scholarly journals Robust inference in summary data Mendelian randomisation via the zero modal pleiotropy assumption

2017 ◽  
Author(s):  
Fernando Pires Hartwig ◽  
George Davey Smith ◽  
Jack Bowden
Keyword(s):  
Instrumental Variable ◽  
Causal Effect ◽  
Association Studies ◽  
Mendelian Randomisation ◽  
Effect Estimate ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Weighted Median ◽  
Summary Data ◽  
Genetic Instruments

AbstractBackgroundMendelian randomisation (MR) is being increasingly used to strengthen causal inference in observational studies. Availability of summary data of genetic associations for a variety of phenotypes from large genome-wide association studies (GWAS) allows straightforward application of MR using summary data methods, typically in a two-sample design. In addition to the conventional inverse variance weighting (IVW) method, recently developed summary data MR methods, such as the MR-Egger and weighted median approaches, allow a relaxation of the instrumental variable assumptions.MethodsHere, a new method –the mode-based estimate (MBE) – is proposed to obtain a single causal effect estimate from multiple genetic instruments. The MBE is consistent when the largest number of similar (identical in infinite samples) individual-instrument causal effect estimates comes from valid instruments, even if the majority of instruments are invalid. We evaluate the performance of the method in simulations designed to mimic the two-sample summary data setting, and demonstrate its use by investigating the causal effect of plasma lipid fractions and urate levels on coronary heart disease risk.ResultsThe MBE presented less bias and type-I error rates than other methods under the null in many situations. Its power to detect a causal effect was smaller compared to the IVW and weighted median methods, but was larger than that of MR-Egger regression, with sample size requirements typically smaller than those available from GWAS consortia.ConclusionsThe MBE relaxes the instrumental variable assumptions, and should be used in combination with other approaches in a sensitivity analysis.Key MessagesSummary data Mendelian randomisation, typically in a two-sample setting, is being increasingly used due to the availability of summary association results from large genome- wide association studies.Mendelian randomisation analyses using multiple genetic instruments are prone to bias due to horizontal pleiotropy, especially when genetic instruments are selected based solely on statistical criteria.A causal effect estimate robust to horizontal pleiotropy can be obtained using the mode- based estimate (MBE).The MBE requires that the most common causal effect estimate is a consistent estimate of the true causal effect, even if the majority of instruments are invalid (i.e., the ZEro Modal Pleiotropy Assumption, or ZEMPA).Plotting the smoothed empirical density function is useful to explore the distribution of causal effect estimates, and to understand how the MBE is determined.

scholarly journals Causal Inference for Heritable Phenotypic Risk Factors Using Heterogeneous Genetic Instruments

2020 ◽  
Author(s):  
Jingshu Wang ◽  
Qingyuan Zhao ◽  
Jack Bowden ◽  
Gilbran Hemani ◽  
George Davey Smith ◽  
...  
Keyword(s):  
Risk Factors ◽  
Complex Traits ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genetic Associations ◽  
Genome Wide ◽  
Genetic Instruments

Over a decade of genome-wide association studies have led to the finding that significant genetic associations tend to spread across the genome for complex traits. The extreme polygenicity where "all genes affect every complex trait" complicates Mendelian Randomization studies, where natural genetic variations are used as instruments to infer the causal effect of heritable risk factors. We reexamine the assumptions of existing Mendelian Randomization methods and show how they need to be clarified to allow for pervasive horizontal pleiotropy and heterogeneous effect sizes. We propose a comprehensive framework GRAPPLE (Genome-wide mR Analysis under Pervasive PLEiotropy) to analyze the causal effect of target risk factors with heterogeneous genetic instruments and identify possible pleiotropic patterns from data. By using summary statistics from genome-wide association studies, GRAPPLE can efficiently use both strong and weak genetic instruments, detect the existence of multiple pleiotropic pathways, adjust for confounding risk factors, and determine the causal direction. With GRAPPLE, we analyze the effect of blood lipids, body mass index, and systolic blood pressure on 25 disease outcomes, gaining new information on their causal relationships and the potential pleiotropic pathways.

scholarly journals Investigating the role of insulin in increased adiposity: Bi-directional Mendelian randomization study

2017 ◽  
Author(s):  
RC Richmond ◽  
KH Wade ◽  
L Corbin ◽  
J Bowden ◽  
G Hemani ◽  
...  
Keyword(s):  
Large Scale ◽  
Causal Effect ◽  
Association Studies ◽  
The Body ◽  
Genome Wide Association Studies ◽  
Fasting Insulin ◽  
Insulin Levels ◽  
Genome Wide ◽  
Weighted Median ◽  
High Degree

AbstractInsulin may serve as a key causal agent which regulates fat accumulation in the body. Here we assessed the causal relationship between fasting insulin and adiposity using publicly-available results from two large-scale genome-wide association studies for body mass index and fasting insulin levels in a two-sample, bidirectional Mendelian Randomized approach. This approach is only valid on the condition that the two instruments are independent of one another. In analysis excluding overlapping loci, there was an increase of 0.20 (0.17, 0.23) log pmol/L fasting insulin per SD increase in BMI (P= 2.80 x 10−36), while there was a null effect of fasting insulin on BMI, with a 0.01 (−0.39, 0.38) SD decrease in BMI per log pmol/L increase in fasting insulin (P= 0.98). Furthermore, a high degree of heterogeneity in the causal estimates was obtained from the insulin-related variants, which may be attributed to varying mechanisms of action of the insulin-associated variants. Results were largely consistent when an Egger regression technique and weighted median and mode estimators were applied. Findings suggest that the positive correlation between adiposity and fasting insulin levels are at least in part explained by the causal effect of adiposity on increasing insulin, rather than vice versa.

scholarly journals A multivariable Mendelian randomisation study exploring the direct effects of nicotine on health compared with the other constituents of tobacco smoke: Implications for e-cigarette use

2021 ◽  
Author(s):  
Jasmine N Khouja ◽  
Eleanor Sanderson ◽  
Robyn E Wootton ◽  
Amy E Taylor ◽  
Marcus R Munafò
Keyword(s):  
Heart Rate ◽  
Tobacco Smoke ◽  
Association Studies ◽  
Mendelian Randomisation ◽  
Genome Wide Association Studies ◽  
Direct Effects ◽  
Uk Biobank ◽  
Genome Wide ◽  
Increased Risk ◽  
Summary Data

AbstractObjectivesGiven the popularity of e-cigarettes, and the lack of longitudinal evidence regarding their safety, novel methods are required to explore potential health effects resulting directly from nicotine use. The aim of this study was to explore the direct effects of nicotine compared with the other constituents of tobacco smoke on health outcomes associated with smoking.DesignObservational study, using Mendelian randomisation and multivariable Mendelian randomisation analyses of summary data.SettingSummary data from two previous genome-wide association studies, and summary data generated from UK Biobank, a prospective cohort study.ParticipantsN = 337,010 individuals enrolled in UK Biobank, and a total of N = 341,882 individuals from two previous genome-wide association studies.Main outcome measuresWe explored the effect of cotinine levels (as a proxy for nicotine exposure) and smoking heaviness (to capture cigarette smoke exposure) on body mass index (BMI), chronic obstructive pulmonary disease (COPD), forced vital capacity (FVC), forced expiratory volume (FEV-1), coronary heart disease (CHD), and heart rate.ResultsIn multivariable Mendelian randomisation analyses, there was weak evidence to suggest that increased cotinine levels may cause increased heart rate among current smokers (β = 0.50 bpm, 95% CI −0.06 to 1.05). There was stronger evidence to suggest that increased smoking heaviness causes decreased BMI among current smokers (β = −1.81 kg/m2, 95% CI −2.64 to −0.98), as well as increased risk of COPD, decreased FEV-1 and FVC, and increased heart rate among ever and current smokers. We also found evidence to suggest that increased smoking heaviness causes increased risk of CHD among ever smokers.ConclusionsOur combined findings are consistent with smoking-related health outcomes being caused by exposure to the non-nicotine components of tobacco smoke.

scholarly journals Association Between Covid-19 and Heart Failure: Evidence From a Two-sample Mendelian Randomization Study

2021 ◽  
Author(s):  
Huachen Wang ◽  
Zheng Guo ◽  
Yulu Zheng ◽  
Bing Chen
Keyword(s):  
Heart Failure ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Association Studies ◽  
Meta Analysis ◽  
European Ancestry ◽  
Genome Wide Association Studies ◽  
Reverse Causality ◽  
Genome Wide ◽  
Weighted Median

Abstract Background: Current research observing inconsistent associations of Corona Virus Disease 2019 (COVID-19) with heart failure (HF) are prone to bias based on reverse causality and residual confounding factors. Our aim was to apply a two-sample Mendelian randomization method to investigate whether COVID-19 has a causal effect on HF. Methods: Twenty-nine single nucleotide polymorphisms (SNPs) were proposed as candidate instrumental variables (IVs). A total of 3,523 patients with COVID-19 and 36,634 control participants were included in the genome-wide meta-analysis. We analyzed the largest genome-wide association studies (GWAS) meta-analysis of heart failure in individuals of European ancestry consisting of 47,309 patients with HF and 930,014 controls. The inverse variance weighted (IVW), the Mendelian randomization-Egger (MR-Egger) regression, the simple mode (SM), weighted median, and weighted mode were utilized for the MR analysis to test the stability and a causal effect. Results: The IVW, MR-Egger regression, SM, weighted median and weighted mode demonstrated there was no association between the genetically predicted COVID-19 infection and HF risk (OR, 1.004; 95%CI, 0.994-1.014; P=0.467; OR, 1.008; 95%CI, 0.996-1.019; P=0.218; OR, 0.968; 95%CI, 0.924-1.015; P=0.186; OR, 1.001; 95%CI, 0.988-1.014; P=0.881; OR, 1.001; 95%CI, 0.989-1.014; P=0.836; respectively). Conclusion: This two-sample Mendelian randomization analysis provided no evidence to sustain the causality of COVID-19 on HF.

The causal effect of interleukin-17 on the risk of psoriatic arthritis: a Mendelian randomization study

Rheumatology ◽  
2020 ◽  
Author(s):  
Dongze Wu ◽  
Priscilla Wong ◽  
Steven H M Lam ◽  
Edmund K Li ◽  
Ling Qin ◽  
...  
Keyword(s):  
Mendelian Randomization ◽  
Causal Effect ◽  
Association Studies ◽  
Preliminary Evidence ◽  
Interleukin 17 ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Genome Wide ◽  
Tnf Α ◽  
Weighted Median

Abstract Objective To determine causal associations between genetically predicted TNF-α, IL-12p70 and IL-17 levels and risk of PsA. Methods The publicly available summary-level findings from genome-wide association studies (GWAS) was used to identify loci influencing normal physiological concentrations of TNF-α, IL-12p70 and IL-17 (n = 8293) among healthy individuals as exposure and a GWAS for PsA from the UK Biobank (PsA = 900, control = 462 033) as the outcome. A two-sample Mendelian randomization (MR) analysis was performed using the inverse-variance weighted (IVW), weighted median and MR–Egger regression methods. Sensitivity analysis and MR–Egger regression analysis were performed to evaluate the heterogeneity and pleiotropic effects of each variant. Results Single-nucleotide polymorphisms (SNPs) at genome-wide significance from GWASs on TNF-α, IL-12p70 and IL-17 were identified as the instrumental variables. The IVW method indicated a causal association between increased IL-17 level and risk of PsA (β = −0.00186 per allele, s.e. = 0.00043, P = 0.002). Results were consistent in the weighted median method (β = −0.00145 per allele, s.e. = 0.00059, P = 0.014) although the MR–Egger method suggested a non-significant association (β = −0.00133 per allele, s.e. = 0.00087; P = 0.087). Single SNP MR results revealed that the C allele of rs117556572 was robustly associated with risk of PsA (β = 0.00210, s.e. = 0.00069, P = 0.002). However, no evidence for a causal effect was observed between TNF-α, IL-12p70, decreased IL-17 levels and risk of PsA. Conclusion Our findings provide preliminary evidence that genetic variants predisposing to higher physiological IL-17 level are associated with decreased risk of PsA.

scholarly journals Transcriptome-wide Mendelian randomization study prioritising novel tissue-dependent genes for glioma susceptibility

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jamie W. Robinson ◽  
Richard M. Martin ◽  
Spiridon Tsavachidis ◽  
Amy E. Howell ◽  
Caroline L. Relton ◽  
...  
Keyword(s):  
Gene Expression ◽  
Association Studies ◽  
Tissue Expression ◽  
Tissue Type ◽  
Mendelian Randomisation ◽  
Genome Wide Association Studies ◽  
Causal Pathways ◽  
Genome Wide ◽  
Glioma Risk ◽  
Brain Tissues

AbstractGenome-wide association studies (GWAS) have discovered 27 loci associated with glioma risk. Whether these loci are causally implicated in glioma risk, and how risk differs across tissues, has yet to be systematically explored. We integrated multi-tissue expression quantitative trait loci (eQTLs) and glioma GWAS data using a combined Mendelian randomisation (MR) and colocalisation approach. We investigated how genetically predicted gene expression affects risk across tissue type (brain, estimated effective n = 1194 and whole blood, n = 31,684) and glioma subtype (all glioma (7400 cases, 8257 controls) glioblastoma (GBM, 3112 cases) and non-GBM gliomas (2411 cases)). We also leveraged tissue-specific eQTLs collected from 13 brain tissues (n = 114 to 209). The MR and colocalisation results suggested that genetically predicted increased gene expression of 12 genes were associated with glioma, GBM and/or non-GBM risk, three of which are novel glioma susceptibility genes (RETREG2/FAM134A, FAM178B and MVB12B/FAM125B). The effect of gene expression appears to be relatively consistent across glioma subtype diagnoses. Examining how risk differed across 13 brain tissues highlighted five candidate tissues (cerebellum, cortex, and the putamen, nucleus accumbens and caudate basal ganglia) and four previously implicated genes (JAK1, STMN3, PICK1 and EGFR). These analyses identified robust causal evidence for 12 genes and glioma risk, three of which are novel. The correlation of MR estimates in brain and blood are consistently low which suggested that tissue specificity needs to be carefully considered for glioma. Our results have implicated genes yet to be associated with glioma susceptibility and provided insight into putatively causal pathways for glioma risk.

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 Cigarette smoking and personality: interrogating causality using Mendelian randomisation

2018 ◽  
Vol 49 (13) ◽  
pp. 2197-2205 ◽  
Author(s):  
Hannah M. Sallis ◽  
George Davey Smith ◽  
Marcus R. Munafò
Keyword(s):  
Personality Traits ◽  
Association Studies ◽  
Smoking Initiation ◽  
Mendelian Randomisation ◽  
Genome Wide Association Studies ◽  
Individual Level ◽  
Causal Pathways ◽  
Genome Wide ◽  
Level Data ◽  
Causal Nature

AbstractBackgroundDespite the well-documented association between smoking and personality traits such as neuroticism and extraversion, little is known about the potential causal nature of these findings. If it were possible to unpick the association between personality and smoking, it may be possible to develop tailored smoking interventions that could lead to both improved uptake and efficacy.MethodsRecent genome-wide association studies (GWAS) have identified variants robustly associated with both smoking phenotypes and personality traits. Here we use publicly available GWAS summary statistics in addition to individual-level data from UK Biobank to investigate the link between smoking and personality. We first estimate genetic overlap between traits using LD score regression and then use bidirectional Mendelian randomisation methods to unpick the nature of this relationship.ResultsWe found clear evidence of a modest genetic correlation between smoking behaviours and both neuroticism and extraversion. We found some evidence that personality traits are causally linked to certain smoking phenotypes: among current smokers each additional neuroticism risk allele was associated with smoking an additional 0.07 cigarettes per day (95% CI 0.02–0.12, p = 0.009), and each additional extraversion effect allele was associated with an elevated odds of smoking initiation (OR 1.015, 95% CI 1.01–1.02, p = 9.6 × 10−7).ConclusionWe found some evidence for specific causal pathways from personality to smoking phenotypes, and weaker evidence of an association from smoking initiation to personality. These findings could be used to inform future smoking interventions or to tailor existing schemes.

scholarly journals Pulmonary function and risk of Alzheimer dementia: two-sample Mendelian randomization study

2020 ◽  
Author(s):  
Tom C. Russ ◽  
Sarah E. Harris ◽  
G. David Batty
Keyword(s):  
Pulmonary Function ◽  
Causal Effect ◽  
Association Studies ◽  
Forced Expiratory Volume ◽  
Health Concern ◽  
Mendelian Randomisation ◽  
Genome Wide Association Studies ◽  
Global Public Health ◽  
Alzheimer Dementia ◽  
Dementia Risk

ABSTRACTDementia is a major global public health concern and in addition to recognised risk factors there is emerging evidence that poorer pulmonary function is linked with subsequent dementia risk. However, it is unclear if this observed association is causal or whether it might result from confounding. Therefore, we present the first two-sample Mendelian randomisation study of the association between pulmonary function and Alzheimer dementia using the most recent genome-wide association studies to produce instrumental variables for both. We found no evidence of a causal effect of reduced Forced Expiratory Volume in 1 second (FEV1) or Forced Vital Capacity (FVC) on Alzheimer dementia risk (both P>0.35). However, the FEV1/FVC ratio was associated with Alzheimer dementia risk with, in fact, superior function predicting an increased dementia risk (OR 1.12, 95%CI 1.02-1.23; P=0.016) which may result from survivor bias. While we can conclude that there is no causal link between impaired pulmonary function and Alzheimer dementia, our study sheds less light on potential links with other types of dementia.

scholarly journals bGWAS: an R package to perform Bayesian genome wide association studies

2020 ◽  
Vol 36 (15) ◽  
pp. 4374-4376
Author(s):  
Ninon Mounier ◽  
Zoltán Kutalik
Keyword(s):  
Mendelian Randomization ◽  
Causal Effect ◽  
Association Studies ◽  
R Package ◽  
Genome Wide Association ◽  
Supplementary Information ◽  
Genome Wide Association Studies ◽  
Biological Mechanisms ◽  
Genome Wide ◽  
Related Risk

Abstract Summary Increasing sample size is not the only strategy to improve discovery in Genome Wide Association Studies (GWASs) and we propose here an approach that leverages published studies of related traits to improve inference. Our Bayesian GWAS method derives informative prior effects by leveraging GWASs of related risk factors and their causal effect estimates on the focal trait using multivariable Mendelian randomization. These prior effects are combined with the observed effects to yield Bayes Factors, posterior and direct effects. The approach not only increases power, but also has the potential to dissect direct and indirect biological mechanisms. Availability and implementation bGWAS package is freely available under a GPL-2 License, and can be accessed, alongside with user guides and tutorials, from https://github.com/n-mounier/bGWAS. Supplementary information Supplementary data are available at Bioinformatics online.

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