Correction to: Mendelian Randomization Studies in Stroke: Exploration of Risk Factors and Drug Targets With Human Genetic Data

Elucidating the causes of stroke is key to developing effective preventive strategies. The Mendelian randomization approach leverages genetic variants related to an exposure of interest to investigate the effects of varying that exposure on disease risk. The random allocation of genetic variants at conception reduces confounding from environmental factors and thus strengthens causal inference, analogous to treatment allocation in a randomized controlled trial. With the recent explosion in the availability of human genetic data, Mendelian randomization has proven a valuable tool for studying risk factors for stroke. In this review, we provide an overview of recent developments in the application of Mendelian randomization to unravel the pathophysiology of stroke subtypes and identify therapeutic targets for clinical translation. The approach has offered novel insight into the differential effects of risk factors and antihypertensive, lipid-lowering, and anticoagulant drug classes on risk of stroke subtypes. Analyses have further facilitated the prioritization of novel drug targets, such as for inflammatory pathways underlying large artery atherosclerotic stroke and for the coagulation cascade that contributes to cardioembolic stroke. With continued methodological advances coupled with the rapidly increasing availability of genetic data related to a broad range of stroke phenotypes, the potential for Mendelian randomization in this context is expanding exponentially.

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Using the MR-Base platform to investigate risk factors and drug targets for thousands of phenotypes

Wellcome Open Research ◽

10.12688/wellcomeopenres.15334.2 ◽

2019 ◽

Vol 4 ◽

pp. 113 ◽

Cited By ~ 12

Author(s):

Venexia M Walker ◽

Neil M Davies ◽

Gibran Hemani ◽

Jie Zheng ◽

Philip C Haycock ◽

...

Keyword(s):

Risk Factors ◽

Web Application ◽

Drug Targets ◽

Genome Wide Association Study ◽

Mendelian Randomization ◽

Causal Effect ◽

R Package ◽

Sensitivity Analyses ◽

Link Type ◽

Study Results

Mendelian randomization (MR) estimates the causal effect of exposures on outcomes by exploiting genetic variation to address confounding and reverse causation. This method has a broad range of applications, including investigating risk factors and appraising potential targets for intervention. MR-Base has become established as a freely accessible, online platform, which combines a database of complete genome-wide association study results with an interface for performing Mendelian randomization and sensitivity analyses. This allows the user to explore millions of potentially causal associations. MR-Base is available as a web application or as an R package. The technical aspects of the tool have previously been documented in the literature. The present article is complementary to this as it focuses on the applied aspects. Specifically, we describe how MR-Base can be used in several ways, including to perform novel causal analyses, replicate results and enable transparency, amongst others. We also present three use cases, which demonstrate important applications of Mendelian randomization and highlight the benefits of using MR-Base for these types of analyses.

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Prioritizing Causal Risk Factors for Severe COVID-19: An Exhaustive Mendelian Randomization Study

10.21203/rs.3.rs-149087/v1 ◽

2021 ◽

Cited By ~ 1

Author(s):

Yitang Sun ◽

Jingqi Zhou ◽

Kaixiong Ye

Keyword(s):

Risk Factors ◽

Fat Mass ◽

Blood Cells ◽

Drug Targets ◽

Large Scale ◽

Mendelian Randomization ◽

White Blood Cells ◽

Fat Free Mass ◽

Cell Counts ◽

Increased Risk

Abstract Identifying causal risk factors for severe coronavirus disease 2019 (COVID-19) is critical for its prevention and treatment. Many associated pre-existing conditions and biomarkers have been reported, but these observational associations suffer from confounding and reverse causation. Here, we perform a large-scale two-sample Mendelian randomization (MR) analysis to evaluate the causal roles of many traits in severe COVID-19. Our results highlight multiple body mass index (BMI)-related traits as risk-increasing: BMI (OR:1.89, 95% CI:1.51–2.37), hip circumference (OR:1.46, 1.15–1.85), and waist circumference (OR:1.82, 1.36–2.43). Our multivariable MR analysis further shows that the BMI-related effect is driven by fat mass (OR:1.63, 1.03–2.58), but not fat-free mass (OR:1.00, 0.61–1.66). Several white blood cell counts are negatively associated with severe COVID-19, including those of neutrophils (OR:0.76, 0.61–0.94), granulocytes (OR:0.75, 0.601–0.93), and myeloid white blood cells (OR:0.77, 0.62–0.96). Furthermore, some circulating proteins are associated with an increased risk of (e.g., zinc-alpha-2-glycoprotein) or protection from severe COVID-19 (e.g., interleukin-3/6 receptor subunit alpha). Our study shows that fat mass and white blood cells underlie the etiology of severe COVID-19. It also identifies risk and protective factors that could serve as drug targets and guide the effective protection of high-risk individuals.

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Prioritizing the Role of Major Lipoproteins and Subfractions as Risk Factors for Peripheral Artery Disease

10.1101/2021.01.11.21249148 ◽

2021 ◽

Author(s):

Michael G. Levin ◽

Verena Zuber ◽

Venexia M. Walker ◽

Derek Klarin ◽

Julie Lynch ◽

...

Keyword(s):

Risk Factors ◽

Drug Targets ◽

Particle Concentration ◽

Mendelian Randomization ◽

Inclusion Probability ◽

Lipoprotein Subfractions ◽

Artery Disease ◽

Lowering Drug ◽

Cad Risk

ABSTRACTBackgroundCirculating lipid and lipoprotein levels have consistently been identified as risk factors for atherosclerotic cardiovascular disease (ASCVD), largely on the basis of studies focused on coronary artery disease (CAD). The relative contributions of specific lipoproteins to risk of peripheral artery disease (PAD) have not been well-defined. Here, we leveraged large scale genetic association data to identify genetic proxies for circulating lipoprotein-related traits, and employed Mendelian randomization analyses to investigate their effects on PAD risk.MethodsGenome-wide association study summary statistics for PAD (Veterans Affairs Million Veteran Program, 31,307 cases) and CAD (CARDIoGRAMplusC4D, 60,801 cases) were used in the Mendelian Randomization Bayesian model averaging (MR-BMA) framework to prioritize the most likely causal major lipoprotein and subfraction risk factors for PAD and CAD. Mendelian randomization was used to estimate the effect of apolipoprotein B lowering on PAD risk using gene regions that proxy potential lipid-lowering drug targets. Transcriptome-wide association studies were performed to identify genes relevant to circulating levels of prioritized lipoprotein subfractions.ResultsApoB was identified as the most likely causal lipoprotein-related risk factor for both PAD (marginal inclusion probability 0.86, p = 0.003) and CAD (marginal inclusion probability 0.92, p = 0.005). Genetic proxies for ApoB-lowering medications were associated with reduced risk of both PAD (OR 0.87 per 1 standard deviation decrease in ApoB, 95% CI 0.84 to 0.91, p = 9 × 10−10) and CAD (OR 0.66, 95% CI 0.63 to 0.69, p = 4 × 10−73), with a stronger predicted effect of ApoB-lowering on CAD (ratio of ORs 1.33, 95% CI 1.25 to 1.42, p = 9 × 10−19). Among ApoB-containing subfractions, extra-small VLDL particle concentration (XS.VLDL.P) was identified as the most likely subfraction associated with PAD risk (marginal inclusion probability 0.91, p = 2.3 × 10−4), while large LDL particle concentration (L.LDL.P) was the most likely subfraction associated with CAD risk (marginal inclusion probability 0.95, p = 0.011). Genes associated with XS.VLDL.P and L.LDL.P included canonical ApoB-pathway components, although gene-specific effects varied across the lipoprotein subfractions.ConclusionApoB was prioritized as the major lipoprotein fraction causally responsible for both PAD and CAD risk. However, diverse effects of ApoB-lowering drug targets and ApoB-containing lipoprotein subfractions on ASCVD, and distinct subfraction-associated genes suggest possible biologic differences in the role of lipoproteins in the pathogenesis of PAD and CAD.

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Hypothesis-free analysis of deep vein thrombosis aetiology: a Mendelian randomization study

10.1101/476135 ◽

2018 ◽

Author(s):

Andrei-Emil Constantinescu ◽

Caroline J Bull ◽

Jie Zheng ◽

Benjamin Elsworth ◽

Ingeborg Hers ◽

...

Keyword(s):

Risk Factors ◽

Deep Vein Thrombosis ◽

Mendelian Randomization ◽

Genetic Data ◽

Sensitivity Analyses ◽

Major Public Health Problem ◽

Genome Wide Association Studies ◽

Vein Thrombosis ◽

Novel Risk Factors ◽

Deep Vein

SummaryBackgroundDeep vein thrombosis (DVT) is the formation of a thrombus/clot in the deep veins, when part of this clot breaks off it can travel to the lungs, resulting in pulmonary embolism. These two conditions together are known as venous thromboembolism (VTE), a leading cause of death and disability worldwide. Despite the prevalence of VTE, we do not fully understand what causes it and it is often overlooked as a major public health problem. Confirming and identifying risk factors associated with DVT is likely to lead to a reduction in the incidence, morbidity and mortality of VTE especially where these risk factors are modifiable. We can do this, by exploiting the availability of summary genetic data from genome-wide association studies (GWAS) of numerous phenotypes, including DVT, which permits hypothesis-free causal inference.ObjectivesTo identify novel risk factors for DVT and to assess the causality of factors previously shown to be associated with DVT.MethodsTwo-sample Mendelian randomization (MR) was performed using summarised genetic data. Inverse variance weighted (IVW) estimates were calculated and validated by additional methods more robust to horizontal pleiotropy (MR Egger, simple mode, weighted mode, and weighted median). Bidirectional and heterogeneity sensitivity analyses were performed to further evaluate our findings.ResultsForty-seven exposures passed an exposure-exposure correlation-adjusted Bonferroni P-value threshold (5.43E-05). These included previously hypothesised risk factors for DVT (e.g. body mass index, varicose veins, height, hyperthyroidism) and novel associations (e.g. prospective memory, basal metabolic rate).ConclusionOur analyses confirmed causal associations of risk factors previously associated with DVT and highlighted several novel risk factors for the disease. Our study demonstrates the utility of using a hypothesis free Mendelian randomization approach for the identification of novel disease risk factors.

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Using Mendelian randomization to understand and develop treatments for neurodegenerative disease

Brain Communications ◽

10.1093/braincomms/fcaa031 ◽

2020 ◽

Vol 2 (1) ◽

Cited By ~ 2

Author(s):

Catherine S Storm ◽

Demis A Kia ◽

Mona Almramhi ◽

Nicholas W Wood

Keyword(s):

Risk Factors ◽

Neurodegenerative Diseases ◽

Neurodegenerative Disease ◽

Translational Medicine ◽

Mendelian Randomization ◽

Genetic Data ◽

Molecular Pathways ◽

Disease Research ◽

The Impact

Abstract Common neurodegenerative diseases are thought to arise from a combination of environmental and genetic exposures. Mendelian randomization is a powerful way to leverage existing genetic data to investigate causal relationships between risk factors and disease. In recent years, Mendelian randomization has gathered considerable traction in neurodegenerative disease research, providing valuable insights into the aetiology of these conditions. This review aims to evaluate the impact of Mendelian randomization studies on translational medicine for neurodegenerative diseases, highlighting the advances made and challenges faced. We will first describe the fundamental principles and limitations of Mendelian randomization and then discuss the lessons from Mendelian randomization studies of environmental risk factors for neurodegeneration. We will illustrate how Mendelian randomization projects have used novel resources to study molecular pathways of neurodegenerative disease and discuss the emerging role of Mendelian randomization in drug development. Finally, we will conclude with our view of the future of Mendelian randomization in these conditions, underscoring unanswered questions in this field.

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Using the MR-Base platform to investigate risk factors and drug targets for thousands of phenotypes

Wellcome Open Research ◽

10.12688/wellcomeopenres.15334.1 ◽

2019 ◽

Vol 4 ◽

pp. 113 ◽

Cited By ~ 1

Author(s):

Venexia M Walker ◽

Neil M Davies ◽

Gibran Hemani ◽

Jie Zheng ◽

Philip C Haycock ◽

...

Keyword(s):

Risk Factors ◽

Web Application ◽

Drug Targets ◽

Genome Wide Association Study ◽

Mendelian Randomization ◽

R Package ◽

Sensitivity Analyses ◽

Link Type ◽

Genome Wide ◽

Study Results

Mendelian randomization (MR) uses genetic information to strengthen causal inference concerning the effect of exposures on outcomes. This method has a broad range of applications, including investigating risk factors and appraising potential targets for intervention. MR-Base has become established as a freely accessible, online platform, which combines a database of complete genome-wide association study results with an interface for performing Mendelian randomization and sensitivity analyses. This allows the user to explore millions of potentially causal associations. MR-Base is available as a web application or as an R package. The technical aspects of the tool have previously been documented in the literature. The present article is complimentary to this as it focuses on the applied aspects. Specifically, we describe how MR-Base can be used in several ways, including to perform novel causal analyses, replicate results and enable transparency, amongst others. We also present three use cases, which demonstrate important applications of Mendelian randomization and highlight the benefits of using MR-Base for these types of analyses.

Download Full-text

Extensive Mendelian randomization study identifies potential causal risk factors for severe COVID-19

Communications Medicine ◽

10.1038/s43856-021-00061-9 ◽

2021 ◽

Vol 1 (1) ◽

Author(s):

Yitang Sun ◽

Jingqi Zhou ◽

Kaixiong Ye

Keyword(s):

Risk Factors ◽

Fat Mass ◽

Blood Cells ◽

Drug Targets ◽

Large Scale ◽

Mendelian Randomization ◽

White Blood Cells ◽

Fat Free Mass ◽

Cell Counts ◽

Increased Risk

Abstract Background Identifying causal risk factors for severe coronavirus disease 2019 (COVID-19) is critical for its prevention and treatment. Many associated pre-existing conditions and biomarkers have been reported, but these observational associations suffer from confounding and reverse causation. Methods Here, we perform a large-scale two-sample Mendelian randomization (MR) analysis to evaluate the causal roles of many traits in severe COVID-19. Results Our results highlight multiple body mass index (BMI)-related traits as risk-increasing: BMI (OR: 1.89, 95% CI: 1.51–2.37), hip circumference (OR: 1.46, 1.15–1.85), and waist circumference (OR: 1.82, 1.36–2.43). Our multivariable MR analysis further suggests that the BMI-related effect might be driven by fat mass (OR: 1.63, 1.03–2.58), but not fat-free mass (OR: 1.00, 0.61–1.66). Several white blood cell counts are negatively associated with severe COVID-19, including those of neutrophils (OR: 0.76, 0.61–0.94), granulocytes (OR: 0.75, 0.601–0.93), and myeloid white blood cells (OR: 0.77, 0.62–0.96). Furthermore, some circulating proteins are associated with an increased risk of (e.g., zinc-alpha-2-glycoprotein) or protection from severe COVID-19 (e.g., prostate-associated microseminoprotein). Conclusions Our study suggests that fat mass and white blood cells might be involved in the development of severe COVID-19. It also prioritizes potential risk and protective factors that might serve as drug targets and guide the effective protection of high-risk individuals.

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Genetic drug target validation using Mendelian randomization

10.1101/781039 ◽

2019 ◽

Cited By ~ 4

Author(s):

A F Schmidt ◽

C Finan ◽

M Gordillo-Marañón ◽

F W Asselbergs ◽

D F Freitag ◽

...

Keyword(s):

Risk Factors ◽

Drug Target ◽

Drug Targets ◽

Mendelian Randomization ◽

Analytical Framework ◽

Causal Chain ◽

Mendelian Randomisation ◽

Mathematical Framework ◽

Alternative Pathways ◽

Drug Target Validation

AbstractMendelian randomisation analysis has emerged as an important tool to elucidate the causal relevance of a range of environmental and biological risk factors for human disease. However, inference on cause is undermined if the genetic variants used to instrument a risk factor of interest also associate with other traits that open alternative pathways to the disease (horizontal pleiotropy). We show how the ‘no horizontal pleiotropy assumption’ in MR analysis is strengthened when proteins are the risk factors of interest. Proteins are the proximal effectors of biological processes encoded in the genome, and are becoming assayable on an-omics scale. Moreover, proteins are the targets of most medicines, so Mendelian randomization (MR) studies of drug targets are becoming a fundamental tool in drug development. To enable such studies we introduce a formal mathematical framework that contrasts MR analysis of proteins with that of risk factors located more distally in the causal chain from gene to disease. Finally, we illustrate key model decisions and introduce an analytical framework for maximizing power and elucidating the robustness of drug target MR analyses.

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Leveraging Genetic Data to Elucidate the Relationship Between COVID‐19 and Ischemic Stroke

Journal of the American Heart Association ◽

10.1161/jaha.121.022433 ◽

2021 ◽

Author(s):

Verena Zuber ◽

Alan Cameron ◽

Evangelos P. Myserlis ◽

Leonardo Bottolo ◽

Israel Fernandez‐Cadenas ◽

...

Keyword(s):

Risk Factors ◽

Body Mass Index ◽

Ischemic Stroke ◽

Body Mass ◽

Mendelian Randomization ◽

Genetic Data ◽

C Reactive Protein ◽

Reactive Protein ◽

Increased Risk ◽

The Relationship

Background The relationship between COVID‐19 and ischemic stroke is poorly understood due to potential unmeasured confounding and reverse causation. We aimed to leverage genetic data to triangulate reported associations. Methods and Results Analyses primarily focused on critical COVID‐19, defined as hospitalization with COVID‐19 requiring respiratory support or resulting in death. Cross‐trait linkage disequilibrium score regression was used to estimate genetic correlations of critical COVID‐19 with ischemic stroke, other related cardiovascular outcomes, and risk factors common to both COVID‐19 and cardiovascular disease (body mass index, smoking and chronic inflammation, estimated using C‐reactive protein). Mendelian randomization analysis was performed to investigate whether liability to critical COVID‐19 was associated with increased risk of any cardiovascular outcome for which genetic correlation was identified. There was evidence of genetic correlation between critical COVID‐19 and ischemic stroke (r g =0.29, false discovery rate [FDR]=0.012), body mass index (r g =0.21, FDR=0.00002), and C‐reactive protein (r g =0.20, FDR=0.00035), but no other trait investigated. In Mendelian randomization, liability to critical COVID‐19 was associated with increased risk of ischemic stroke (odds ratio [OR] per logOR increase in genetically predicted critical COVID‐19 liability 1.03, 95% CI 1.00–1.06, P ‐value=0.03). Similar estimates were obtained for ischemic stroke subtypes. Consistent estimates were also obtained when performing statistical sensitivity analyses more robust to the inclusion of pleiotropic variants, including multivariable Mendelian randomization analyses adjusting for potential genetic confounding through body mass index, smoking, and chronic inflammation. There was no evidence to suggest that genetic liability to ischemic stroke increased the risk of critical COVID‐19. Conclusions These data support that liability to critical COVID‐19 is associated with an increased risk of ischemic stroke. The host response predisposing to severe COVID‐19 is likely to increase the risk of ischemic stroke, independent of other potentially mitigating risk factors.

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