Software Application Profile: Bayesian estimation of inverse variance weighted and MR-Egger models for two-sample Mendelian randomization studies—mrbayes

2020 ◽  
Author(s):  
Okezie Uche-Ikonne ◽  
Frank Dondelinger ◽  
Tom Palmer
Keyword(s):  
Bayesian Estimation ◽  
Prior Distribution ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Model Parameters ◽  
Software Environment ◽  
Prior Distributions ◽  
Software Application ◽  
Level Data ◽  
Inverse Variance

Abstract Motivation We present our package, mrbayes, for the open source software environment R. The package implements Bayesian estimation for inverse variance weighted (IVW) and MR-Egger models, including the radial MR-Egger model, for summary-level data in Mendelian randomization (MR) analyses. Implementation We have implemented a choice of prior distributions for the model parameters, namely; weakly informative, non-informative, a joint prior for the MR-Egger model slope and intercept, and an informative prior (pseudo-horseshoe prior), or the user can specify their own prior distribution. General features Users have the option of fitting the models using either JAGS or Stan software packages with similar prior distributions; the option for the user-defined prior distribution is only in our JAGS functions. We show how to use the package through an applied example investigating the causal effect of body mass index (BMI) on acute ischaemic stroke. Availability The package is freely available, under the GNU General Public License v3.0, on GitHub [https://github.com/okezie94/mrbayes] or CRAN [https://CRAN.R-project.org/package=mrbayes].

scholarly journals Bayesian estimation of IVW and MR-Egger models for two-sample Mendelian randomization studies

2019 ◽  
Author(s):  
Okezie Uche-Ikonne ◽  
Frank Dondelinger ◽  
Tom Palmer
Keyword(s):  
Bayesian Estimation ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Model Parameters ◽  
Software Environment ◽  
Link Type ◽  
Mcmc Estimation ◽  
Level Data ◽  
Future Work ◽  
Model Slope

AbstractWe present our package, mrbayes, for the open source software environment R. The package implements Bayesian estimation of IVW and MR-Egger models, including the radial MR-Egger model, for summary-level data Mendelian randomization analyses. We have implemented a choice of prior distributions for the model parameters, namely; weakly informative, non-informative, a joint prior for the MR-Egger model slope and intercept, and a pseudo-horseshoe prior, or the user can specify their own prior. We show how to use the package through an applied example investigating the causal effect of BMI on insulin resistance. In future work, we plan to provide functions for alternative MCMC estimation software such as Stan and OpenBugs.AvailabilityThe package is freely available, under the MIT license, on GitHub here https://github.com/okezie94/mrbayes.It can be installed in R using the following commands.There is a website of the package helpfiles at https://okezie94.github.io/mrbayes/.

Effect of metabolites levels on type 2 diabetes mellitus and glycemic traits: a Mendelian randomization study

2021 ◽  
Author(s):  
Yue Sun ◽  
Ya-Ke Lu ◽  
Hao-Yu Gao ◽  
Yu-Xiang Yan
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Meta Analysis ◽  
Significance Level ◽  
Inverse Variance ◽  
Meta Analyses

Abstract Objective To assess the causal associations of plasma levels of metabolites with type 2 diabetes mellitus (T2DM) and glycemic traits. Methods Two-sample mendelian randomization (MR) was conducted to assess the causal associations. Genetic variants strongly associated with metabolites at genome-wide significance level (P < 5 × 10 −8) were selected from public GWAS, and SNPs of Outcomes were obtained from the DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) consortium for T2DM and from the Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC) for the fasting glucose, insulin and HbA1c. The Wald ratio and inverse-variance weighted methods were used for analyses, and MR-Egger was used for sensitivity analysis. Results The β estimates per 1 SD increasement of arachidonic acid (AA) level was 0.16 (95% CI: 0.078, 0.242; P<0.001). Genetic predisposition to higher plasma AA levels were associated with higher FG levels (β 0.10 [95%CI: 0.064, 0.134], P<0.001), higher HbA1c levels (β 0.04 [95%CI: 0.027, 0.061]) and lower FI levels (β -0.025 [95%CI: -0.047, -0.002], P=0.033). Besides, 2-hydroxybutyric acid (2-HBA) might have positive causal effect on glycemic traits. Conclusions Our findings suggest that AA and 2-HBA may have the causal associations on T2DM and glycemic traits. It is beneficial for clarifying the pathogenesis of T2DM, which would be valuable for early identification and prevention for T2DM.

Circulating adiponectin levels and systemic lupus erythematosus: a two-sample Mendelian randomization study

Rheumatology ◽  
2020 ◽  
Author(s):  
Yi-Lin Dan ◽  
Peng Wang ◽  
Zhongle Cheng ◽  
Qian Wu ◽  
Xue-Rong Wang ◽  
...  
Keyword(s):  
Single Nucleotide Polymorphisms ◽  
Instrumental Variables ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Causal Effects ◽  
European Ancestry ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Inverse Variance ◽  
Weighted Median

Abstract Objectives Several studies have reported increased serum/plasma adiponectin levels in SLE patients. This study was performed to estimate the causal effects of circulating adiponectin levels on SLE. Methods We selected nine independent single-nucleotide polymorphisms that were associated with circulating adiponectin levels (P < 5 × 10−8) as instrumental variables from a published genome-wide association study (GWAS) meta-analysis. The corresponding effects between instrumental variables and outcome (SLE) were obtained from an SLE GWAS analysis, including 7219 cases with 15 991 controls of European ancestry. Two-sample Mendelian randomization (MR) analyses with inverse-variance weighted, MR-Egger regression, weighted median and weight mode methods were used to evaluate the causal effects. Results The results of inverse-variance weighted methods showed no significantly causal associations of genetically predicted circulating adiponectin levels and the risk for SLE, with an odds ratio (OR) of 1.38 (95% CI 0.91, 1.35; P = 0.130). MR-Egger [OR 1.62 (95% CI 0.85, 1.54), P = 0.195], weighted median [OR 1.37 (95% CI 0.82, 1.35), P = 0.235) and weighted mode methods [OR 1.39 (95% CI 0.86, 1.38), P = 0.219] also supported no significant associations of circulating adiponectin levels and the risk for SLE. Furthermore, MR analyses in using SLE-associated single-nucleotide polymorphisms as an instrumental variable showed no associations of genetically predicted risk of SLE with circulating adiponectin levels. Conclusion Our study did not find evidence for a causal relationship between circulating adiponectin levels and the risk of SLE or of a causal effect of SLE on circulating adiponectin levels.

scholarly journals Software application profile: mrrobust—a tool for performing two-sample summary Mendelian randomization analyses

2018 ◽  
Vol 48 (3) ◽  
pp. 684-690 ◽  
Author(s):  
Wes Spiller ◽  
Neil M Davies ◽  
Tom M Palmer
Keyword(s):  
Mendelian Randomization ◽  
Association Studies ◽  
Estimation Methods ◽  
Genome Wide Association Studies ◽  
Software Application ◽  
Genome Wide ◽  
Inverse Variance ◽  
Randomization Analysis ◽  
Summary Data ◽  
Weighted Estimation

Abstract Motivation In recent years, Mendelian randomization analysis using summary data from genome-wide association studies has become a popular approach for investigating causal relationships in epidemiology. The mrrobust Stata package implements several of the recently developed methods. Implementation mrrobust is freely available as a Stata package. General features The package includes inverse variance weighted estimation, as well as a range of median, modal and MR-Egger estimation methods. Using mrrobust, plots can be constructed visualizing each estimate either individually or simultaneously. The package also provides statistics such as IGX2, which are useful in assessing attenuation bias in causal estimates. Availability The software is freely available from GitHub [https://raw.github.com/remlapmot/mrrobust/master/].

scholarly journals The association between depression and metabolic syndrome and its components: a bidirectional two-sample Mendelian randomization study

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Min Zhang ◽  
Jing Chen ◽  
Zhiqun Yin ◽  
Lanbing Wang ◽  
Lihua Peng
Keyword(s):  
Metabolic Syndrome ◽  
Waist Circumference ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Fasting Blood Glucose ◽  
Density Lipoprotein ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Level Data ◽  
Inverse Variance

AbstractObservational studies suggested a bidirectional correlation between depression and metabolic syndrome (MetS) and its components. However, the causal associations between them remained unclear. We aimed to investigate whether genetically predicted depression is related to the risk of MetS and its components, and vice versa. We performed a bidirectional two-sample Mendelian randomization (MR) study using summary-level data from the most comprehensive genome-wide association studies (GWAS) of depression (n = 2,113,907), MetS (n = 291,107), waist circumference (n = 462,166), hypertension (n = 463,010) fasting blood glucose (FBG, n = 281,416), triglycerides (n = 441,016), high-density lipoprotein cholesterol (HDL-C, n = 403,943). The random-effects inverse-variance weighted (IVW) method was applied as the primary method. The results identified that genetically predicted depression was significantly positive associated with risk of MetS (OR: 1.224, 95% CI: 1.091–1.374, p = 5.58 × 10−4), waist circumference (OR: 1.083, 95% CI: 1.027–1.143, p = 0.003), hypertension (OR: 1.028, 95% CI: 1.016–1.039, p = 1.34 × 10−6) and triglycerides (OR: 1.111, 95% CI: 1.060–1.163, p = 9.35 × 10−6) while negative associated with HDL-C (OR: 0.932, 95% CI: 0.885–0.981, p = 0.007) but not FBG (OR: 1.010, 95% CI: 0.986–1.034, p = 1.34). No causal relationships were identified for MetS and its components on depression risk. The present MR analysis strength the evidence that depression is a risk factor for MetS and its components (waist circumference, hypertension, FBG, triglycerides, and HDL-C). Early diagnosis and prevention of depression are crucial in the management of MetS and its components.

scholarly journals Causal effect of Insulin Resistance on Small Vessel Stroke and Alzheimer's Disease: A Mendelian Randomization Analysis

2021 ◽  
Author(s):  
Mengyuan Zhou ◽  
Hao Li ◽  
Yongjun Wang ◽  
Yuesong Pan ◽  
Yilong Wang
Keyword(s):  
Alzheimer’S Disease ◽  
Insulin Resistance ◽  
Alzheimer's Disease ◽  
Gold Standard ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Small Vessel ◽  
European Ancestry ◽  
Fasting Insulin ◽  
Inverse Variance

Abstract Background The causal effect of insulin resistance on small vessel stroke and Alzheimer Disease was controversial in previous studies. Methods We selected 12 single-nucleotide polymorphisms (SNPs) associated with body mass index (BMI)-adjusted fasting insulin levels and 5 SNPs associated with gold standard measures of insulin resistance as instrumental variables in Mendelian randomization (MR) analyses. Summary statistical data of SNP-small vessel stroke and of SNP-AD associations were derived from the Multi-ancestry Genome-Wide Association Study of Stroke Consortium and Psychiatric Genomics Consortium-Alzheimer’s Disease Workgroup data of individuals of European ancestry. Two-sample MR estimates were conducted with inverse-variance-weighted, robust inverse-variance-weighted, simple median, weighted median, weighted mode-based estimator, and MR pleiotropy residual sum and outlier methods. Results Genetically predicted higher insulin resistance had a higher odds ratio (OR) of small vessel stroke (OR 1.23; 95% confidence interval [CI] 1.05–1.44; P = 0.01 using BMI-adjusted fasting insulin; OR 1.25; 95% CI 1.07–1.46; P = 0.006 using gold standard measure of insulin resistance) and AD (OR 1.13; 95% CI 1.04–1.23; P = 0.004 using BMI-adjusted fasting insulin; OR 1.02; 95% CI 1.00-1.03; P = 0.03 using gold standard measures of insulin resistance) using the inverse-variance-weighted method. No evidence of pleiotropy was found using MR-Egger regression. Conclusion Our findings provide genetic support for a causal effect of insulin resistance on small vessel stroke and AD. Further investigation on the involved mechanisms is needed.

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.

P5313The association between coffee and caffeine consumption and renal function: insight from individual-level data, Mendelian randomization, and meta-analysis

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
M Mazidi ◽  
D P Mikhailidis ◽  
A Dehghan ◽  
J Jozwiak ◽  
J Rysz ◽  
...  
Keyword(s):  
Renal Function ◽  
Mendelian Randomization ◽  
Meta Analysis ◽  
Coffee Consumption ◽  
Caffeine Intake ◽  
Caffeine Consumption ◽  
Coffee Intake ◽  
Urine Albumin ◽  
Level Data ◽  
Inverse Variance

Abstract Background The reported relationship between coffee intake and renal function is poorly understood. Purpose By applying on two-sample Mendelian randomization (MR) and systematic review and meta-analysis we investigated the association between caffeine and coffee intake with prevalent CKD and markers of renal function. Methods For the individual data analysis we analysed the NHANES data on renal function markers and caffeine intake. MR was implemented by using summary-level data from genome-wide association studies conducted on coffee intake (N=91,462) and kidney function (N=133,413). Inverse variance weighted method (IVW), weighted median-based method, MR-Egger, MR-RAPS, MR-PRESSO were applied. Random effects models and generic inverse variance methods were used for the meta-analysis. Results Finally, we included the data of 18,436 participants, 6.9% had prevalent CKD (based on eGFR). Caffeine intake for general population was 131.1±1.1 mg. The % of pts. with CKD, by caffeine quartile was 16.6% in Q1 (lowest), 13.9% in Q2, 12.2% in Q3 and 11.0% in Q4 (p<0.001). After adjustment, for increasing quartiles for caffeine consumption, mean urine albumin, albumin-creatinine ratio and eGFR did not change significantly (p>0.234). In fully adjusted logistic regression models, there was no significant difference in chances of CKD prevalence (p-trend=0.745) (Table). In the same line, results of MR showed no impact of coffee intake on CKD (IVW=β: −0.0191, SE: 0.069, p=0.781) (Figure), on eGFR (overall= IVW= β: −0.0005, SE: 0.005, p=0.926) both in diabetic (IVW= β: −0.006, SE: 0.009, p=0.478), and non-diabetic patients (IVW= β: −6.772, SE: 0.006, p=0.991). Results from the meta-analysis indicted that coffee consumption was not significantly associated with CKD (OR: 0.85, 95% CI 0.71–1.02, p=0.090, n=6 studies, I2=0.32). These findings were robust in sensitivity analyses. Levels of CKD markers across caffeine Qs Characteristics Quartiles of Caffeine p-value First Second Third Fourth Number of participants (n) 4609 4611 4608 4608 Log Urine Albumin (mg/L) 2.20±0.02 2.16±0.02 2.19±0.02 2.17±0.02 0.239 Serum Creatinine (mg/dL) 0.89±0.003 0.90±0.004 0.91±0.002 0.88±0.003 0.234 Log ACR (mg/g) 2.14±0.02 2.10±0.02 2.11±0.02 2.16±0.02 0.352 eGFR (ml/min/1.73m2) 91.2±0.7 92.8±0.4 90.2±0.5 89.6±0.3 0.415 MR on the impact of coffee intake on CKD Conclusions By implementing on different strategies we have highlighted no significant association between coffee consumption with renal function and chance of CKD. Acknowledgement/Funding None

scholarly journals Causal Effect of Obstructive Sleep Apnea on Atrial Fibrillation: A Mendelian Randomization Study

2021 ◽  
Author(s):  
Weiqi Chen ◽  
Xueli Cai ◽  
Hongyi Yan ◽  
Yuesong Pan
Keyword(s):  
Atrial Fibrillation ◽  
Obstructive Sleep Apnea ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Genome Wide Association ◽  
Sensitivity Analyses ◽  
Genome Wide ◽  
Inverse Variance ◽  
Obstructive Sleep ◽  
Increased Risk

Background Obstructive sleep apnea (OSA) has shown to be associated with an increased risk of atrial fibrillation in observational studies. Whether this association reflect causal effect is still unclear. The aim of this study was to evaluate the causal effect of OSA on atrial fibrillation. Methods and Results We used a 2‐sample Mendelian randomization (MR) method to evaluate the causal effect of OSA on atrial fibrillation. Summary data on genetic variant‐OSA association were obtained from a recently published genome‐wide association studies with up to 217 955 individuals and data on variant‐atrial fibrillation association from another genome‐wide association study with up to 1 030 836 individuals. Effect estimates were evaluated using inverse‐variance weighted method. Other MR analyses, including penalized inverse‐variance weighted, penalized robust inverse‐variance weighted, MR‐Egger, simple median, weighted median, weighted mode‐based estimate and Mendelian Randomization Pleiotropy Residual Sum and Outlier methods were performed in sensitivity analyses. The MR analyses in both the fixed‐effect and random‐effect inverse‐variance weighted models showed that genetically predicted OSA was associated with an increased risk of atrial fibrillation (odds ratio [OR], 1.21; 95% CI, 1.12–1.31, P <0.001; OR, 1.21; 95% CI, 1.11–1.32, P <0.001) using 5 single nucleotide polymorphisms as the instruments. MR‐Egger indicated no evidence of genetic pleiotropy (intercept, −0.014; 95% CI, −0.033 to 0.005, P =0.14). Results were robust using other MR methods in sensitivity analyses. Conclusions This MR analysis found that genetically predicted OSA had causal effect on an increased risk of atrial fibrillation.

scholarly journals Automating Mendelian randomization through machine learning to construct a putative causal map of the human phenome

2017 ◽  
Author(s):  
Gibran Hemani ◽  
Jack Bowden ◽  
Philip Haycock ◽  
Jie Zheng ◽  
Oliver Davis ◽  
...  
Keyword(s):  
Machine Learning ◽  
Causal Inference ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
False Discovery Rates ◽  
Level Data ◽  
Invalid Instruments ◽  
Major Application

AbstractA major application for genome-wide association studies (GWAS) has been the emerging field of causal inference using Mendelian randomization (MR), where the causal effect between a pair of traits can be estimated using only summary level data. MR depends on SNPs exhibiting vertical pleiotropy, where the SNP influences an outcome phenotype only through an exposure phenotype. Issues arise when this assumption is violated due to SNPs exhibiting horizontal pleiotropy. We demonstrate that across a range of pleiotropy models, instrument selection will be increasingly liable to selecting invalid instruments as GWAS sample sizes continue to grow. Methods have been developed in an attempt to protect MR from different patterns of horizontal pleiotropy, and here we have designed a mixture-of-experts machine learning framework (MR-MoE 1.0) that predicts the most appropriate model to use for any specific causal analysis, improving on both power and false discovery rates. Using the approach, we systematically estimated the causal effects amongst 2407 phenotypes. Almost 90% of causal estimates indicated some level of horizontal pleiotropy. The causal estimates are organised into a publicly available graph database (http://eve.mrbase.org), and we use it here to highlight the numerous challenges that remain in automated causal inference.

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