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 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 Inference about causation from examination of familial confounding (ICE FALCON): a model for assessing causation analogous to Mendelian randomization

2020 ◽  
Vol 49 (4) ◽  
pp. 1259-1269
Author(s):  
Shuai Li ◽  
Minh Bui ◽  
John L Hopper
Keyword(s):  
Genetic Variants ◽  
Instrumental Variable ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
A Priori ◽  
Genetic Data ◽  
Regression Coefficients ◽  
Genetic Knowledge ◽  
Wide Range ◽  
Related Individuals

Abstract Background We developed a method to make Inference about Causation from Examination of FAmiliaL CONfounding (ICE FALCON) using observational data for related individuals and considering changes in a pair of regression coefficients. ICE FALCON has some similarities to Mendelian randomization (MR) but uses in effect all the familial determinants of the exposure, not just those captured by measured genetic variants, and does not require genetic data nor make strong assumptions. ICE FALCON can assess tracking of a measure over time, an issue often difficult to assess using MR due to lack of a valid instrumental variable. Methods We describe ICE FALCON and present two empirical applications with simulations. Results We found evidence consistent with body mass index (BMI) having a causal effect on DNA methylation at the ABCG1 locus, the same conclusion as from MR analyses but providing about 2.5 times more information per subject. We found evidence that tracking of BMI is consistent with longitudinal causation, as well as familial confounding. The simulations supported the validity of ICE FALCON. Conclusions There are conceptual similarities between ICE FALCON and MR, but empirically they are giving similar conclusions with possibly more information per subject from ICE FALCON. ICE FALCON can be applied to circumstances in which MR cannot be applied, such as when there is no a priori genetic knowledge and/or data available to create a valid instrumental variable, or when the assumptions underlying MR analysis are suspect. ICE FALCON could provide insights into causality for a wide range of public health questions.

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 The causal effect of adiposity on hospital costs: Mendelian Randomization analysis of over 300,000 individuals from the UK Biobank

2019 ◽  
Author(s):  
Padraig Dixon ◽  
William Hollingworth ◽  
Sean Harrison ◽  
Neil M Davies ◽  
George Davey Smith
Keyword(s):  
Genetic Variants ◽  
Instrumental Variable ◽  
Healthcare Costs ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Hospital Costs ◽  
Overweight And Obesity ◽  
Effect Sizes ◽  
Marginal Effect ◽  
Omitted Variables

AbstractEstimates of the marginal effect of measures of adiposity such as body mass index (BMI) on healthcare costs are important for the formulation and evaluation of policies targeting adverse weight profiles. Many existing estimates of this association are affected by endogeneity bias caused by simultaneity, measurement error and omitted variables. The contribution of this study is to avoid this bias by using a novel identification strategy – random germline genetic variation in an instrumental variable analysis – to identify the presence and magnitude of the causal effect of BMI on inpatient hospital costs. We also use data on genetic variants to undertake much richer testing of the sensitivity of results to potential violations of the instrumental variable assumptions than is possible with existing approaches. Using data on over 300,000 individuals, we found effect sizes for the marginal unit of BMI more than 50% larger than multivariable effect sizes. These effects attenuated under sensitivity analyses, but remained larger than multivariable estimates for all but one estimator. There was little evidence for non-linear effects of BMI on hospital costs. Within-family estimates, intended to address dynastic biases, were null but suffered from low power. This paper is the first to use genetic variants in a Mendelian Randomization framework to estimate the causal effect of BMI (or any other disease/trait) on healthcare costs. This type of analysis can be used to inform the cost-effectiveness of interventions and policies targeting the prevention and treatment of overweight and obesity, and for setting research priorities.

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 Mendelian Randomization with Instrumental Variable Synthesis (IVY)

2019 ◽  
Author(s):  
Zhaobin Kuang ◽  
Aldo Cordova-Palomera ◽  
Fred Sala ◽  
Sen Wu ◽  
Jared Dunnmon ◽  
...  
Keyword(s):  
Graphical Models ◽  
Instrumental Variable ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Uk Biobank ◽  
Inference Method ◽  
Real World Data ◽  
Individual Level ◽  
Artery Disease ◽  
Effect Estimation

SUMMARYMendelian Randomization (MR) is an important causal inference method primarily used in biomedical research. This work applies contemporary techniques in machine learning to improve the robustness and power of traditional MR tools. By denoising and combining candidate genetic variants through techniques from unsupervised probabilistic graphical models, an influential latent instrumental variable is constructed for causal effect estimation. We present results on identifying relationships between biomarkers and the occurrence of coronary artery disease using individual-level real-world data from UK-BioBank via the proposed method. The approach, termed Instrumental Variable sYnthesis (IVY) is proposed as a complement to current methods, and is able to improve results based on allele scoring, particularly at moderate sample sizes.

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 A Novel Method for Mendelian Randomization Analyses With Pleiotropy and Linkage Disequilibrium in Genetic Variants From Individual Data

2021 ◽  
Vol 12 ◽  
Author(s):  
Yuquan Wang ◽  
Tingting Li ◽  
Liwan Fu ◽  
Siqian Yang ◽  
Yue-Qing Hu
Keyword(s):  
Linkage Disequilibrium ◽  
Genetic Variants ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Association Studies ◽  
Random Effect ◽  
Pleiotropic Effect ◽  
Genome Wide Association Studies ◽  
The Novel ◽  
Novel Method

Mendelian randomization makes use of genetic variants as instrumental variables to eliminate the influence induced by unknown confounders on causal estimation in epidemiology studies. However, with the soaring genetic variants identified in genome-wide association studies, the pleiotropy, and linkage disequilibrium in genetic variants are unavoidable and may produce severe bias in causal inference. In this study, by modeling the pleiotropic effect as a normally distributed random effect, we propose a novel mixed-effects regression model-based method PLDMR, pleiotropy and linkage disequilibrium adaptive Mendelian randomization, which takes linkage disequilibrium into account and also corrects for the pleiotropic effect in causal effect estimation and statistical inference. We conduct voluminous simulation studies to evaluate the performance of the proposed and existing methods. Simulation results illustrate the validity and advantage of the novel method, especially in the case of linkage disequilibrium and directional pleiotropic effects, compared with other methods. In addition, by applying this novel method to the data on Atherosclerosis Risk in Communications Study, we conclude that body mass index has a significant causal effect on and thus might be a potential risk factor of systolic blood pressure. The novel method is implemented in R and the corresponding R code is provided for free download.

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.

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