scholarly journals Understanding the Assumptions Underlying Mendelian Randomization

2021 ◽  
Author(s):  
Christiaan de Leeuw ◽  
Jeanne Savage ◽  
Ioan Gabriel Bucur ◽  
Tom Heskes ◽  
Danielle Posthuma
Keyword(s):  
Instrumental Variables ◽  
Experimental Research ◽  
Genetic Variants ◽  
Mendelian Randomization ◽  
Secondary Analysis ◽  
Genetic Data ◽  
Causal Effects ◽  
Data Sets ◽  
Analysis Method

With the rapidly increasing availability of large genetic data sets in recent years, Mendelian Randomization (MR) has quickly gained popularity as a novel secondary analysis method. Leveraging genetic variants as instrumental variables, MR can be used to estimate the causal effects of one phenotype on another even when experimental research is not feasible, and therefore has the potential to be highly informative. It is dependent on strong assumptions however, often producing strongly biased results if these are not met. It is therefore imperative that these assumptions are well-understood by researchers aiming to use MR, in order to evaluate their validity in the context of their analyses and data. The aim of this perspective is therefore to further elucidate these assumptions and the role they play in MR, as well as how different kinds of data can be used to further support them.

scholarly journals Investigating genetic correlations and causal effects between caffeine consumption and sleep behaviours

2017 ◽  
Author(s):  
Jorien L. Treur ◽  
Mark Gibson ◽  
Amy E Taylor ◽  
Peter J Rogers ◽  
Marcus R Munafò
Keyword(s):  
Genetic Correlation ◽  
Sleep Duration ◽  
Genetic Variants ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Genetic Correlations ◽  
Causal Effects ◽  
Genome Wide Association Studies ◽  
Caffeine Consumption ◽  
Insomnia Complaints

AbstractStudy Objectives:Higher caffeine consumption has been linked to poorer sleep and insomnia complaints. We investigated whether these observational associations are the result of genetic risk factors influencing both caffeine consumption and poorer sleep, and/or whether they reflect (possibly bidirectional) causal effects.Methods:Summary-level data were available from genome-wide association studies (GWAS) on caffeine consumption (n=91,462), sleep duration, and chronotype (i.e., being a ‘morning’ versus an ‘evening’ person) (both n=128,266), and insomnia complaints (n=113,006). Linkage disequilibrium (LD) score regression was used to calculate genetic correlations, reflecting the extent to which genetic variants influencing caffeine consumption and sleep behaviours overlap. Causal effects were tested with bidirectional, two-sample Mendelian randomization (MR), an instrumental variable approach that utilizes genetic variants robustly associated with an exposure variable as an instrument to test causal effects. Estimates from individual genetic variants were combined using inverse-variance weighted meta-analysis, weighted median regression and MR Egger regression methods.Results:There was no clear evidence for genetic correlation between caffeine consumption and sleep duration (rg=0.000,p=0.998), chronotype (rg=0.086,p=0.192) or insomnia (rg=-0.034,p=0.700). Two-sample Mendelian randomization analyses did not support causal effects from caffeine consumption to sleep behaviours, or the other way around.Conclusions:We found no evidence in support of genetic correlation or causal effects between caffeine consumption and sleep. While caffeine may have acute effects on sleep when taken shortly before habitual bedtime, our findings suggest that a more sustained pattern of high caffeine consumption is likely associated with poorer sleep through shared environmental factors.

scholarly journals Novel insights into the consequences of obesity: a phenotype-wide Mendelian randomization study

2022 ◽  
Author(s):  
Chang He ◽  
Miaoran Zhang ◽  
Jiuling Li ◽  
Yiqing Wang ◽  
Lanlan Chen ◽  
...  
Keyword(s):  
Instrumental Variables ◽  
Fixed Effects ◽  
Mendelian Randomization ◽  
Portion Size ◽  
Causal Effects ◽  
Calorie Intake ◽  
Smoking Behaviors ◽  
Prostate Cancers ◽  
The Uk ◽  
Consequences Of Obesity

AbstractObesity is thought to significantly impact the quality of life. In this study, we sought to evaluate the health consequences of obesity on the risk of a broad spectrum of human diseases. The causal effects of exposing to obesity on health outcomes were inferred using Mendelian randomization (MR) analyses using a fixed effects inverse-variance weighted model. The instrumental variables were SNPs associated with obesity as measured by body mass index (BMI) reported by GIANT consortium. The spectrum of outcome consisted of the phenotypes from published GWAS and the UK Biobank. The MR-Egger intercept test was applied to estimate horizontal pleiotropic effects, along with Cochran’s Q test to assess heterogeneity among the causal effects of instrumental variables. Our MR results confirmed many putative disease risks due to obesity, such as diabetes, dyslipidemia, sleep disorder, gout, smoking behaviors, arthritis, myocardial infarction, and diabetes-related eye disease. The novel findings indicated that elevated red blood cell count was inferred as a mediator of BMI-induced type 2 diabetes in our bidirectional MR analysis. Intriguingly, the effects that higher BMI could decrease the risk of both skin and prostate cancers, reduce calorie intake, and increase the portion size warrant further studies. Our results shed light on a novel mechanism of the disease-causing roles of obesity.

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

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

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

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

2019 ◽  
Vol 49 (4) ◽  
pp. 1147-1158 ◽  
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.

scholarly journals Mendelian randomization with fine-mapped genetic data: Choosing from large numbers of correlated instrumental variables

2017 ◽  
Vol 41 (8) ◽  
pp. 714-725 ◽  
Author(s):  
Stephen Burgess ◽  
Verena Zuber ◽  
Elsa Valdes-Marquez ◽  
Benjamin B Sun ◽  
Jemma C Hopewell
Keyword(s):  
Instrumental Variables ◽  
Mendelian Randomization ◽  
Genetic Data ◽  
Large Numbers

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 Mendelian randomization studies of biomarkers and type 2 diabetes

2015 ◽  
Vol 4 (4) ◽  
pp. 249-260 ◽  
Author(s):  
Ali Abbasi
Keyword(s):  
Type 2 Diabetes ◽  
Genetic Variants ◽  
Large Scale ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Causal Effects ◽  
Current Evidence ◽  
Genome Wide Association Studies ◽  
Glycaemic Traits

Many biomarkers are associated with type 2 diabetes (T2D) risk in epidemiological observations. The aim of this study was to identify and summarize current evidence for causal effects of biomarkers on T2D. A systematic literature search in PubMed and EMBASE (until April 2015) was done to identify Mendelian randomization studies that examined potential causal effects of biomarkers on T2D. To replicate the findings of identified studies, data from two large-scale, genome-wide association studies (GWAS) were used: DIAbetes Genetics Replication And Meta-analysis (DIAGRAMv3) for T2D and the Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC) for glycaemic traits. GWAS summary statistics were extracted for the same genetic variants (or proxy variants), which were used in the original Mendelian randomization studies. Of the 21 biomarkers (from 28 studies), ten have been reported to be causally associated with T2D in Mendelian randomization. Most biomarkers were investigated in a single cohort study or population. Of the ten biomarkers that were identified, nominally significant associations with T2D or glycaemic traits were reached for those genetic variants related to bilirubin, pro-B-type natriuretic peptide, delta-6 desaturase and dimethylglycine based on the summary data from DIAGRAMv3 or MAGIC. Several Mendelian randomization studies investigated the nature of associations of biomarkers with T2D. However, there were only a few biomarkers that may have causal effects on T2D. Further research is needed to broadly evaluate the causal effects of multiple biomarkers on T2D and glycaemic traits using data from large-scale cohorts or GWAS including many different genetic variants.

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