Mendelian randomization as a tool to inform drug development using human genetics

Identification of Potential Drug Targets for Myopia Through Mendelian Randomization

Purpose

The purpose of this study was to identify potential drug targets for myopia and explore underlying mechanisms.

Methods

Mendelian randomization (MR) was implemented to assess the effect of 2684 pharmacologically targetable genes in the blood and retina on the risk of myopia from a genomewide association study (GWAS) for age-at-onset of spectacle wearing-inferred mean spherical equivalent (MSE; discovery cohort, N = 287,448, European), which was further validated in a GWAS for autorefraction-measured MSE (replication cohort, N = 95,619, European). The reliability of the identified significant potential targets was strengthened by colocalization analysis. Additionally, enrichment analysis, protein-protein interaction network, and molecular docking were performed to explore the functional roles and the druggability of these targets.

Results

This systematic drug target identification has unveiled 6 putative genetically causal targets for myopia-CD34, CD55, Wnt3, LCAT, BTN3A1, and TSSK6-each backed by colocalization evidence in adult blood eQTL datasets. Functional analysis found that dopaminergic neuron differentiation, cell adhesion, Wnt signaling pathway, and plasma lipoprotein-associated pathways may be involved in myopia pathogenesis. Finally, drug prediction and molecular docking corroborated the pharmacological value of these targets with LCAT demonstrating the strongest binding affinity.

Conclusions

Our study not only opens new avenues for the development of therapeutic interventions for myopia but may also help to understand the underlying mechanisms of myopia.

Mechanisms of Hypercoagulability Driving Stroke Risk in Obesity: A Mendelian Randomization Study

BACKGROUND AND OBJECTIVES

Obesity is hypothesized to induce a hypercoagulable state that increases stroke risk. The molecular mechanisms underlying this association are largely uncharacterized. We aimed to apply mendelian randomization to identify whether the association of genetically proxied body mass index (BMI) with cardioembolic stroke risk is mediated by changes in levels of circulating coagulation factors.

METHODS

Genetic proxies for BMI and levels of circulating coagulation factors were obtained, respectively, from the Genetic Investigation of ANthropometric Traits consortium (n = 694,649) and deCODE cohort (n = 35,559). Genetic associations with cardioembolic stroke risk were obtained from the GIGASTROKE consortium (10,804 cases and 1,234,804 controls). We performed a two-sample mendelian randomization analysis testing the association of genetically proxied BMI with cardioembolic stroke risk, genetically proxied BMI with levels of coagulation factors, and genetically proxied levels of coagulation factors with cardioembolic stroke risk. These estimates were carried forward to mediation and sensitivity analyses.

RESULTS

A 1-SD increase in genetically proxied BMI associated with increased cardioembolic stroke risk (OR of cardioembolic stroke per 1-SD of BMI 1.20, 95% CI 1.08-1.33, p = 8.65 × 10-4) with similar findings in statistical sensitivity analyses more robust to the inclusion of pleiotropic variants. Genetically proxied BMI was further associated with increased levels of Factor VII, Factor Xa, Factor XI, and Protein S (all p < 5.9 × 10-6). Of these factors, genetically proxied levels of Factor XI were associated with cardioembolic stroke risk (OR of cardioembolic stroke per 1-SD increase in Factor XI levels 1.32, 1.19-1.46, p = 6.18 × 10-8). The mediated effect of genetically proxied BMI through Factor XI accounted for 26% (6%-49%) of the total effect of BMI on cardioembolic stroke.

DISCUSSION

Human genetic data support increased levels of Factor XI as a mechanistic explanation for how obesity increases cardioembolic stroke risk. The clinical relevance of this association warrants further investigation within ongoing clinical trials of Factor XI inhibition.

Robust use of phenotypic heterogeneity at drug target genes for mechanistic insights: Application of cis-multivariable Mendelian randomization to GLP1R gene region

Phenotypic heterogeneity at genomic loci encoding drug targets can be exploited by multivariable Mendelian randomization to provide insight into the pathways by which pharmacological interventions may affect disease risk. However, statistical inference in such investigations may be poor if overdispersion heterogeneity in measured genetic associations is unaccounted for. In this work, we first develop conditional F statistics for dimension-reduced genetic associations that enable more accurate measurement of phenotypic heterogeneity. We then develop a novel extension for two-sample multivariable Mendelian randomization that accounts for overdispersion heterogeneity in dimension-reduced genetic associations. Our empirical focus is to use genetic variants in the GLP1R gene region to understand the mechanism by which GLP1R agonism affects coronary artery disease (CAD) risk. Colocalization analyses indicate that distinct variants in the GLP1R gene region are associated with body mass index and type 2 diabetes (T2D). Multivariable Mendelian randomization analyses that were corrected for overdispersion heterogeneity suggest that bodyweight lowering rather than T2D liability lowering effects of GLP1R agonism are more likely contributing to reduced CAD risk. Tissue-specific analyses prioritized brain tissue as the most likely to be relevant for CAD risk, of the tissues considered. We hope the multivariable Mendelian randomization approach illustrated here is widely applicable to better understand mechanisms linking drug targets to diseases outcomes, and hence to guide drug development efforts.

Genetic assessment of efficacy and safety profiles of coagulation cascade proteins identifies Factors II and XI as actionable anticoagulant targets

Aims

Anticoagulants are routinely used by millions of patients worldwide to prevent blood clots. Yet, problems with anticoagulant therapy remain, including a persistent and cumulative bleeding risk in patients undergoing prolonged anticoagulation. New safer anticoagulant targets are needed.

Methods and results

To prioritize anticoagulant targets with the strongest efficacy [venous thromboembolism (VTE) prevention] and safety (low bleeding risk) profiles, we performed two-sample Mendelian randomization and genetic colocalization. We leveraged three large-scale plasma protein data sets (deCODE as discovery data set and Fenland and Atherosclerosis Risk in Communities as replication data sets] and one liver gene expression data set (Institut Universitaire de Cardiologie et de Pneumologie de Québec bariatric biobank) to evaluate evidence for a causal effect of 26 coagulation cascade proteins on VTE from a new genome-wide association meta-analysis of 44 232 VTE cases and 847 152 controls, stroke subtypes, bleeding outcomes, and parental lifespan as an overall measure of efficacy/safety ratio. A 1 SD genetically predicted reduction in F2 blood levels was associated with lower risk of VTE [odds ratio (OR) = 0.44, 95% confidence interval (CI) = 0.38-0.51, P = 2.6e-28] and cardioembolic stroke risk (OR = 0.55, 95% CI = 0.39-0.76, P = 4.2e-04) but not with bleeding (OR = 1.13, 95% CI = 0.93-1.36, P = 2.2e-01). Genetically predicted F11 reduction was associated with lower risk of VTE (OR = 0.61, 95% CI = 0.58-0.64, P = 4.1e-85) and cardioembolic stroke (OR = 0.77, 95% CI = 0.69-0.86, P = 4.1e-06) but not with bleeding (OR = 1.01, 95% CI = 0.95-1.08, P = 7.5e-01). These Mendelian randomization associations were concordant across the three blood protein data sets and the hepatic gene expression data set as well as colocalization analyses.

Conclusion

These results provide strong genetic evidence that F2 and F11 may represent safe and efficacious therapeutic targets to prevent VTE and cardioembolic strokes without substantially increasing bleeding risk.

Mendelian Randomization Applied to Neurology: Promises and Challenges

The Mendelian randomization (MR) paradigm allows for causal inferences to be drawn using genetic data. In recent years, the expansion of well-powered publicly available genetic association data related to phenotypes such as brain tissue gene expression, brain imaging, and neurologic diseases offers exciting opportunities for the application of MR in the field of neurology. In this review, we discuss the basic principles of MR, its myriad applications to research in neurology, and potential pitfalls of injudicious applications. Throughout, we provide examples where MR-informed findings have shed light on long-standing epidemiologic controversies, provided insights into the pathophysiology of neurologic conditions, prioritized drug targets, and informed drug repurposing opportunities. With the ever-expanding availability of genome-wide association data, we project MR to become a key driver of progress in the field of neurology. It is therefore paramount that academics and clinicians within the field are familiar with the approach.

Cerebrospinal Fluid C1-Esterase Inhibitor and Tie-1 Levels Affect Cognitive Performance: Evidence from Proteome-Wide Mendelian Randomization

OBJECTIVE

The association of cerebrospinal fluid (CSF) protein levels with cognitive function in the general population remains largely unexplored. We performed Mendelian randomization (MR) analyses to query which CSF proteins may have potential causal effects on cognitive performance.

METHODS AND ANALYSIS

Genetic associations with CSF proteins were obtained from a genome-wide association study conducted in up to 835 European-ancestry individuals and for cognitive performance from a meta-analysis of GWAS including 257,841 European-ancestry individuals. We performed Mendelian randomization (MR) analyses to test the effect of randomly allocated variation in 154 genetically predicted CSF protein levels on cognitive performance. Findings were validated by performing colocalization analyses and considering cognition-related phenotypes.

RESULTS

Genetically predicted C1-esterase inhibitor levels in the CSF were associated with a better cognitive performance (SD units of cognitive performance per 1 log-relative fluorescence unit (RFU): 0.23, 95% confidence interval: 0.12 to 0.35, p = 7.91 × 10-5), while tyrosine-protein kinase receptor Tie-1 (sTie-1) levels were associated with a worse cognitive performance (-0.43, -0.62 to -0.23, p = 2.08 × 10-5). These findings were supported by colocalization analyses and by concordant effects on distinct cognition-related and brain-volume measures.

CONCLUSIONS

Human genetics supports a role for the C1-esterase inhibitor and sTie-1 in cognitive performance.

Incorporating biological and clinical insights into variant choice for Mendelian randomisation: examples and principles

Mendelian randomisation is an accessible and valuable epidemiological approach to provide insight into the causal nature of relationships between risk factor exposures and disease outcomes. However, if performed without critical thought, we may simply have replaced one set of implausible assumptions (no unmeasured confounding or reverse causation) with another set of implausible assumptions (no pleiotropy or other instrument invalidity). The most critical decision to avoid pleiotropy is which genetic variants to use as instrumental variables. Two broad strategies for instrument selection are a biologically motivated strategy and a genome-wide strategy; in general, a biologically motivated strategy is preferred. In this review, we discuss various ways of implementing a biologically motivated selection strategy: using variants in a coding gene region for the exposure or a gene region that encodes a regulator of exposure levels, using a positive control variable and using a biomarker as the exposure rather than its behavioural proxy. In some cases, a genome-wide analysis can provide important complementary evidence, even when its reliability is questionable. In other cases, a biologically-motivated analysis may not be possible. The choice of genetic variants must be informed by biological and functional considerations where possible, requiring collaboration to combine biological and clinical insights with appropriate statistical methodology.

Unlocking the Medicinal Mysteries: Preventing Lacunar Stroke with Drug Repurposing

Currently, only the general control of the risk factors is known to prevent lacunar cerebral infarction, but it is unknown which type of medication for controlling the risk factors has a causal relationship with reducing the risk of lacunar infarction. To unlock this medical mystery, drug-target Mendelian randomization analysis was applied to estimate the effect of common antihypertensive agents, hypolipidemic agents, and hypoglycemic agents on lacunar stroke. Lacunar stroke data for the transethnic analysis were derived from meta-analyses comprising 7338 cases and 254,798 controls. We have confirmed that genetic variants mimicking calcium channel blockers were found to most stably prevent lacunar stroke. The genetic variants at or near HMGCR, NPC1L1, and APOC3 were predicted to decrease lacunar stroke incidence in drug-target MR analysis. These variants mimic the effects of statins, ezetimibe, and antisense anti-apoC3 agents, respectively. Genetically proxied GLP1R agonism had a marginal effect on lacunar stroke, while a genetically proxied improvement in overall glycemic control was associated with reduced lacunar stroke risk. Here, we show that certain categories of drugs currently used in clinical practice can more effectively reduce the risk of stroke. Repurposing several drugs with well-established safety and low costs for lacunar stroke prevention should be given high priority when doctors are making decisions in clinical practice. This may contribute to healthier brain aging.