Mendelian Randomization Test of Causal Effect Using High-Dimensional 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.

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

10.1101/2021.03.31.21254730 ◽

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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Powerful three-sample genome-wide design and robust statistical inference in summary-data Mendelian randomization

International Journal of Epidemiology ◽

10.1093/ije/dyz142 ◽

2019 ◽

Vol 48 (5) ◽

pp. 1478-1492 ◽

Cited By ~ 12

Author(s):

Qingyuan Zhao ◽

Yang Chen ◽

Jingshu Wang ◽

Dylan S Small

Keyword(s):

Odds Ratio ◽

Statistical Power ◽

Mendelian Randomization ◽

Causal Effect ◽

Density Lipoprotein ◽

P Value ◽

Genome Wide ◽

A Genome ◽

Summary Data ◽

Genetic Instruments

Abstract Background Summary-data Mendelian randomization (MR) has become a popular research design to estimate the causal effect of risk exposures. With the sample size of GWAS continuing to increase, it is now possible to use genetic instruments that are only weakly associated with the exposure. Development We propose a three-sample genome-wide design where typically 1000 independent genetic instruments across the whole genome are used. We develop an empirical partially Bayes statistical analysis approach where instruments are weighted according to their strength; thus weak instruments bring less variation to the estimator. The estimator is highly efficient with many weak genetic instruments and is robust to balanced and/or sparse pleiotropy. Application We apply our method to estimate the causal effect of body mass index (BMI) and major blood lipids on cardiovascular disease outcomes, and obtain substantially shorter confidence intervals (CIs). In particular, the estimated causal odds ratio of BMI on ischaemic stroke is 1.19 (95% CI: 1.07–1.32, P-value <0.001); the estimated causal odds ratio of high-density lipoprotein cholesterol (HDL-C) on coronary artery disease (CAD) is 0.78 (95% CI: 0.73–0.84, P-value <0.001). However, the estimated effect of HDL-C attenuates and become statistically non-significant when we only use strong instruments. Conclusions A genome-wide design can greatly improve the statistical power of MR studies. Robust statistical methods may alleviate but not solve the problem of horizontal pleiotropy. Our empirical results suggest that the relationship between HDL-C and CAD is heterogeneous, and it may be too soon to completely dismiss the HDL hypothesis.

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An examination of multivariable Mendelian randomization in the single sample and two-sample summary data settings

10.1101/306209 ◽

2018 ◽

Cited By ~ 21

Author(s):

Eleanor Sanderson ◽

George Davey Smith ◽

Frank Windmeijer ◽

Jack Bowden

Keyword(s):

Mendelian Randomization ◽

Causal Effect ◽

Causal Effects ◽

Single Sample ◽

Mendelian Randomisation ◽

Uk Biobank ◽

Level Data ◽

Wide Range ◽

Using Data ◽

Summary Data

AbstractBackgroundMendelian Randomisation (MR) is a powerful tool in epidemiology which can be used to estimate the causal effect of an exposure on an outcome in the presence of unobserved confounding, by utilising genetic variants that are instrumental variables (IVs) for the exposure. This has been extended to Multivariable MR (MVMR) to estimate the effect of two or more exposures on an outcome.Methods/ResultsWe use simulations and theory to clarify the interpretation of estimated effects in a MVMR analysis under a range of underlying scenarios, where a secondary exposure acts variously as a confounder, a mediator, a pleiotropic pathway and a collider. We then describe how instrument strength and validity can be assessed for an MVMR analysis in the single sample setting, and develop tests to assess these assumptions in the popular two-sample summary data setting. We illustrate our methods using data from UK biobank to estimate the effect of education and cognitive ability on body mass index.ConclusionMVMR analysis consistently estimates the effect of an exposure, or exposures, of interest and provides a powerful tool for determining causal effects in a wide range of scenarios with either individual or summary level data.

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Causality of Abdominal Obesity on Cognition: a Trans-ethnic Mendelian Randomization study

10.1101/2021.10.13.21264895 ◽

2021 ◽

Author(s):

Shi-Heng Wang ◽

Mei-Hsin Su ◽

Chia-Yen Chen ◽

Yen-Feng Lin ◽

Yen-Chen Anne Feng ◽

...

Keyword(s):

Cognitive Aging ◽

Observational Studies ◽

Mendelian Randomization ◽

Causal Effect ◽

European Ancestry ◽

P Value ◽

Abdominal Adiposity ◽

Uk Biobank ◽

Reverse Causality ◽

Summary Data

Obesity has been associated with cognition in observational studies; however, whether its effect is confounding, reverse causality, or causal remains inconclusive. Using two-sample Mendelian randomization (MR) analyses, we investigated the causality of overall obesity, measured by BMI, and abdominal adiposity, measured by waist-hip ratio adjusted for BMI (WHRadjBMI), on cognition. Using summary data from the GIANT consortium, COGENT consortium, and UK Biobank of European ancestry, there was no causal effect of BMI on cognition performance (beta[95% CI]=-0.04[-0.12,0.04], p-value=0.35); however, a 1-SD increase in WHRadjBMI was associated with 0.07 standardized decrease in cognition performance (beta[95% CI]=-0.07[-0.12,-0.02], p=0.006). Using raw data from the Taiwan Biobank of Asian ancestry, there was no causal effect of BMI on cognitive aging (beta[95% CI]=0.00[-0.09,0.09], p-value=0.95); however, a 1-SD increase in WHRadjBMI was associated with a 0.17 standardized decrease in cognitive aging (beta[95% CI]=-0.17[-0.30,-0.03], p=0.02). This trans-ethnic MR study reveals that abdominal adiposity impairs cognition.

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

10.1101/159442 ◽

2017 ◽

Cited By ~ 17

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.

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

10.1101/200378 ◽

2017 ◽

Cited By ~ 7

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.

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

International Journal of Epidemiology ◽

10.1093/ije/dyy258 ◽

2018 ◽

Vol 48 (3) ◽

pp. 728-742 ◽

Cited By ~ 42

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.

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An examination of multivariable Mendelian randomization in the single-sample and two-sample summary data settings

International Journal of Epidemiology ◽

10.1093/ije/dyy262 ◽

2018 ◽

Vol 48 (3) ◽

pp. 713-727 ◽

Cited By ~ 90

Author(s):

Eleanor Sanderson ◽

George Davey Smith ◽

Frank Windmeijer ◽

Jack Bowden

Keyword(s):

Genetic Variants ◽

Mendelian Randomization ◽

Causal Effect ◽

Causal Effects ◽

Single Sample ◽

Uk Biobank ◽

Level Data ◽

Wide Range ◽

Using Data ◽

Summary Data

Abstract Background Mendelian randomization (MR) is a powerful tool in epidemiology that can be used to estimate the causal effect of an exposure on an outcome in the presence of unobserved confounding, by utilizing genetic variants that are instrumental variables (IVs) for the exposure. This has been extended to multivariable MR (MVMR) to estimate the effect of two or more exposures on an outcome. Methods and results We use simulations and theory to clarify the interpretation of estimated effects in a MVMR analysis under a range of underlying scenarios, where a secondary exposure acts variously as a confounder, a mediator, a pleiotropic pathway and a collider. We then describe how instrument strength and validity can be assessed for an MVMR analysis in the single-sample setting, and develop tests to assess these assumptions in the popular two-sample summary data setting. We illustrate our methods using data from UK Biobank to estimate the effect of education and cognitive ability on body mass index. Conclusion MVMR analysis consistently estimates the direct causal effect of an exposure, or exposures, of interest and provides a powerful tool for determining causal effects in a wide range of scenarios with either individual- or summary-level data.

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OP96 The causal effect of BMI on neurodevelopment: a within family mendelian randomization study using MoBa

10.1136/jech-2020-ssmabstracts.94 ◽

2020 ◽

Author(s):

AM Hughes ◽

H Ask ◽

T Tesli ◽

RB Askeland ◽

T Reichborn-Kjennerud ◽

...

Keyword(s):

Mendelian Randomization ◽

Causal Effect

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