Network Mendelian randomization: using genetic variants as instrumental variables to investigate mediation in causal pathways

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.

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Factorial Mendelian randomization: using genetic variants to assess interactions

International Journal of Epidemiology ◽

10.1093/ije/dyz161 ◽

2019 ◽

Vol 49 (4) ◽

pp. 1147-1158 ◽

Cited By ~ 6

Author(s):

Jessica M B Rees ◽

Christopher N Foley ◽

Stephen Burgess

Keyword(s):

Risk Factors ◽

Instrumental Variables ◽

Genetic Variants ◽

Mendelian Randomization ◽

Real Data ◽

Uk Biobank ◽

Pharmacological Interventions ◽

Natural Break ◽

Using Data ◽

Randomization Analysis

Abstract Background Factorial 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 four subgroups as in a factorial randomized trial. Methods We 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. Results Simulated and real data show that efficiency is maximized using the full set of interactions between genetic variants as instruments. In the applied example, between 4- and 10-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. Conclusions Previous factorial Mendelian randomization analyses may have been underpowered. Efficiency can be improved by using all genetic variants and their interactions as instrumental variables, rather than the 2 × 2 approach.

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Pleiotropy-robust Mendelian Randomization

10.1101/072603 ◽

2016 ◽

Cited By ~ 3

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.

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Avoiding bias in Mendelian randomization when stratifying on a collider

10.1101/2021.08.17.21262178 ◽

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.

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Estimating Marginal Healthcare Costs Using Genetic Variants as Instrumental Variables: Mendelian Randomization in Economic Evaluation

PharmacoEconomics ◽

10.1007/s40273-016-0432-x ◽

2016 ◽

Vol 34 (11) ◽

pp. 1075-1086 ◽

Cited By ~ 13

Author(s):

Padraig Dixon ◽

George Davey Smith ◽

Stephanie von Hinke ◽

Neil M. Davies ◽

William Hollingworth

Keyword(s):

Economic Evaluation ◽

Instrumental Variables ◽

Genetic Variants ◽

Healthcare Costs ◽

Mendelian Randomization

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P–374 Investigating causality of risk factors for miscarriage – a Mendelian randomization analysis

Human Reproduction ◽

10.1093/humrep/deab130.373 ◽

2021 ◽

Vol 36 (Supplement_1) ◽

Author(s):

J Painter ◽

T Laisk ◽

C Lindgren ◽

S Medland

Keyword(s):

Risk Factors ◽

Body Mass Index ◽

Risk Factor ◽

Instrumental Variables ◽

Genetic Variants ◽

Odds Ratio ◽

Mendelian Randomization ◽

Recurrent Miscarriage ◽

Coffee Consumption ◽

Smoking Initiation

Abstract Study question Do modifiable risk factors such as smoking, alcohol or coffee consumption, and adiposity causally increase the risk of sporadic or recurrent miscarriage? Summary answer We found evidence for a causal relationship between smoking initiation and sporadic miscarriage, but not for any other risk factor tested. What is known already Miscarriage is estimated to end between 10–25% of clinically confirmed pregnancies, and many observational studies have suggested numerous lifestyle factors, such as coffee and alcohol consumption, smoking and increased adiposity, may increase miscarriage risk. However, results are not always consistent across studies, and definitive causal relationships between various risk factors and miscarriage have not yet been demonstrated. Mendelian randomization utilizes genetic variants significantly associated with heritable risk factors (i.e. at P-values <5x10–8 in large genome-wide association studies) as instrumental variables to investigate causality of risk factors in population health outcomes. Study design, size, duration We conducted two-sample Mendelian randomization analyses to investigate causality of smoking (initiation and quantity), alcohol and coffee consumption (quantity), and adiposity (body mass index and waist-hip ratio) in sporadic and recurrent miscarriage. Data included in this study were taken from previously published summary genetic association statistics (betas, standard errors and P-values) from large-scale genome-wide association studies (GWAS) for each risk factor, and from our recently published GWAS of sporadic and recurrent miscarriage. Participants/materials, setting, methods Instrumental variables were constructed using 5–306 genetic variants significantly associated with the listed risk factors in published GWAS (minimum N = 178,000 individuals). Two instrumental variables were constructed per risk factor using data from different GWAS. Associations of the instrumental variables with miscarriage were investigated using summary association data from women of European ancestry included in our miscarriage GWAS, including 49,996 sporadic miscarriage cases and 174,109 female controls, and 750 recurrent miscarriage cases and 150,215 female controls. Main results and the role of chance We found a significant association between sporadic miscarriage and the instrumental variables for two smoking measures: smoking initiation (inverse variance weighted Odds Ratio = 1.17, 95% confidence intervals = 1.10–1.24, P = 2.7 x 10–07) and lifetime smoking (inverse variance weighted Odds Ratio = 1.22, 95% confidence intervals 1.11–1.35, P = 4.2x10–5). No other risk factors (smoking quantity, coffee or alcohol consumption, or BMI or waist-hip ratio) were associated with either sporadic or recurrent miscarriage. A priori power calculations considering the amount of phenotypic variance in each risk factor explained by the associated SNPs suggested our analysis to have at least 75% power to detect an association with Odds Ratio of 1.2 with sporadic miscarriage for analyses of body mass index, waist hip ratio and smoking initiation, quantity and the lifetime smoking measure, but that the alcohol and coffee consumption analyses were underpowered (4.9% and 48%, respectively). All analyses were underpowered for recurrent miscarriage given the small case sample size (N = 750). Limitations, reasons for caution While data utilised here come from large-scale GWAS including 1000s of individuals, genetic variants significantly associated with each risk factor currently explain small percentages (0.02–6%) of the variance in each trait. Larger GWAS for specific risk factors, and for sporadic and recurrent miscarriage, are required to clarify some published associations. Wider implications of the findings: We find no evidence of a causal link between adiposity and miscarriage, indicating that observational findings of increased miscarriage risk with increasing body mass index require further explanation. Significant associations between measures of ever-smoking and sporadic miscarriage highlights that no amount of smoking is safe in regards to miscarriage risk. Trial registration number Not applicable

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

10.1101/2020.10.21.347773 ◽

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.

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Investigating causal pathways between liability to ADHD and substance use, and liability to substance use and ADHD risk, using Mendelian randomization

10.1101/524769 ◽

2019 ◽

Cited By ~ 1

Author(s):

Jorien L Treur ◽

Ditte Demontis ◽

George Davey Smith ◽

Hannah Sallis ◽

Tom G Richardson ◽

...

Keyword(s):

Substance Use ◽

Alcohol Use ◽

Genetic Variants ◽

Mendelian Randomization ◽

Smoking Initiation ◽

Cannabis Use ◽

Causal Effects ◽

Sensitivity Analyses ◽

Genome Wide Association Studies ◽

Causal Pathways

ABSTRACTBackgroundAttention-deficit hyperactivity disorder (ADHD) has consistently been associated with substance (ab)use, but the nature of this association is not fully understood. In view of preventive efforts, a vital question is whether there are causal effects, from ADHD to substance use and/or from substance use to ADHD.MethodsWe applied bidirectional Mendelian randomization using summary-level data from the largest available genome-wide association studies (GWASs) on ADHD, smoking (initiation, cigarettes/day, cessation, and a compound measure of lifetime smoking), alcohol use (drinks/week and alcohol use disorder), cannabis use (initiation and cannabis use disorder (CUD)) and coffee consumption (cups/day). Genetic variants robustly associated with the ‘exposure’ were selected as instruments and then identified in the ‘outcome’ GWAS. Effect estimates from individual genetic variants were combined with inverse-variance weighted regression and five sensitivity analyses were applied (weighted median, weighted mode, MR-Egger, generalized summary-data-based MR, and Steiger filtering).ResultsWe found strong evidence that liability to ADHD increases likelihood of smoking initiation and also cigarettes per day among smokers, decreases likelihood of smoking cessation, and increases likelihood of cannabis initiation and CUD. In the other direction, there was evidence that liability to smoking initiation and CUD increase ADHD risk. There was no clear evidence of causal effects between liability to ADHD and alcohol or caffeine consumption.ConclusionsWe find evidence for causal effects of liability to ADHD on smoking and cannabis use, and of liability to smoking and cannabis use on ADHD risk, indicating bidirectional pathways. Further work is needed to explore causal mechanisms.

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Factorial Mendelian randomization: using genetic variants to assess interactions

10.1101/531228 ◽

2019 ◽

Cited By ~ 3

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.

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Constrained Instruments and their Application to Mendelian Randomization with Pleiotropy

10.1101/227454 ◽

2017 ◽

Cited By ~ 3

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.

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