variables requires that all of the instruments are independent of the outcome conditional on the risk factor of interest and any confounders. In Mendelian randomization studies with large numbers of genetic variants used as instruments, it is unlikely that this condition will be met. Any given genetic variant could be associated with a large number of traits, all of which represent potential pathways to the outcome which bypass the risk factor of interest. Such pleiotropy can be accounted for using standard multivariable Mendelian randomization with all possible pleiotropic traits included as covariates. However, the estimator obtained in this way will be inefficient if some of the covariates do not truly sit on pleiotropic pathways to the outcome. We present a method that uses regularization to identify which out of a set of potential covariates need to be accounted for in a Mendelian randomization analysis in order to produce an efficient and robust estimator of a causal effect. The method can be used in the case where individual-level data are not available and the analysis must rely on summary-level data only. It can be used where there are any number of potential pleiotropic covariates up to the number of genetic variants less one. We show the results of simulation studies that demonstrate the performance of the proposed regularization method in realistic settings. We also illustrate the method in an applied example which looks at the causal effect of urate plasma concentration on coronary heart disease.

scholarly journals Is disrupted sleep a risk factor for Alzheimer’s disease? Evidence from a two-sample Mendelian randomization analysis

2020 ◽  
Author(s):  
Emma L Anderson ◽  
Rebecca C Richmond ◽  
Samuel E Jones ◽  
Gibran Hemani ◽  
Kaitlin H Wade ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Risk Factor ◽  
Daytime Sleepiness ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Association Studies ◽  
Self Report ◽  
Genome Wide Association Studies ◽  
Tentative Evidence

Abstract Background It is established that Alzheimer’s disease (AD) patients experience sleep disruption. However, it remains unknown whether disruption in the quantity, quality or timing of sleep is a risk factor for the onset of AD. Methods We used the largest published genome-wide association studies of self-reported and accelerometer-measured sleep traits (chronotype, duration, fragmentation, insomnia, daytime napping and daytime sleepiness), and AD. Mendelian randomization (MR) was used to estimate the causal effect of self-reported and accelerometer-measured sleep parameters on AD risk. Results Overall, there was little evidence to support a causal effect of sleep traits on AD risk. There was some suggestive evidence that self-reported daytime napping was associated with lower AD risk [odds ratio (OR): 0.70, 95% confidence interval (CI): 0.50–0.99). Some other sleep traits (accelerometer-measured ‘eveningness’ and sleep duration, and self-reported daytime sleepiness) had ORs of a similar magnitude to daytime napping, but were less precisely estimated. Conclusions Overall, we found very limited evidence to support a causal effect of sleep traits on AD risk. Our findings provide tentative evidence that daytime napping may reduce AD risk. Given that this is the first MR study of multiple self-report and objective sleep traits on AD risk, findings should be replicated using independent samples when such data become available.

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