scholarly journals On Multiply Robust Mendelian Randomization (MR2) With Many Invalid Genetic Instruments

2021 ◽  
Author(s):  
Baoluo Sun ◽  
Zhonghua Liu ◽  
Eric Tchetgen Tchetgen
Keyword(s):  
Large Scale ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
New Class ◽  
Large Scale Analysis ◽  
Empirical Performance ◽  
Class Of Estimators ◽  
Semiparametric Theory ◽  
Semiparametric Efficiency Bound ◽  
Special Case

Mendelian randomization (MR) is a popular instrumental variable (IV) approach, in which genetic markers are used as IVs. In order to improve efficiency, multiple markers are routinely used in MR analyses, leading to concerns about bias due to possible violation of IV exclusion restriction of no direct effect of any IV on the outcome other than through the exposure in view. To address this concern, we introduce a new class of Multiply Robust MR (MR2) estimators that are guaranteed to remain consistent for the causal effect of interest provided that at least one genetic marker is a valid IV without necessarily knowing which IVs are invalid. We show that the proposed MR2 estimators are a special case of a more general class of estimators that remain consistent provided that a set of at least k† out of K candidate instrumental variables are valid, for k† ≤ K set by the analyst ex ante, without necessarily knowing which IVs are invalid. We provide formal semiparametric theory supporting our results, and characterize the semiparametric efficiency bound for the exposure causal effect which cannot be improved upon by any regular estimator with our favorable robustness property. We conduct extensive simulation studies and apply our methods to a large-scale analysis of UK Biobank data, demonstrating the superior empirical performance of MR2 compared to competing MR methods.

scholarly journals Using a Two-Sample Mendelian Randomization Method in Assessing the Causal Relationships Between Human Blood Metabolites and Heart Failure

2021 ◽  
Vol 8 ◽  
Author(s):  
Zixian Wang ◽  
Shiyu Chen ◽  
Qian Zhu ◽  
Yonglin Wu ◽  
Guifeng Xu ◽  
...  
Keyword(s):  
Heart Failure ◽  
Human Blood ◽  
Large Scale ◽  
Genome Wide Association Study ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Meta Analysis ◽  
Sensitivity Analyses ◽  
Blood Metabolites ◽  
Causal Relationships

Background: Heart failure (HF) is the main cause of morbidity and mortality worldwide, and metabolic dysfunction is an important factor related to HF pathogenesis and development. However, the causal effect of blood metabolites on HF remains unclear.Objectives: Our chief aim is to investigate the causal relationships between human blood metabolites and HF risk.Methods: We used an unbiased two-sample Mendelian randomization (MR) approach to assess the causal relationships between 486 human blood metabolites and HF risk. Exposure information was obtained from Sample 1, which is the largest metabolome-based genome-wide association study (mGWAS) data containing 7,824 Europeans. Outcome information was obtained from Sample 2, which is based on the results of a large-scale GWAS meta-analysis of HF and contains 47,309 cases and 930,014 controls of Europeans. The inverse variance weighted (IVW) model was used as the primary two-sample MR analysis method and followed the sensitivity analyses, including heterogeneity test, horizontal pleiotropy test, and leave-one-out analysis.Results: We observed that 11 known metabolites were potentially related to the risk of HF after using the IVW method (P < 0.05). After adding another four MR models and performing sensitivity analyses, we found a 1-SD increase in the xenobiotics 4-vinylphenol sulfate was associated with ~22% higher risk of HF (OR [95%CI], 1.22 [1.07–1.38]).Conclusions: We revealed that the 4-vinylphenol sulfate may nominally increase the risk of HF by 22% after using a two-sample MR approach. Our findings may provide novel insights into the pathogenesis underlying HF and novel strategies for HF prevention.

scholarly journals Parathyroid Hormone and Bone Mineral Density: A Mendelian Randomization Study

2020 ◽  
Vol 105 (11) ◽  
Author(s):  
Zihao Qu ◽  
Fangkun Yang ◽  
Jianqiao Hong ◽  
Wei Wang ◽  
Shigui Yan
Keyword(s):  
Bone Mineral Density ◽  
Parathyroid Hormone ◽  
Bone Mineral ◽  
Large Scale ◽  
Genome Wide Association Study ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Age Groups ◽  
Mineral Density ◽  
Serum Pth

Abstract Purpose Accumulating evidence implicates parathyroid hormone (PTH) in the development of osteoporosis. However, the causal effect of PTH on bone mineral density (BMD) remains unclear. Thus, this study is aimed at exploring the association between the concentrations of serum PTH and BMD. Methods The instrumental variables for PTH were selected from a large-scale genome-wide association study (GWAS; n = 29 155). Outcomes included BMD of the forearm (FA; n = 8143), femoral neck (FN; n = 33 297), lumbar spine (LS; n = 32 735), heel (HL; n = 394 929), and risk of fractures in these bones (n = 361 194). Furthermore, the BMD of 5 different age groups: 15 years or younger (n = 11 807), 15–30 (n = 4180), 30–45 (n = 10 062), 45–60 (n = 18 805), and 60 years or older (n = 22 504) were extracted from a GWAS meta-analysis study. The analyses were performed using the 2-sample Mendelian randomization method. Results Mendelian randomization analysis revealed that the level of serum PTH was inversely associated with BMD of FA (95% CI: -0.763 to -0.016), FN (95% CI: -0.669 to -0.304), and LS (95% CI: -0.667 to -0.243). A causal relationship between serum PTH levels and BMD was observed in individuals aged 30–45 (95% CI: -0.888 to -0.166), 45–60 (95% CI: -0.758 to -0.232), and over 60 years (95% CI: -0.649 to -0.163). Main Conclusions This study demonstrated that the concentrations of serum PTH is inversely associated with BMD of several bones. Further analysis revealed site- and age-specific correlations between serum PTH levels and BMD, which implies that the levels of serum PTH contribute to the development of osteoporosis.

scholarly journals Inflammatory Cytokines and Risk of Ischemic Stroke: A Mendelian Randomization Study

2022 ◽  
Vol 12 ◽  
Author(s):  
Yalan Li ◽  
Jun Lu ◽  
Jie Wang ◽  
Peizhi Deng ◽  
Changjiang Meng ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Inflammatory Cytokines ◽  
Large Scale ◽  
Genome Wide Association Study ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Meta Analysis ◽  
Outcome Data ◽  
Sensitivity Analyses ◽  
A Genome

Background: Observational studies have revealed the association between some inflammatory cytokines and the occurrence of ischemic stroke, but the causal relationships remain unclear.Methods: We conducted a two-sample Mendelian randomization (MR) analysis to assess the causal effects of thirty inflammatory cytokines and the risk of ischemic stroke. For exposure data, we collected genetic variants associated with inflammatory cytokines as instrumental variables (IVs) from a genome-wide association study (GWAS) meta-analysis from Finland (sample size up to 8,293). For the outcome data, we collected summary data of ischemic stroke from a large-scale GWAS meta-analysis involved 17 studies (34,217 cases and 406,111 controls). We further performed a series of sensitivity analyses as validation of primary MR results.Results: According to the primary MR estimations and further sensitivity analyses, we established one robust association after Bonferroni correction: the odds ratio (95% CI) per unit change in genetically increased IL-4 was 0.84 (0.89–0.95) for ischemic stroke. The chemokine MCP3 showed a nominally significant association with ischemic stroke risk (OR: 0.93, 95% CI: 0.88–0.99, unadjusted p < 0.05). There was no evidence of a causal effect of other inflammatory cytokines and the risk of ischemic stroke.Conclusions: Our study suggested that genetically increased IL-4 levels showed a protective effect on the risk of ischemic stroke, which provides important new insights into the potential therapeutic target for preventing ischemic stroke.

scholarly journals Pleiotropy-robust Mendelian Randomization

2016 ◽  
Author(s):  
Hans van Kippersluis ◽  
Cornelius A Rietveld
Keyword(s):  
Sensitivity Analysis ◽  
Instrumental Variables ◽  
Genetic Variants ◽  
Large Scale ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Pleiotropic Effects ◽  
List Type ◽  
Exclusion Restriction ◽  
The Uk

AbstractBackgroundThe potential of Mendelian Randomization studies is rapidly expanding due to (i) the growing power of GWAS meta-analyses to detect genetic variants associated with several exposures, and (ii) the increasing availability of these genetic variants in large-scale surveys. However, without a proper biological understanding of the pleiotropic working of genetic variants, a fundamental assumption of Mendelian Randomization (the exclusion restriction) can always be contested.MethodsWe build upon and synthesize recent advances in the econometric literature on instrumental variables (IV) estimation that test and relax the exclusion restriction. Our Pleiotropy-robust Mendelian Randomization (PRMR) method first estimates the degree of pleiotropy, and in turn corrects for it. If a sample exists for which the genetic variants do not affect the exposure, and pleiotropic effects are homogenous, PRMR obtains unbiased estimates of causal effects in case of pleiotropy.ResultsSimulations show that existing MR methods produce biased estimators for realistic forms of pleiotropy. Under the aforementioned assumptions, PRMR produces unbiased estimators. We illustrate the practical use of PRMR by estimating the causal effect of (i) cigarettes smoked per day on Body Mass Index (BMI); (ii) prostate cancer on self-reported health, and (iii) educational attainment on BMI in the UK Biobank data.ConclusionsPRMR allows for instrumental variables that violate the exclusion restriction due to pleiotropy, and corrects for pleiotropy in the estimation of the causal effect. If the degree of pleiotropy is unknown, PRMR can still be used as a sensitivity analysis.Key messagesIf genetic variants have pleiotropic effects, causal estimates of Mendelian Randomization studies will be biased.Pleiotropy-robust Mendelian Randomization (PRMR) produces unbiased causal estimates in case (i) a subsample can be identified for which the genetic variants do not affect the exposure, and (ii) pleiotropic effects are homogenous.If such a subsample does not exist, PRMR can still routinely be reported as a sensitivity analysis in any MR analysis.If pleiotropic effects are not homogenous, PRMR can be used as an informal test to gauge the exclusion restriction.

scholarly journals Elucidating the role of maternal environmental exposures on offspring health and disease using two-sample Mendelian randomization

2019 ◽  
Vol 48 (3) ◽  
pp. 861-875 ◽  
Author(s):  
David M Evans ◽  
Gunn-Helen Moen ◽  
Liang-Dar Hwang ◽  
Debbie A Lawlor ◽  
Nicole M Warrington
Keyword(s):  
Large Scale ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Environmental Exposures ◽  
Individual Level ◽  
Public Sharing ◽  
Health Related ◽  
Offspring Genotype ◽  
Risk Of Disease ◽  
Offspring Health

Abstract Background There is considerable interest in estimating the causal effect of a range of maternal environmental exposures on offspring health-related outcomes. Previous attempts to do this using Mendelian randomization methodologies have been hampered by the paucity of epidemiological cohorts with large numbers of genotyped mother–offspring pairs. Methods We describe a new statistical model that we have created which can be used to estimate the effect of maternal genotypes on offspring outcomes conditional on offspring genotype, using both individual-level and summary-results data, even when the extent of sample overlap is unknown. Results We describe how the estimates obtained from our method can subsequently be used in large-scale two-sample Mendelian randomization studies to investigate the causal effect of maternal environmental exposures on offspring outcomes. This includes studies that aim to assess the causal effect of in utero exposures related to fetal growth restriction on future risk of disease in offspring. We illustrate our framework using examples related to offspring birthweight and cardiometabolic disease, although the general principles we espouse are relevant for many other offspring phenotypes. Conclusions We advocate for the establishment of large-scale international genetics consortia that are focused on the identification of maternal genetic effects and committed to the public sharing of genome-wide summary-results data from such efforts. This information will facilitate the application of powerful two-sample Mendelian randomization studies of maternal exposures and offspring outcomes.

scholarly journals Causal Association Between Periodontitis and Type 2 Diabetes: A Bidirectional Two-Sample Mendelian Randomization Analysis

2022 ◽  
Vol 12 ◽  
Author(s):  
Yong-Bo Wang ◽  
Si-Yu Yan ◽  
Xu-Hui Li ◽  
Qiao Huang ◽  
Li-Sha Luo ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Large Scale ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Association Studies ◽  
European Ancestry ◽  
Genome Wide Association Studies ◽  
Causal Association ◽  
Bidirectional Association

Background: Previous observational studies have reported a bidirectional association between periodontitis and type 2 diabetes, but the causality of these relationships remains unestablished. We clarified the bidirectional causal association through two-sample Mendelian randomization (MR).Methods: We obtained summary-level data for periodontitis and type 2 diabetes from several published large-scale genome-wide association studies (GWAS) of individuals of European ancestry. For the casual effect of periodontitis on type 2 diabetes, we used five independent single-nucleotide polymorphisms (SNPs) specific to periodontitis from three GWAS. The summary statistics for the associations of exposure-related SNPs with type 2 diabetes were drawn from the GWAS in the Diabetes Genetics Replication and Meta-analysis (DIAGRAM) consortium and the FinnGen consortium R5 release, respectively. For the reversed causal inference, 132 and 49 SNPs associated with type 2 diabetes from the DIAGRAM consortium and the FinnGen consortium R5 release were included, and the summary-level statistics were obtained from the Gene-Lifestyle Interactions in Dental Endpoints consortium. Multiple approaches of MR were carried out.Results: Periodontitis was not causally related with the risk of type 2 diabetes (all p > 0.05). No causal effect of type 2 diabetes on periodontitis was found (all p > 0.05). Estimates were consistent across multiple MR analyses.Conclusion: This study based on genetic data does not support a bidirectional causal association between periodontitis and type 2 diabetes.

scholarly journals The causal role of circulating vitamin D concentrations in human complex traits and diseases: a large-scale Mendelian randomization study

2019 ◽  
Author(s):  
Xia Jiang ◽  
Tian Ge ◽  
Chia-Yen Chen
Keyword(s):  
Multiple Sclerosis ◽  
Vitamin D ◽  
Complex Traits ◽  
Large Scale ◽  
Randomized Clinical Trials ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
European Ancestry ◽  
Effect Estimate ◽  
Human Complex

AbstractVitamin D has been associated with a variety of human complex traits and diseases in observational studies, but a causal relationship remains unclear. To examine a putative causal effect of vitamin D across phenotypic domains and disease categories, we conducted Mendelian randomization (MR) analyses using genetic instruments associated with circulating 25-hydroxyvitamin D [25(OH)D] concentrations. We leveraged genome-wide significant 25(OH)D-associated SNPs (N=138) from a meta-analysis combining a vitamin D GWAS conducted in 401,460 white British UK Biobank (UKBB) participants and an independent vitamin D GWAS including 42,274 samples of European ancestry, and examined 190 large-scale health-related GWAS spanning a broad spectrum of complex traits, diseases and biomarkers. We applied multiple MR methods to estimate the causal effect of vitamin D while testing and controlling for potential biases from horizontal pleiotropy. Consistent with previous findings, genetically predicted increased 25(OH)D levels significantly decreased the risk of multiple sclerosis (OR=0.824; 95%CI=0.689-0.986). The protective effect estimate was consistent across different MR methods and four different multiple sclerosis GWAS with varying sample sizes and genotyping platforms. On the contrary, we found limited evidence in support of a causal effect of 25(OH)D on anthropometric traits, obesity, cognitive function, sleep behavior, breast and prostate cancer, and autoimmune, cardiovascular, metabolic, neurological and psychiatric traits and diseases, and blood biomarkers. Our results may inform ongoing and future randomized clinical trials of vitamin D supplementation.

scholarly journals Mendelian randomization study shows no causal effects of serum urate levels on the risk of MS

2020 ◽  
Vol 8 (1) ◽  
pp. e920
Author(s):  
Adil Harroud ◽  
J. Brent Richards ◽  
Sergio E. Baranzini
Keyword(s):  
Large Scale ◽  
Genome Wide Association Study ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Meta Analysis ◽  
Serum Urate ◽  
Sensitivity Analyses ◽  
Data Sets ◽  
A Genome ◽  
Mr Study

ObjectiveTo examine whether lifelong genetically increased serum urate levels, a potent antioxidant, contribute to MS susceptibility using Mendelian randomization (MR).MethodsThis 2-sample MR study included 25 independent genetic variants strongly associated with serum urate levels in a genome-wide association study meta-analysis of 140,949 individuals. Effects on the risk of MS were assessed with summary statistics from 3 large-scale MS genetic data sets totaling 61,667 MS cases and 86,806 controls from the International MS Genetic Consortium. Multiple sensitivity analyses were performed to evaluate the assumptions of MR and remove potentially pleiotropic variants.ResultsUsing inverse-variance weighted MR, we found no evidence for a causal effect of serum urate level on the risk of MS in any of the cohorts (MS1: OR 0.99 per each mg/dL unit increase in urate, 95% CI 0.89–1.08, p = 0.76; MS2: OR = 0.99, 95% CI 0.89–1.11, p = 0.90; MS3: OR = 1.00, 95% CI 0.98–1.2, p = 0.91). Pleiotropy robust MR methods yielded consistent estimates.ConclusionThis MR study does not support a clinically relevant causal effect of serum urate levels on the risk of MS.

scholarly journals Evaluation of the causal effects of blood lipid levels on gout with summary level GWAS data: two-sample Mendelian randomization and mediation analysis

2019 ◽  
Author(s):  
Xinghao Yu ◽  
Haimiao Chen ◽  
Shuiping Huang ◽  
Ping Zeng
Keyword(s):  
Mediation Analysis ◽  
Large Scale ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Association Studies ◽  
Serum Urate ◽  
Mediation Effect ◽  
Independent Effect ◽  
List Type ◽  
Genome Wide Association Studies

AbstractObjectiveMany observational studies have identified that gout patients are often comorbid with dyslipidemia, which is typically characterized by a decrease in high-density lipoprotein cholesterol (HDL) and an increase in triglycerides (TG). However, the relationship between dyslipidemia and gout is still unclear.MethodsWe first performed a two-sample Mendelian randomization (MR) to evaluate the causal effect of four lipid traits on gout and serum urate based on summary association statistics available from large scale genome-wide association studies (up to ∼100,000 for lipid, 69,374 for gout and 110,347 for serum urate). We adopted multivariable Mendelian randomization to estimate the causal effect independently. We also assessed the mediated effect by serum urate between lipids and gout with a mediation analysis. The MR results were validated with extensive sensitive analyses.ResultsGenetically lower HDL was positively associated with the risk of gout and serum urate concentration. Each standard deviation (SD) (∼12.26 mg/dL) increase was genetically associated with an odds ratio of gout of 0.75 (95% CI 0.62 ∼ 0.91, p = 3.31E-3) and with a 0.09 mg/dL (95% CI: -0.12 ∼ -0.05, p = 7.00E-04) decrease in serum urate concentration. Genetically higher TG was positively associated with the serum urate concentration. Each SD (∼112.33 mg/dL) increase was genetically associated with a 0.10 mg/dL (95% CI: 0.06 ∼ 0.14, p = 9.87E-05) increase in serum urate concentration. Those results were robust against various sensitive analyses. In addition, the multivariable Mendelian randomization confirmed the independent effect of HDL and TG on the gout/serum urate after adjustment for the other lipids. Finally, the mediation analysis showed that both HDL and TG could indirectly affect gout morbidity via the pathway of serum urate. The mediation effect accounted for about 13.0% or 28.0% of the total effect of HDL and TG, respectively.ConclusionOur study confirmed the causal associations between HDL/TG and gout/serum urate. Furthermore, the effect of HDL or TG on gout could also be mediated by serum urate.Key MessagesEpidemiological studies have identified an accompanying association between lipid and gout. However, whether the association is causal is unclear.Mendelian randomization with genetic variants as instrumental variables is a useful tool facilitate the validation of a causal relationship for modifiable risk factors.The direct and indirect effects of lipids on gout, controlling for the serum urate concentration, can be estimated by a mediation analysis with serum urate serving as a mediator.We confirmed that elevated HDL levels can directly and indirectly lead to the decreased risk of gout, whereas elevation of TG levels can directly and indirectly elevate the risk of gout.

scholarly journals Examining the Causal Inference of Leptin and Soluble Plasma Leptin Receptor Levels on Schizophrenia: A Mendelian Randomization Study

2021 ◽  
Vol 12 ◽  
Author(s):  
Guoqing Chen ◽  
Qiuling Wang ◽  
Ranran Xue ◽  
Xia Liu ◽  
Hao Yu
Keyword(s):  
Large Scale ◽  
Leptin Receptor ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Association Studies ◽  
Sensitivity Analyses ◽  
European Ancestry ◽  
Genome Wide Association Studies ◽  
Unit Increase ◽  
Plasma Leptin

Background: Observational studies that have supported the role of the leptin level in schizophrenia (SCZ) risk are conflicting. Therefore, we performed a two-sample Mendelian randomization (MR) analysis to investigate whether the circulating leptin and soluble plasma leptin receptor (sOB-R) levels play a causal role in SCZ risk.Methods: We first selected five independent single-nucleotide polymorphisms (SNPs) associated with the circulating leptin level and three independent SNPs associated with the sOB-R level from two genome-wide association studies (GWASs) of European individuals. Then, we extracted their associations with SCZ using a large-scale GWAS that consisted of 40,675 patients with SCZ and 64,643 controls of European ancestry. We performed an MR analysis using the inverse variance-weighted (IVW) method to examine the causal effect of leptin on SCZ risk. Moreover, we performed sensitivity analyses to verify our MR results using the weighted median and MR-Egger methods.Results: According to the IVW method, genetically predicted circulating leptin levels were not associated with SCZ risk (OR = 1.98, for per 1-SD unit increase in leptin level; 95% CI, 0.87–4.53; p = 0.10). In addition, the sOB-R level showed no causal effect on the SCZ risk using IVW (OR = 0.98 for per 1-SD unit increase in sOB-R level; 95% CI, 0.97–1.00; p = 0.06). Our sensitivity analysis results confirmed our MR findings.Conclusions: By estimating the causal effect of leptin on SCZ risk using the MR methods, we identified no effect of genetically predicted circulating leptin or the sOB-R level on SCZ. As such, our study suggests that leptin might not be a risk factor for SCZ.

Sign in / Sign up

Export Citation Format

Share Document