scholarly journals Improving the visualisation, interpretation and analysis of two-sample summary data Mendelian randomization via the radial plot and radial regression

2017 ◽  
Author(s):  
Jack Bowden ◽  
Wesley Spiller ◽  
Fabiola Del Greco-M F ◽  
Nuala Sheehan ◽  
John Thompson ◽  
...  
Keyword(s):  
Mendelian Randomization ◽  
Disease Risk ◽  
Causal Effect ◽  
Original Method ◽  
Effect Estimate ◽  
Scatter Plot ◽  
Modelling Framework ◽  
Summary Data ◽  
Mr Study ◽  
Radial Plot

AbstractBackgroundSummary data furnishing a two-sample Mendelian randomization study are often visualized with the aid of a scatter plot, in which single nucleotide polymorphism (SNP)-outcome associations are plotted against the SNP-exposure associations to provide an immediate picture of the causal effect estimate for each individual variant. It is also convenient to overlay the standard inverse variance weighted (IVW) estimate of causal effect as a fitted slope, to see whether an individual SNP provides evidence that supports, or conflicts with, the overall consensus. Unfortunately, the traditional scatter plot is not the most appropriate means to achieve this aim whenever SNP-outcome associations are estimated with varying degrees of precision and this is reflected in the analysis.MethodsWe propose instead to use a small modification of the scatter plot - the Galbraith radial plot - for the presentation of data and results from an MR study, which enjoys many advantages over the original method. On a practical level it removes the need to recode the genetic data and enables a more straightforward detection of outliers and influential data points. Its use extends beyond the purely aesthetic, however, to suggest a more general modelling framework to operate within when conducting an MR study, including a new form of MR-Egger regression.ResultsWe illustrate the methods using data from a two-sample Mendelian randomization study to probe the causal effect of systolic blood pressure on coronary heart disease risk, allowing for the possible effects of pleiotropy. The radial plot is shown to aid the detection of a single outlying variant which is responsible for large differences between IVW and MR-Egger regression estimates. Several additional plots are also proposed for informative data visualisation.ConclusionThe radial plot should be considered in place of the scatter plot for visualising, analysing and interpreting data from a two-sample summary data MR study. Software is provided to help facilitate its use.

scholarly journals Improving the accuracy of two-sample summary data Mendelian randomization: moving beyond the NOME assumption

2017 ◽  
Author(s):  
Jack Bowden ◽  
Fabiola Del Greco M ◽  
Cosetta Minelli ◽  
Qingyuan Zhao ◽  
Debbie A Lawlor ◽  
...  
Keyword(s):  
Genetic Variants ◽  
Type I Error ◽  
Mendelian Randomization ◽  
Disease Risk ◽  
Causal Effect ◽  
Meta Analysis ◽  
Type I ◽  
Weak Instruments ◽  
Using Data ◽  
Summary Data

AbstractBackgroundTwo-sample summary data Mendelian randomization (MR) incorporating multiple genetic variants within a meta-analysis framework is a popular technique for assessing causality in epidemiology. If all genetic variants satisfy the instrumental variable (IV) and necessary modelling assumptions, then their individual ratio estimates of causal effect should be homogeneous. Observed heterogeneity signals that one or more of these assumptions could have been violated.MethodsCausal estimation and heterogeneity assessment in MR requires an approximation for the variance, or equivalently the inverse-variance weight, of each ratio estimate. We show that the most popular ‘1st order’ weights can lead to an inflation in the chances of detecting heterogeneity when in fact it is not present. Conversely, ostensibly more accurate ‘2nd order’ weights can dramatically increase the chances of failing to detect heterogeneity, when it is truly present. We derive modified weights to mitigate both of these adverse effects.ResultsUsing Monte Carlo simulations, we show that the modified weights outperform 1st and 2nd order weights in terms of heterogeneity quantification. Modified weights are also shown to remove the phenomenon of regression dilution bias in MR estimates obtained from weak instruments, unlike those obtained using 1st and 2nd order weights. However, with small numbers of weak instruments, this comes at the cost of a reduction in estimate precision and power to detect a causal effect compared to 1st order weighting. Moreover, 1st order weights always furnish unbiased estimates and preserve the type I error rate under the causal null. We illustrate the utility of the new method using data from a recent two-sample summary data MR analysis to assess the causal role of systolic blood pressure on coronary heart disease risk.ConclusionsWe propose the use of modified weights within two-sample summary data MR studies for accurately quantifying heterogeneity and detecting outliers in the presence of weak instruments. Modified weights also have an important role to play in terms of causal estimation (in tandem with 1st order weights) but further research is required to understand their strengths and weaknesses in specific settings.

scholarly journals Improving the accuracy of two-sample summary-data Mendelian randomization: moving beyond the NOME assumption

2018 ◽  
Vol 48 (3) ◽  
pp. 728-742 ◽  
Author(s):  
Jack Bowden ◽  
Fabiola Del Greco M ◽  
Cosetta Minelli ◽  
Qingyuan Zhao ◽  
Debbie A Lawlor ◽  
...  
Keyword(s):  
Genetic Variants ◽  
Mendelian Randomization ◽  
Disease Risk ◽  
Causal Effect ◽  
Meta Analysis ◽  
Second Order ◽  
Type I ◽  
Weak Instruments ◽  
First Order ◽  
Summary Data

Abstract Background Two-sample summary-data Mendelian randomization (MR) incorporating multiple genetic variants within a meta-analysis framework is a popular technique for assessing causality in epidemiology. If all genetic variants satisfy the instrumental variable (IV) and necessary modelling assumptions, then their individual ratio estimates of causal effect should be homogeneous. Observed heterogeneity signals that one or more of these assumptions could have been violated. Methods Causal estimation and heterogeneity assessment in MR require an approximation for the variance, or equivalently the inverse-variance weight, of each ratio estimate. We show that the most popular ‘first-order’ weights can lead to an inflation in the chances of detecting heterogeneity when in fact it is not present. Conversely, ostensibly more accurate ‘second-order’ weights can dramatically increase the chances of failing to detect heterogeneity when it is truly present. We derive modified weights to mitigate both of these adverse effects. Results Using Monte Carlo simulations, we show that the modified weights outperform first- and second-order weights in terms of heterogeneity quantification. Modified weights are also shown to remove the phenomenon of regression dilution bias in MR estimates obtained from weak instruments, unlike those obtained using first- and second-order weights. However, with small numbers of weak instruments, this comes at the cost of a reduction in estimate precision and power to detect a causal effect compared with first-order weighting. Moreover, first-order weights always furnish unbiased estimates and preserve the type I error rate under the causal null. We illustrate the utility of the new method using data from a recent two-sample summary-data MR analysis to assess the causal role of systolic blood pressure on coronary heart disease risk. Conclusions We propose the use of modified weights within two-sample summary-data MR studies for accurately quantifying heterogeneity and detecting outliers in the presence of weak instruments. Modified weights also have an important role to play in terms of causal estimation (in tandem with first-order weights) but further research is required to understand their strengths and weaknesses in specific settings.

scholarly journals Causal effect of renal function on venous thromboembolism: a two-sample Mendelian randomization investigation

2021 ◽  
Author(s):  
Shuai Yuan ◽  
Maria Bruzelius ◽  
Susanna C. Larsson
Keyword(s):  
Renal Function ◽  
Venous Thromboembolism ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Meta Analysis ◽  
Genome Wide Association Studies ◽  
Uk Biobank ◽  
Genome Wide ◽  
Increased Risk ◽  
Mr Study

AbstractWhether renal function is causally associated with venous thromboembolism (VTE) is not yet fully elucidated. We conducted a two-sample Mendelian randomization (MR) study to determine the causal effect of renal function, measured as estimated glomerular filtration rate (eGFR), on VTE. Single-nucleotide polymorphisms associated with eGFR were selected as instrumental variables at the genome-wide significance level (p < 5 × 10−8) from a meta-analysis of 122 genome-wide association studies including up to 1,046,070 individuals. Summary-level data for VTE were obtained from the FinnGen consortium (6913 VTE cases and 169,986 non-cases) and UK Biobank study (4620 VTE cases and 356,574 non-cases). MR estimates were calculated using the random-effects inverse-variance weighted method and combined using fixed-effects meta-analysis. Genetically predicted decreased eGFR was significantly associated with an increased risk of VTE in both FinnGen and UK Biobank. For one-unit decrease in log-transformed eGFR, the odds ratios of VTE were 2.93 (95% confidence interval (CI) 1.25, 6.84) and 4.46 (95% CI 1.59, 12.5) when using data from FinnGen and UK Biobank, respectively. The combined odds ratio was 3.47 (95% CI 1.80, 6.68). Results were consistent in all sensitivity analyses and no horizontal pleiotropy was detected. This MR-study supported a casual role of impaired renal function in VTE.

scholarly journals Bias in two-sample Mendelian randomization when using heritable covariable-adjusted summary associations

2021 ◽  
Author(s):  
Fernando Pires Hartwig ◽  
Kate Tilling ◽  
George Davey Smith ◽  
Deborah A Lawlor ◽  
Maria Carolina Borges
Keyword(s):  
Waist Circumference ◽  
Genetic Variants ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Association Studies ◽  
Real Data ◽  
Sensitivity Analyses ◽  
Effect Estimate ◽  
Genome Wide Association Studies ◽  
Residual Confounding

Abstract Background Two-sample Mendelian randomization (MR) allows the use of freely accessible summary association results from genome-wide association studies (GWAS) to estimate causal effects of modifiable exposures on outcomes. Some GWAS adjust for heritable covariables in an attempt to estimate direct effects of genetic variants on the trait of interest. One, both or neither of the exposure GWAS and outcome GWAS may have been adjusted for covariables. Methods We performed a simulation study comprising different scenarios that could motivate covariable adjustment in a GWAS and analysed real data to assess the influence of using covariable-adjusted summary association results in two-sample MR. Results In the absence of residual confounding between exposure and covariable, between exposure and outcome, and between covariable and outcome, using covariable-adjusted summary associations for two-sample MR eliminated bias due to horizontal pleiotropy. However, covariable adjustment led to bias in the presence of residual confounding (especially between the covariable and the outcome), even in the absence of horizontal pleiotropy (when the genetic variants would be valid instruments without covariable adjustment). In an analysis using real data from the Genetic Investigation of ANthropometric Traits (GIANT) consortium and UK Biobank, the causal effect estimate of waist circumference on blood pressure changed direction upon adjustment of waist circumference for body mass index. Conclusions Our findings indicate that using covariable-adjusted summary associations in MR should generally be avoided. When that is not possible, careful consideration of the causal relationships underlying the data (including potentially unmeasured confounders) is required to direct sensitivity analyses and interpret results with appropriate caution.

scholarly journals Causal Effect Between Total Cholesterol and HDL Cholesterol as Risk Factors for Chronic Kidney Disease: A Mendelian Randomization Study

2020 ◽  
Author(s):  
Liu Miao ◽  
Yan Min ◽  
Chuan-Meng Zhu ◽  
Jian-Hong Chen ◽  
Bin Qi ◽  
...  
Keyword(s):  
Total Cholesterol ◽  
Serum Lipid ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Hdl Cholesterol ◽  
Causal Effects ◽  
Lipid Levels ◽  
Serum Lipid Levels ◽  
Genome Wide ◽  
Mr Study

Abstract Background/Aims: While observational studies show an association between serum lipid levels and cardiovascular disease (CVD), intervention studies that examine the preventive effects of serum lipid levels on the development of CKD are lacking. Methods: To estimate the role of serum lipid levels in the etiology of CKD, we conducted a two-sample Mendelian randomization (MR) study on serum lipid levels. Single nucleotide polymorphisms (SNPs), which were significantly associated genome-wide with plasma serum lipid levels from the GLGC and CKDGen consortium genome-wide association study (GWAS), including total cholesterol (TC, n = 187365), triglyceride (TG, n = 177861), HDL cholesterol (HDL-C, n = 187167), LDL cholesterol (LDL-C, n = 173082), apolipoprotein A1 (ApoA1, n = 20687), apolipoprotein B (ApoB, n = 20690) and CKD (n = 117165), were used as instrumental variables. None of the lipid-related SNPs was associated with CKD (all P > 0.05). Results: MR analysis genetically predicted the causal effect between TC/HDL-C and CKD. The odds ratio (OR) and 95% confidence interval (CI) of TC within CKD was 0.756 (0.579 to 0.933) (P = 0.002), and HDL-C was 0.85 (0.687 to 1.012) (P = 0.049). No causal effects between TG, LDL-C- ApoA1, ApoB and CKD were observed. Sensitivity analyses confirmed that TC and HDL-C were significantly associated with CKD. Conclusions: The findings from this MR study indicate causal effects between TC, HDL-C and CKD. Decreased TC and elevated HDL-C may reduce the incidence of CKD but need to be further confirmed by using a genetic and environmental approach.

scholarly journals Bipolar disorder and cannabis use: a bidirectional two-sample Mendelian randomization study

2020 ◽  
Author(s):  
Oskar Hougaard Jefsen ◽  
Maria Speed ◽  
Doug Speed ◽  
Søren Dinesen Østergaard
Keyword(s):  
Bipolar Disorder ◽  
Standard Deviation ◽  
Odds Ratio ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Cannabis Use ◽  
Genome Wide ◽  
Log Odds ◽  
The Relationship ◽  
Mr Study

AbstractAimsCannabis use is associated with a number of psychiatric disorders, however the causal nature of these associations has been difficult to establish. Mendelian randomization (MR) offers a way to infer causality between exposures with known genetic predictors (genome-wide significant single nucleotide polymorphisms (SNPs)) and outcomes of interest. MR has previously been applied to investigate the relationship between lifetime cannabis use (having ever used cannabis) and schizophrenia, depression, and attention-deficit / hyperactivity disorder (ADHD), but not bipolar disorder, representing a gap in the literature.MethodsWe conducted a two-sample bidirectional MR study on the relationship between bipolar disorder and lifetime cannabis use. Genetic instruments (SNPs) were obtained from the summary statistics of recent large genome-wide association studies (GWAS). We conducted a two-sample bidirectional MR study on the relationship between bipolar disorder and lifetime cannabis use, using inverse-variance weighted regression, weighted median regression and Egger regression.ResultsGenetic liability to bipolar disorder was significantly associated with an increased risk of lifetime cannabis use: scaled log-odds ratio (standard deviation) = 0.0174 (0.039); P-value = 0.00001. Genetic liability to lifetime cannabis use showed no association with the risk of bipolar disorder: scaled log-odds ratio (standard deviation) = 0.168 (0.180); P-value = 0.351. The sensitivity analyses showed no evidence for pleiotropic effects.ConclusionsThe present study finds evidence for a causal effect of liability to bipolar disorder on the risk of using cannabis at least once. No evidence was found for a causal effect of liability to cannabis use on the risk of bipolar disorder. These findings add important new knowledge to the understanding of the complex relationship between cannabis use and psychiatric disorders.

scholarly journals Major depressive disorder but not bipolar disorder and schizophrenia is a causal factor for type 2 diabetes as determined by Mendelian randomization

2020 ◽  
Author(s):  
Heejin Jin ◽  
Jeewon Lee ◽  
Oh Sohee ◽  
Sanghun Lee ◽  
Sungho Won
Keyword(s):  
Type 2 Diabetes ◽  
Bipolar Disorder ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Reverse Causality ◽  
Major Depressive ◽  
Mr Study

Objective: In many epidemiologic studies, type 2 diabetes has been reported to be associated with severe mental illness (SMI) such as schizophrenia (SCZ), bipolar disorder (BPD), and major depressive disorder (MDD). However, the relationship between SMI and type 2 diabetes is bi-directional, and the causal relationship remains unclear due to various confounders. Therefore, a Mendelian randomization (MR) study is necessary to identify the causality between them. Research Design and Methods: We conducted a two−sample MR study to identify the causal effect of SMI on type 2 diabetes using the inverse-variance weighted (IVW), MR−Egger, MR− Egger with a simulation extrapolation, weighted median approach, and MR-Pleiotropy RESidual Sum and Outlier methods. The most appropriate method was selected according to the instrument variables assumption. Results: We found that MDD had a significant causal effect on type 2 diabetes from the results obtained using the IVW method (Odds ratio (OR): 1.191, 95% CI: 1.036−1.372, P = 0.014); however, this was not observed for BPD (IVW, OR: 1.006, 95% CI: 0.918−1.104, P = 0.892) or SCZ (IVW, OR: 1.016, 95% CI: 0.974−1.059, P = 0.463). The absence of reverse-causality between MDD and type 2 diabetes was also demonstrated from bidirectional MR studies. Conclusions: These results clearly reveal important knowledge on the causal role of MDD in the risk of type 2 diabetes without a residual confounding, whereas the causality of BPD and SCZ was not shown. Therefore, careful attention should be paid to MDD patients in type 2 diabetes prevention and treatment.

scholarly journals Treatment of Metformin Decreases the Risk of Varicose Vein: A Mendelian Randomization Study

2020 ◽  
Author(s):  
Qinchang Chen ◽  
Lingling Li ◽  
Ridong Wu ◽  
Shenming Wang ◽  
Chen Yao
Keyword(s):  
Sensitivity Analysis ◽  
Varicose Vein ◽  
Protective Factor ◽  
Genome Wide Association Study ◽  
Mendelian Randomization ◽  
Vascular System ◽  
Varicose Veins ◽  
Causal Effect ◽  
Regression Method ◽  
Summary Data

Abstract Background Varicose vein is a common illness of the vascular system which affects life quality and social function of patients. We aimed to assess the causality between metformin and varicose vein using a two-sample Mendelian randomization（MR）analysis based on genome-wide association study (GWAS) summary data.Methods Twenty-five single nucleotide polymorphisms (SNPs) were selected from the GWAS summary data from Neale Lab and MRC-IEU Consortium available on the MR-base platform. Inverse variance weighted (IVW), MR-egger method, weighted median method and weighted mode were adopted. Results were evaluated by pleiotropy test using an Egger regression method and sensitivity analysis preforming a leaving-one-out (LOO) method. Analyses were performed using R package “TwoSampleMR”.Results The result of IVW method showed that one SD increased treatment of metformin was linked with approximately 10% lower risk of varicose vein (OR,0.90; 95%CI, 0.85-0.96; P, 6.99e-04). It was similar to that measured by other methods in the aspect of effect size and direction. There is no evidence to supporting genetic pleiotropy in the MR-Egger regression method (intercept=2.5e-04, P=0.33). No single SNP was detected to be strongly driving the overall causal effect in a LOO sensitivity analysis. The genetically predicted treatment of metformin was negatively casually associated with varicose veins.Conclusions This study suggested that treatment of metformin was a casual protective factor of varicose vein. Further researches are required to confirm our findings and explore the potential mechanisms of metformin on varicose vein.

scholarly journals Selecting causal risk factors from high-throughput experiments using multivariable Mendelian randomization

2018 ◽  
Author(s):  
Verena Zuber ◽  
Johanna Maria Colijn ◽  
Caroline Klaver ◽  
Stephen Burgess
Keyword(s):  
Risk Factors ◽  
High Throughput ◽  
Bayesian Model Averaging ◽  
Mendelian Randomization ◽  
Disease Risk ◽  
Causal Effect ◽  
Model Averaging ◽  
Age Related Macular Degeneration ◽  
Age Related ◽  
High Throughput Experiments

AbstractModern high-throughput experiments provide a rich resource to investigate causal determinants of disease risk. Mendelian randomization (MR) is the use of genetic variants as instrumental variables to infer the causal effect of a specific risk factor on an outcome. Multivariable MR is an extension of the standard MR framework to consider multiple potential risk factors in a single model. However, current implementations of multivariable MR use standard linear regression and hence perform poorly with many risk factors.Here, we propose a novel approach to two-sample multivariable MR based on Bayesian model averaging (MR-BMA) that scales to high-throughput experiments. In a realistic simulation study, we show that MR-BMA can detect true causal risk factors even when the candidate risk factors are highly correlated. We illustrate MR-BMA by analysing publicly-available summarized data on metabolites to prioritise likely causal biomarkers for age-related macular degeneration.

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.

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