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 Avoiding bias in Mendelian randomization when stratifying on a collider

2021 ◽  
Author(s):  
Claudia Coscia ◽  
Dipender Gill ◽  
Raquel Benítez ◽  
Teresa Pérez ◽  
Núria Malats ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Risk Factor ◽  
Instrumental Variables ◽  
Genetic Variants ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
New Approach ◽  
Unbiased Estimates ◽  
Different Characteristics ◽  
Collider Bias

AbstractBackgroundMendelian randomization (MR) uses genetic variants as instrumental variables to investigate the causal effect of a risk factor on an outcome. A collider is a variable influenced by two or more other variables. Naive calculation of MR estimates in strata of the population defined by a variable affected by the risk factor can result in collider bias.MethodsWe propose an approach that allows MR estimation in strata of the population while avoiding collider bias. This approach constructs a new variable, the residual collider, as the residual from regression of the collider on the genetic instrument, and then calculates causal estimates in strata defined by quantiles of the residual collider. Estimates stratified on the residual collider will typically have an equivalent interpretation to estimates stratified on the collider, but they are not subject to collider bias. We apply the approach in several simulation scenarios considering different characteristics of the collider variable and strengths of the instrument. We then apply the proposed approach to investigate the causal effect of smoking on bladder cancer in strata of the population defined by bodyweight.ResultsThe new approach generated unbiased estimates in all the simulation settings. In the applied example, we observed a trend in the stratum-specific MR estimates at different bodyweight levels that suggested stronger effects of smoking on bladder cancer among individuals with lower bodyweight.ConclusionsThe proposed approach can be used to perform MR studying heterogeneity among subgroups of the population while avoiding collider bias.

scholarly journals Robust Mendelian randomization in the presence of residual population stratification, batch effects and horizontal pleiotropy

2020 ◽  
Author(s):  
Carlos Cinelli ◽  
Nathan LaPierre ◽  
Brian L. Hill ◽  
Sriram Sankararaman ◽  
Eleazar Eskin
Keyword(s):  
Blood Pressure ◽  
Instrumental Variables ◽  
Genetic Variants ◽  
Population Stratification ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Effect Estimate ◽  
Batch Effects ◽  
Genetic Associations ◽  
Worst Case

ABSTRACTMendelian Randomization (MR) exploits genetic variants as instrumental variables to estimate the causal effect of an “exposure” trait on an “outcome” trait from observational data. However, the validity of such studies is threatened by population stratification, batch effects, and horizontal pleiotropy. Although a variety of methods have been proposed to partially mitigate those problems, residual biases may still remain, leading to highly statistically significant false positives in large genetic databases. Here, we describe a suite of sensitivity analysis tools for MR that enables investigators to properly quantify the robustness of their findings against these (and other) unobserved validity threats. Specifically, we propose the routine reporting of sensitivity statistics that can be used to readily quantify the robustness of a MR result: (i) the partial R2 of the genetic instrument with the exposure and the outcome traits; and, (ii) the robustness value of both genetic associations. These statistics quantify the minimal strength of violations of the MR assumptions that would be necessary to explain away the MR causal effect estimate. We also provide intuitive displays to visualize the sensitivity of the MR estimate to any degree of violation, and formal methods to bound the worst-case bias caused by violations in terms of multiples of the observed strength of principal components, batch effects, as well as putative pleiotropic pathways. We demonstrate how these tools can aid researchers in distinguishing robust from fragile findings, by showing that the MR estimate of the causal effect of body mass index (BMI) on diastolic blood pressure is relatively robust, whereas the MR estimate of the causal effect of BMI on Townsend deprivation index is relatively fragile.

scholarly journals A new Mendelian Randomization method to estimate causal effects of multivariable brain imaging exposures

2021 ◽  
Author(s):  
Chen Mo ◽  
Zhenyao Ye ◽  
Hongjie Ke ◽  
Tong Lu ◽  
Travis Canida ◽  
...  
Keyword(s):  
Cognitive Function ◽  
Brain Imaging ◽  
Instrumental Variables ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Imaging Genetics ◽  
Causal Effects ◽  
Imaging Data ◽  
Multiple Exposures ◽  
The Uk

The advent of simultaneously collected imaging-genetics data in large study cohorts provides an unprecedented opportunity to assess the causal effect of brain imaging traits on externally measured experimental results (e.g., cognitive tests) by treating genetic variants as instrumental variables. However, classic Mendelian Randomization methods are limited when handling high-throughput imaging traits as exposures to identify causal effects. We propose a new Mendelian Randomization framework to jointly select instrumental variables and imaging exposures, and then estimate the causal effect of multivariable imaging data on the outcome. We validate the proposed method with extensive data analyses and compare it with existing methods. We further apply our method to evaluate the causal effect of white matter microstructure integrity on cognitive function. The findings suggest that our method achieved better performance regarding sensitivity, bias, and false discovery rate compared to individually assessing the causal effect of a single exposure and jointly assessing the causal effect of multiple exposures without dimension reduction. Our application results indicated that WM measures across different tracts have a joint causal effect that significantly impacts the cognitive function among the participants from the UK Biobank.

scholarly journals Factorial Mendelian randomization: using genetic variants to assess interactions

2019 ◽  
Author(s):  
Jessica MB Rees ◽  
Christopher N Foley ◽  
Stephen Burgess
Keyword(s):  
Risk Factors ◽  
Randomized Trial ◽  
Instrumental Variables ◽  
Genetic Variants ◽  
Mendelian Randomization ◽  
Real Data ◽  
List Type ◽  
Pharmacological Interventions ◽  
Product Term ◽  
Natural Break

AbstractBackgroundFactorial Mendelian randomization is the use of genetic variants to answer questions about interactions. Although the approach has been used in applied investigations, little methodological advice is available on how to design or perform a factorial Mendelian randomization analysis. Previous analyses have employed a 2 × 2 approach, using dichotomized genetic scores to divide the population into 4 subgroups as in a factorial randomized trial.MethodsWe describe two distinct contexts for factorial Mendelian randomization: investigating interactions between risk factors, and investigating interactions between pharmacological interventions on risk factors. We propose two-stage least squares methods using all available genetic variants and their interactions as instrumental variables, and using continuous genetic scores as instrumental variables rather than dichotomized scores. We illustrate our methods using data from UK Biobank to investigate the interaction between body mass index and alcohol consumption on systolic blood pressure.ResultsSimulated and real data show that efficiency is maximized using the full set of interactions between genetic variants as instruments. In the applied example, between four- and ten-fold improvement in efficiency is demonstrated over the 2 × 2 approach. Analyses using continuous genetic scores are more efficient than those using dichotomized scores. Efficiency is improved by finding genetic variants that divide the population at a natural break in the distribution of the risk factor, or else divide the population into more equal sized groups.ConclusionsPrevious factorial Mendelian randomization analyses may have been under-powered. Efficiency can be improved by using all genetic variants and their interactions as instrumental variables, rather than the 2 × 2 approach.Key messagesFactorial Mendelian randomization is an extension of the Mendelian randomization paradigm to answer questions about interactions.There are two contexts in which factorial Mendelian randomization can be used: for investigating interactions between risk factors, and interactions between pharmacological interventions on risk factors.While most applications of factorial Mendelian randomization have dichotomized the population as in a 2 × 2 factorial randomized trial, this approach is generally inefficient for detecting statistical interactions.In the first context, efficiency is maximized by including all genetic variants and their cross-terms as instrumental variables for the two risk factors and their product term.In the second context, efficiency is maximized by using continuous genetic scores rather than dichotomized scores.

scholarly journals Constrained Instruments and their Application to Mendelian Randomization with Pleiotropy

2017 ◽  
Author(s):  
Lai Jiang ◽  
Karim Oualkacha ◽  
Vanessa Didelez ◽  
Antonio Ciampi ◽  
Pedro Rosa ◽  
...  
Keyword(s):  
Instrumental Variables ◽  
Genetic Variants ◽  
Instrumental Variable ◽  
Genetic Variant ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Naive Method ◽  
Effect Estimation ◽  
Selection Of ◽  
Better Than

AbstractIn Mendelian randomization (MR), genetic variants are used to construct instrumental variables, which enable inference about the causal relationship between a phenotype of interest and a response or disease outcome. However, standard MR inference requires several assumptions, including the assumption that the genetic variants only influence the response through the phenotype of interest. Pleiotropy occurs when a genetic variant has an effect on more than one phenotype; therefore, a pleiotropic genetic variant may be an invalid instrumental variable. Hence, a naive method for constructing instrumental variables may lead to biased estimation of the causality between the phenotype and the response. Here, we present a set of intuitive methods (Constrained Instrumental Variable methods [CIV]) to construct valid instrumental variables and perform adjusted causal effect estimation when pleiotropy exists, focusing particularly on the situation where pleiotropic phenotypes have been measured. Our approach includes an automatic and valid selection of genetic variants when building the instrumental variables. We also provide details of the features of many existing methods, together with a comparison of their performance in a large series of simulations. CIV methods performed consistently better than many comparators across four different pleiotropic violations of the MR assumptions. We analyzed data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) Mueller et al. (2005) to disentangle causal relationships of several biomarkers with AD progression. The results showed that CIV methods can provide causal effect estimates, as well as selection of valid instruments while accounting for pleiotropy.

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 Using Mendelian randomization to determine causal effects of maternal pregnancy (intrauterine) exposures on offspring outcomes: Sources of bias and methods for assessing them

2017 ◽  
Vol 2 ◽  
pp. 11 ◽  
Author(s):  
Deborah A. Lawlor ◽  
Rebecca Richmond ◽  
Nicole Warrington ◽  
George McMahon ◽  
George Davey Smith ◽  
...  
Keyword(s):  
Genetic Variants ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Strong Association ◽  
Simulated Data ◽  
Real Data ◽  
Causal Effects ◽  
Sensitivity Analyses ◽  
Specific Situation ◽  
Exclusion Restriction

Mendelian randomization (MR), the use of genetic variants as instrumental variables (IVs) to test causal effects, is increasingly used in aetiological epidemiology. Few of the methodological developments in MR have considered the specific situation of using genetic IVs to test the causal effect of exposures in pregnant women on postnatal offspring outcomes. In this paper, we describe specific ways in which the IV assumptions might be violated when MR is used to test such intrauterine effects. We highlight the importance of considering the extent to which there is overlap between genetic variants in offspring that influence their outcome with genetic variants used as IVs in their mothers. Where there is overlap, and particularly if it generates a strong association of maternal genetic IVs with offspring outcome via the offspring genotype, the exclusion restriction assumption of IV analyses will be violated. We recommend a set of analyses that ought to be considered when MR is used to address research questions concerned with intrauterine effects on post-natal offspring outcomes, and provide details of how these can be undertaken and interpreted. These additional analyses include the use of genetic data from offspring and fathers, examining associations using maternal non-transmitted alleles, and using simulated data in sensitivity analyses (for which we provide code). We explore the extent to which new methods that have been developed for exploring violation of the exclusion restriction assumption in the two-sample setting (MR-Egger and median based methods) might be used when exploring intrauterine effects in one-sample MR. We provide a list of recommendations that researchers should use when applying MR to test the effects of intrauterine exposures on postnatal offspring outcomes and use an illustrative example with real data to demonstrate how our recommendations can be applied and subsequent results appropriately interpreted.

scholarly journals Inferring the direction of a causal link and estimating its effect via a Bayesian Mendelian randomization approach

2019 ◽  
Vol 29 (4) ◽  
pp. 1081-1111 ◽  
Author(s):  
Ioan Gabriel Bucur ◽  
Tom Claassen ◽  
Tom Heskes
Keyword(s):  
Instrumental Variables ◽  
Genetic Variants ◽  
Bayesian Model Averaging ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Model Averaging ◽  
Causal Link ◽  
Reverse Direction ◽  
Causal Pathways ◽  
Health Related

The use of genetic variants as instrumental variables – an approach known as Mendelian randomization – is a popular epidemiological method for estimating the causal effect of an exposure (phenotype, biomarker, risk factor) on a disease or health-related outcome from observational data. Instrumental variables must satisfy strong, often untestable assumptions, which means that finding good genetic instruments among a large list of potential candidates is challenging. This difficulty is compounded by the fact that many genetic variants influence more than one phenotype through different causal pathways, a phenomenon called horizontal pleiotropy. This leads to errors not only in estimating the magnitude of the causal effect but also in inferring the direction of the putative causal link. In this paper, we propose a Bayesian approach called BayesMR that is a generalization of the Mendelian randomization technique in which we allow for pleiotropic effects and, crucially, for the possibility of reverse causation. The output of the method is a posterior distribution over the target causal effect, which provides an immediate and easily interpretable measure of the uncertainty in the estimation. More importantly, we use Bayesian model averaging to determine how much more likely the inferred direction is relative to the reverse direction.

scholarly journals Genetic predictors of participation in optional components of UK Biobank

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Jessica Tyrrell ◽  
Jie Zheng ◽  
Robin Beaumont ◽  
Kathryn Hinton ◽  
Tom G. Richardson ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Genetic Variants ◽  
Genetic Factors ◽  
Mendelian Randomization ◽  
Genetic Correlations ◽  
Mr Studies ◽  
Uk Biobank ◽  
Factors Affecting ◽  
Participation Bias ◽  
Genetic Predictors

AbstractLarge studies such as UK Biobank are increasingly used for GWAS and Mendelian randomization (MR) studies. However, selection into and dropout from studies may bias genetic and phenotypic associations. We examine genetic factors affecting participation in four optional components in up to 451,306 UK Biobank participants. We used GWAS to identify genetic variants associated with participation, MR to estimate effects of phenotypes on participation, and genetic correlations to compare participation bias across different studies. 32 variants were associated with participation in one of the optional components (P < 6 × 10−9), including loci with links to intelligence and Alzheimer’s disease. Genetic correlations demonstrated that participation bias was common across studies. MR showed that longer educational duration, older menarche and taller stature increased participation, whilst higher levels of adiposity, dyslipidaemia, neuroticism, Alzheimer’s and schizophrenia reduced participation. Our effect estimates can be used for sensitivity analysis to account for selective participation biases in genetic or non-genetic analyses.

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.

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