The effect of smoking on multiple sclerosis: a mendelian randomization study

AbstractThe causes of multiple sclerosis (MS) remain unknown. Smoking has been associated with MS in observational studies and is often thought of as an environmental risk factor. We used two-sample Mendelian Randomization (MR) to examined whether this association is causal using genetic variants identified in genome-wide association studies (GWAS) as associated with smoking. We assessed both smoking initiation and lifetime smoking behaviour (which captures smoking duration, heaviness and cessation). There was very limited evidence for a meaningful effect of smoking on MS susceptibility was measured using summary statistics from the International Multiple Sclerosis Genetics Consortium (IMSGC) meta-analysis, including 14,802 cases and 26,703 controls. There was no clear evidence for an effect of smoking on the risk of developing MS (smoking initiation: odds ratio [OR] 1.03, 95% confidence interval [CI] 0.92-1.61; lifetime smoking: OR 1.10, 95% CI 0.87-1.40). These findings suggest that smoking does not have a detrimental consequence on MS susceptibility. Further work is needed to determine the causal effect of smoking on MS progression.

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Little evidence for an effect of smoking on multiple sclerosis risk: A Mendelian Randomization study

PLoS Biology ◽

10.1371/journal.pbio.3000973 ◽

2020 ◽

Vol 18 (11) ◽

pp. e3000973

Author(s):

Ruth E. Mitchell ◽

Kirsty Bates ◽

Robyn E. Wootton ◽

Adil Harroud ◽

J. Brent Richards ◽

...

Keyword(s):

Multiple Sclerosis ◽

Mendelian Randomization ◽

Causal Effect ◽

Association Studies ◽

Meta Analysis ◽

Smoking Initiation ◽

Smoking Behaviour ◽

Genome Wide Association Studies ◽

Genome Wide ◽

Smoking Duration

The causes of multiple sclerosis (MS) remain unknown. Smoking has been associated with MS in observational studies and is often thought of as an environmental risk factor. We used two-sample Mendelian randomization (MR) to examine whether this association is causal using genetic variants identified in genome-wide association studies (GWASs) as associated with smoking. We assessed both smoking initiation and lifetime smoking behaviour (which captures smoking duration, heaviness, and cessation). There was very limited evidence for a meaningful effect of smoking on MS susceptibility as measured using summary statistics from the International Multiple Sclerosis Genetics Consortium (IMSGC) meta-analysis, including 14,802 cases and 26,703 controls. There was no clear evidence for an effect of smoking on the risk of developing MS (smoking initiation: odds ratio [OR] 1.03, 95% confidence interval [CI] 0.92–1.61; lifetime smoking: OR 1.10, 95% CI 0.87–1.40). These findings suggest that smoking does not have a detrimental consequence on MS susceptibility. Further work is needed to determine the causal effect of smoking on MS progression.

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Evidence for causal effects of lifetime smoking on risk for depression and schizophrenia: a Mendelian randomisation study

Psychological Medicine ◽

10.1017/s0033291719002678 ◽

2019 ◽

Vol 50 (14) ◽

pp. 2435-2443 ◽

Cited By ~ 29

Author(s):

Robyn E. Wootton ◽

Rebecca C. Richmond ◽

Bobby G. Stuijfzand ◽

Rebecca B. Lawn ◽

Hannah M. Sallis ◽

...

Keyword(s):

Causal Effect ◽

Association Studies ◽

Smoking Initiation ◽

Smoking Behaviour ◽

Positive Control ◽

Mendelian Randomisation ◽

Genome Wide Association Studies ◽

Weak Evidence ◽

Smoking Duration ◽

The Uk

AbstractBackgroundSmoking prevalence is higher amongst individuals with schizophrenia and depression compared with the general population. Mendelian randomisation (MR) can examine whether this association is causal using genetic variants identified in genome-wide association studies (GWAS).MethodsWe conducted two-sample MR to explore the bi-directional effects of smoking on schizophrenia and depression. For smoking behaviour, we used (1) smoking initiation GWAS from the GSCAN consortium and (2) we conducted our own GWAS of lifetime smoking behaviour (which captures smoking duration, heaviness and cessation) in a sample of 462690 individuals from the UK Biobank. We validated this instrument using positive control outcomes (e.g. lung cancer). For schizophrenia and depression we used GWAS from the PGC consortium.ResultsThere was strong evidence to suggest smoking is a risk factor for both schizophrenia (odds ratio (OR) 2.27, 95% confidence interval (CI) 1.67–3.08, p < 0.001) and depression (OR 1.99, 95% CI 1.71–2.32, p < 0.001). Results were consistent across both lifetime smoking and smoking initiation. We found some evidence that genetic liability to depression increases smoking (β = 0.091, 95% CI 0.027–0.155, p = 0.005) but evidence was mixed for schizophrenia (β = 0.022, 95% CI 0.005–0.038, p = 0.009) with very weak evidence for an effect on smoking initiation.ConclusionsThese findings suggest that the association between smoking, schizophrenia and depression is due, at least in part, to a causal effect of smoking, providing further evidence for the detrimental consequences of smoking on mental health.

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

10.21203/rs.3.rs-277791/v1 ◽

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.

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Causal effect of renal function on venous thromboembolism: a two-sample Mendelian randomization investigation

Journal of Thrombosis and Thrombolysis ◽

10.1007/s11239-021-02494-4 ◽

2021 ◽

Author(s):

Shuai Yuan ◽

Maria Bruzelius ◽

Susanna C. Larsson

Keyword(s):

Renal Function ◽

Venous Thromboembolism ◽

Mendelian Randomization ◽

Causal Effect ◽

Meta Analysis ◽

Genome Wide Association Studies ◽

Uk Biobank ◽

Genome Wide ◽

Increased Risk ◽

Mr Study

AbstractWhether renal function is causally associated with venous thromboembolism (VTE) is not yet fully elucidated. We conducted a two-sample Mendelian randomization (MR) study to determine the causal effect of renal function, measured as estimated glomerular filtration rate (eGFR), on VTE. Single-nucleotide polymorphisms associated with eGFR were selected as instrumental variables at the genome-wide significance level (p < 5 × 10−8) from a meta-analysis of 122 genome-wide association studies including up to 1,046,070 individuals. Summary-level data for VTE were obtained from the FinnGen consortium (6913 VTE cases and 169,986 non-cases) and UK Biobank study (4620 VTE cases and 356,574 non-cases). MR estimates were calculated using the random-effects inverse-variance weighted method and combined using fixed-effects meta-analysis. Genetically predicted decreased eGFR was significantly associated with an increased risk of VTE in both FinnGen and UK Biobank. For one-unit decrease in log-transformed eGFR, the odds ratios of VTE were 2.93 (95% confidence interval (CI) 1.25, 6.84) and 4.46 (95% CI 1.59, 12.5) when using data from FinnGen and UK Biobank, respectively. The combined odds ratio was 3.47 (95% CI 1.80, 6.68). Results were consistent in all sensitivity analyses and no horizontal pleiotropy was detected. This MR-study supported a casual role of impaired renal function in VTE.

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

Bioinformatics ◽

10.1093/bioinformatics/btaa549 ◽

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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Causal Inference for Heritable Phenotypic Risk Factors Using Heterogeneous Genetic Instruments

10.1101/2020.05.06.077982 ◽

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.

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Bayesian weighted Mendelian randomization for causal inference based on summary statistics

Bioinformatics ◽

10.1093/bioinformatics/btz749 ◽

2019 ◽

Author(s):

Jia Zhao ◽

Jingsi Ming ◽

Xianghong Hu ◽

Gang Chen ◽

Jin Liu ◽

...

Keyword(s):

Causal Inference ◽

Complex Traits ◽

Mendelian Randomization ◽

Causal Effect ◽

Association Studies ◽

Supplementary Information ◽

Genome Wide Association Studies ◽

Simulation Studies ◽

Genome Wide ◽

Complex Human Traits

Abstract Motivation The results from Genome-Wide Association Studies (GWAS) on thousands of phenotypes provide an unprecedented opportunity to infer the causal effect of one phenotype (exposure) on another (outcome). Mendelian randomization (MR), an instrumental variable (IV) method, has been introduced for causal inference using GWAS data. Due to the polygenic architecture of complex traits/diseases and the ubiquity of pleiotropy, however, MR has many unique challenges compared to conventional IV methods. Results We propose a Bayesian weighted Mendelian randomization (BWMR) for causal inference to address these challenges. In our BWMR model, the uncertainty of weak effects owing to polygenicity has been taken into account and the violation of IV assumption due to pleiotropy has been addressed through outlier detection by Bayesian weighting. To make the causal inference based on BWMR computationally stable and efficient, we developed a variational expectation-maximization (VEM) algorithm. Moreover, we have also derived an exact closed-form formula to correct the posterior covariance which is often underestimated in variational inference. Through comprehensive simulation studies, we evaluated the performance of BWMR, demonstrating the advantage of BWMR over its competitors. Then we applied BWMR to make causal inference between 130 metabolites and 93 complex human traits, uncovering novel causal relationship between exposure and outcome traits. Availability and implementation The BWMR software is available at https://github.com/jiazhao97/BWMR. Supplementary information Supplementary data are available at Bioinformatics online.

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The putative causal effect of type 2 diabetes in risk of cataract: a Mendelian randomization study in East Asian

10.1101/2021.02.08.430342 ◽

2021 ◽

Author(s):

Haoyang Zhang ◽

Xuehao Xiu ◽

Angli Xue ◽

Yuedong Yang ◽

Yuanhao Yang ◽

...

Keyword(s):

Type 2 Diabetes ◽

Genetic Correlation ◽

Mendelian Randomization ◽

Causal Effect ◽

Association Studies ◽

Population Based ◽

P Value ◽

Genome Wide Association Studies ◽

Genome Wide

AbstractBackgroundThe epidemiological association between type 2 diabetes and cataract has been well-established. However, it remains unclear whether the two diseases share a genetic basis, and if so, whether this reflects a causal relationship.MethodsWe utilized East Asian population-based genome-wide association studies (GWAS) summary statistics of type 2 diabetes (Ncase=36,614, Ncontrol=155,150) and cataract (Ncase=24,622, Ncontrol=187,831) to comprehensively investigate the shared genetics between the two diseases. We performed 1. linkage disequilibrium score regression (LDSC) and heritability estimation from summary statistics (ρ-HESS) to estimate the genetic correlation and local genetic correlation between type 2 diabetes and cataract; 2. multiple Mendelian randomization (MR) analyses to infer the putative causality between type 2 diabetes and cataract; and 3. Summary-data-based Mendelian randomization (SMR) to identify candidate risk genes underling the causality.ResultsWe observed a strong genetic correlation (rg=0.58; p-value=5.60×10−6) between type 2 diabetes and cataract. Both ρ-HESS and multiple MR methods consistently showed a putative causal effect of type 2 diabetes on cataract, with estimated liability-scale MR odds ratios (ORs) at around 1.10 (95% confidence interval [CI] ranging from 1.06 to 1.17). In contrast, no evidence supports a causal effect of cataract on type 2 diabetes. SMR analysis identified two novel genes MIR4453HG (βSMR=−0.34, p-value=6.41×10−8) and KCNK17 (βSMR=−0.07, p-value=2.49×10−10), whose expression levels were likely involved in the putative causality of type 2 diabetes on cataract.ConclusionsOur results provided robust evidence supporting a causal effect of type 2 diabetes on the risk of cataract in East Asians, and posed new paths on guiding prevention and early-stage diagnosis of cataract in type 2 diabetes patients.Key MessagesWe utilized genome-wide association studies of type 2 diabetes and cataract in a large Japanese population-based cohort and find a strong genetic overlap underlying the two diseases.We performed multiple Mendelian randomization models and consistently disclosed a putative causal effect of type 2 diabetes on the development of cataract.We revealed two candidate genes MIR4453HG and KCNK17 whose expression levelss are likely relevant to the causality between type 2 diabetes and cataract.Our study provided theoretical fundament at the genetic level for improving early diagnosis, prevention and treatment of cataract in type 2 diabetes patients in clinical practice

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Depression and Osteoporosis: A Mendelian Randomization Study

Calcified Tissue International ◽

10.1007/s00223-021-00886-5 ◽

2021 ◽

Author(s):

Bin He ◽

Qiong Lyu ◽

Lifeng Yin ◽

Muzi Zhang ◽

Zhengxue Quan ◽

...

Keyword(s):

Observational Studies ◽

Mendelian Randomization ◽

Causal Effect ◽

Association Studies ◽

Meta Analysis ◽

Sensitivity Analyses ◽

European Ancestry ◽

Genome Wide Association Studies ◽

Reverse Causality ◽

Mineral Density

AbstractObservational studies suggest a link between depression and osteoporosis, but these may be subject to confounding and reverse causality. In this two-sample Mendelian randomization analysis, we included the large meta-analysis of genome-wide association studies for depression among 807,553 individuals (246,363 cases and 561,190 controls) of European descent, the large meta-analysis to identify genetic variants associated with femoral neck bone mineral density (FN-BMD), forearm BMD (FA-BMD) and lumbar spine BMD (LS-BMD) among 53,236 individuals of European ancestry, and the GWAS summary data of heel BMD (HE-BMD) and fracture among 426,824 individuals of European ancestry. The results revealed that genetic predisposition towards depression showed no causal effect on FA-BMD (beta-estimate: 0.091, 95% confidence interval [CI] − 0.088 to 0.269, SE:0.091, P value = 0.320), FN-BMD (beta-estimate: 0.066, 95% CI − 0.016 to 0.148, SE:0.042, P value = 0.113), LS-BMD (beta-estimate: 0.074, 95% CI − 0.029 to 0.177, SE:0.052, P value = 0.159), HE-BMD (beta-estimate: 0.009, 95% CI − 0.043 to 0.061, SE:0.027, P value = 0.727), or fracture (beta-estimate: 0.008, 95% CI − 0.071 to 0.087, SE:0.041, P value = 0.844). These results were also confirmed by multiple sensitivity analyses. Contrary to the findings of observational studies, our results do not reveal a causal role of depression in osteoporosis or fracture.

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