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

Identification of Novel Therapeutic Targets for Hypertension

BACKGROUND

Persistently high blood pressure remains the leading risk factor for mortality worldwide. This study aims to identify potential drug targets for hypertension.

METHODS

Mendelian randomization was used to identify therapeutic targets for hypertension. Genome-wide association study summary statistics were obtained from the UK Biobank and FinnGen study. Cis-expression quantitative trait loci from the eQTLGen Consortium served as genetic instruments. Colocalization analysis evaluated the likelihood of shared causal variants between single-nucleotide polymorphisms influencing hypertension and gene expression. Survival analysis of UK Biobank data assessed hypertension and mortality risks across participants with different gene alleles.

RESULTS

Mendelian randomization analysis identified 190 drug targets in the discovery cohort and 65 in the replication cohort after multiple testing correction. Colocalization analysis identified 14 hypertension-related drug targets, including ACE, AIMP1, CDC25A, EHMT2, FES, GPX1, GRK4, HSD3B7, NEK4, PTPN12, SIK2, SLC22A4, SLC2A4, and TNFSF12. Survival analysis revealed individuals with the A allele at rs4308 in the ACE gene had a higher incidence of hypertension, while those with the T allele at rs11242109 in the SLC22A4 gene showed a lower hypertension-specific mortality rate.

CONCLUSIONS

Drug target Mendelian randomization studies offer new directions for hypertension treatment, providing insights into its mechanisms and robust targets for developing antihypertensive drugs.

Application of Human Genetics to Prioritize Coagulation Cascade Protein Targets for Ischemic Stroke Prevention

BACKGROUND

While interindividual variations in concentration and function of coagulation cascade proteins are established risk factors for venous thromboembolism (VTE), their associations with arterial ischemic stroke are less well defined.

METHODS

We identified and validated genetic proxies for lifelong, randomized perturbations of coagulation cascade proteins in genome-wide association studies of circulating protein levels (deCODE, n=35 559; UK Biobank, n=46 218) and of VTE risk (81 190 cases and 1 419 671 controls). Study participants were all of European ancestry. We performed 2-sample Mendelian randomization and colocalization analyses to test associations of these genetic proxies with risk of ischemic stroke (62 100 cases and 1 234 808 controls from the GIGASTROKE consortium) and ischemic stroke subtypes, and further contextualized associations with VTE and secondary efficacy and safety outcomes.

RESULTS

We identified genetic proxies for 30 coagulation factors, with cross-trait associations recapitulating canonical coagulation biology. Mendelian randomization and colocalization analyses supported causal associations of genetically proxied levels of 5 proteins with risk of ischemic stroke, with all proteins associating with the cardioembolic stroke subtype: factor XI (odds ratio [OR] of cardioembolic stroke per 1-SD increase, 1.31 [95% CI, 1.19-1.44]; P=3.30×10-8), high-molecular-weight kininogen (OR, 1.19 [95% CI, 1.09-1.30]; P=7.79×10-5), prothrombin (OR, 1.83 [95% CI, 1.31-2.57]; P=4.20×10-4), soluble PROCR (protein C receptor; OR, 0.88 [95% CI, 0.82-0.95]; P=6.19×10-4), and γ' fibrinogen (OR per doubling in VTE risk due to lower γ' fibrinogen levels, 1.44 [95% CI, 1.25-1.66]; P=3.96×10-7). γ' Fibrinogen and prothrombin also associated with large artery atherosclerotic stroke, and no proteins were associated with small vessel stroke risk. By contrast, genetic proxies for several coagulation factors (including proteins C and S and factors V and VII) showed selective associations with VTE.

CONCLUSIONS

These data highlight specific coagulation cascade components implicated in ischemic stroke pathogenesis, while identifying proteins with distinct roles in VTE. These findings may inform development of novel anticoagulants and optimize their use in targeted populations with stroke.

Causal relationship between molecular markers of biological aging and orthopedic diseases: A two-sample bidirectional Mendelian randomization study

BACKGROUND

Studies indicate an association between biological aging and orthopedic diseases, but the causality remains unclear.

AIMS

This study aims to investigate the bidirectional causal relationship between molecular markers of biological aging age and orthopedic conditions.

METHODS

A two-sample Mendelian randomization (MR) analysis based on a genome-wide association study (GWAS) was conducted to explore these causal relationships. Analysis methods included inverse variance weighted (IVW), MR-Egger, weighted median, and weighted mode. Sensitivity analyses involved Cochran's Q, MR-Egger, leave-one-out, and MR pleiotropy residual sum and outlier (MR-PRESSO) tests.

RESULTS

The forward MR analysis identified several causal relationships: granulocyte proportions influenced intervertebral disc degeneration (IVDD) (OR 0.2316, P = 0.0101) and low back pain (LBP) (OR 0.2624, P = 0.007); telomere length (TL) affected cervical spondylosis (C/S) (OR 0.8759, P = 0.0167) and IVDD (OR 0.9184, P = 0.023); fibroblast growth factor-23 (FGF-23) impacted frozen shoulder (FS) (OR 1.2424, P = 0.0316); and HannumAge influenced C/S (OR 0.9518, P = 0.0233). The reverse MR analysis found that FS influenced TL (OR 0.9582, P = 0.0002) and α-Klotho (OR 0.7592, P = 0.0256), while sciatica affected TL (OR 0.9344, P = 0.0055) and C/S impacted PhenoAge (OR 1.6583, P = 0.0131) after outlier exclusion. Cochran's Q indicated heterogeneity in certain analyses, and MR-Egger showed no horizontal pleiotropy in significant causal associations.

CONCLUSIONS

This study suggests a potential causal associations between molecular markers of biological aging and orthopedic diseases, suggesting avenues for future research into the underlying mechanisms.

Plasma protein levels and hepatocellular carcinoma: a Mendelian randomization study with drug screening implications

BACKGROUND

Hepatocellular carcinoma (HCC) remains a significant cause of cancer-related mortality, highlighting the need for novel therapeutic strategies. Identifying key proteins and potential therapeutic agents is critical for improving treatment outcomes.

METHODS

We employed Mendelian randomization to identify proteins associated with HCC risk and utilized drug enrichment and molecular docking analyses to discover potential therapeutic agents. The efficacy of identified drugs was evaluated in vitro using immune-tumor co-culture systems and in vivo in a murine HCC model. Single-cell expression profiling and clinical sample analyses were conducted to explore expression patterns.

RESULTS

Our analyses identified 16 proteins linked to HCC pathogenesis. Among the therapeutic agents tested, Belinostat significantly enhanced T cell-mediated cytotoxicity against HCC cells and effectively reduced tumor growth in vivo. Single-cell analysis revealed significant modulation of immune cells within the tumor microenvironment, suggesting potential mechanisms for the observed therapeutic effects.

CONCLUSION

This study highlights the potential of Belinostat as a promising therapeutic agent for HCC. By modulating immune responses and tumor growth, Belinostat offers a novel approach to HCC treatment, warranting further clinical investigation to validate its efficacy and therapeutic potential.

A causal effects of neutrophil extracellular traps and its biomarkers on acute respiratory distress syndrome: a two-sample Mendelian randomization study

Previous studies have indicated an association between neutrophil extracellular traps (NETs) and acute respiratory distress syndrome (ARDS). This study aimed to investigate the potential causal effects of NETs and NETs-related biomarkers on ARDS or vice-versa. A two-sample Mendelian randomization (MR) utilizing genome-wide association studies (GWAS) data was employed to analyze the causality. The primary analysis was conducted using inverse-variance weighted (IVW) methods; weighted median, MR-Egger, and weighted model methods were used to validate the results. Horizontal pleiotropy and outlier detection were assessed via MR-Egger and MR pleiotropy residual sum and outlier (MR-PRESSO), respectively; Cochran's Q test evaluated heterogeneity, while Leave-one-out analyses were used to evaluate the presence of predominant instrumental variables (IVs). IVW method suggested causal associations between genetically predicted IL-13 and a higher risk of ARDS [OR (95%CI) = 1.52 (1.03-2.23), P = 0.047], while there was no causal effect of other factors on ARDS (all P > 0.05). Also, ARDS had no effect on NETs and NETs-related biomarkers (all P > 0.05). Cochran's Q confirmed no significant heterogeneity. MR-Egger regression ruled out horizontal pleiotropy's influence, and MR-PRESSO analysis identified no outliers, reinforcing the study's findings. This MR study established a causal relationship between IL-13 and ARDS, suggesting its potential role as a therapeutic target and biomarker of ARDS. Future work should delve into the underlying mechanisms and clinical applications.

Genetically mimicked effects of evinacumab on psoriasis: a drug target Mendelian randomization study

BACKGROUND AND OBJECTIVES

Dyslipidemia has been reported to contribute to the psoriasis pathogenesis. Thus, evinacumab, a novel lipid-lowering drug targeting angiopoietin-like 3, may have therapeutic potential to treat and/or manage psoriasis.

METHODS AND STUDY DESIGN

Summary statistics were obtained from genome-wide association studies addressing psoriasis (FinnGen Consortium; n=216,752) and serum lipid concentrations (United Kingdom Biobank; n=403,943-440,546). Two-sample Mendelian randomization analyses were conducted to evaluate the associations of serum lipid concentrations and genetically mimicked effects of evinacumab, respectively, with the risks of psoriasis and its subtypes.

RESULTS

Genetically determined per standard deviation increase in triglyceride concentrations was associated with increased risk of psoriasis (OR: 1.17, 95% CI: 1.03-1.32, p=0.018), whereas that in low-density lipoprotein-cholesterol (LDL-C) was associated with both psoriasis (OR: 1.22, 95% CI: 1.05-1.43, p=0.011) and its subtypes, including arthropathic psoriasis (OR: 1.30, 95% CI: 1.02-1.65, p=0.032), psoriasis vulgaris (OR: 1.87, 95% CI: 1.16-2.99, p=0.0095), and guttate psoriasis (OR: 2.19, 95% CI: 1.17-4.07, p=0.014). Moreover, genetically mimicked effects of evinacumab, via angiopoietin-like 3 inhibition, significantly reduced the risk of psoriasis (OR: 0.752 per standard deviation reduction in triglycerides, 95% CI: 0.577-0.982, p=0.036) and arthropathic psoriasis (OR: 0.266 per standard deviation reduction in LDL-C, 95% CI: 0.0886-0.799, p=0.018).

CONCLUSIONS

The genetically mimicked effect of evinacumab has the potential to reduce the risk of psoriasis and arthropathic psoriasis by lowering circulating triglyceride and LDL-C concentrations, respectively. These findings suggest that evinacumab may help prevent psoriasis and psoriatic arthritis progression in clinical practice.

Human Genetic Evidence to Inform Clinical Development of IL-6 Signaling Inhibition for Abdominal Aortic Aneurysm

BACKGROUND

Abdominal aortic aneurysm (AAA) represents a significant cause of mortality, yet no medical therapies have proven efficacious. The aim of the current study was to leverage human genetic evidence to inform clinical development of IL-6 (interleukin-6) signaling inhibition for the treatment of AAA.

METHODS

Associations of rs2228145, a missense variant in the IL6R gene region, are expressed per additional copy of the C allele, corresponding to the genetically predicted effect of IL-6 signaling inhibition. We consider genetic associations with AAA risk in the AAAgen consortium (39 221 cases and 1 086 107 controls) and UK Biobank (1963 cases and 365 680 controls). To validate against known effects of IL-6 signaling inhibition, we present associations with rheumatoid arthritis, polymyalgia rheumatica, and severe COVID-19. To explore mechanism specificity, we present associations with thoracic aortic aneurysm, intracranial aneurysm, and coronary artery disease. We further explored genetic associations in clinically relevant subgroups of the population.

RESULTS

We observed strong genetic associations with AAA risk in the AAAgen consortium, UK Biobank, and FinnGen (odds ratios: 0.91 [95% CI, 0.90-0.92], P=4×10-30; 0.90 [95% CI, 0.84-0.96], P=0.001; and 0.86 [95% CI, 0.82-0.91], P=7×10-9, respectively). The association was similar for fatal AAA but with greater uncertainty due to the lower number of events. The association with AAA was of greater magnitude than associations with coronary artery disease and even rheumatological disorders for which IL-6 inhibitors have been approved. No strong associations were observed with thoracic aortic aneurysm or intracranial aneurysm. Associations attenuated toward the null in populations with concomitant rheumatological or connective tissue disease.

CONCLUSIONS

Inhibition of IL-6 signaling is a promising strategy for treating AAA but not other types of aneurysmal disease. These findings serve to help inform clinical development of IL-6 signaling inhibition for AAA treatment.

Mendelian Randomization Study Reveals Causal Pathways for Hypertrophic Cardiomyopathy, Cardiovascular Proteins, and Atrial Fibrillation

Aims/Background Research evidence has demonstrated a significant association between hypertrophic cardiomyopathy (HCM) and atrial fibrillation (AF), but the causality and pattern of this link remain unexplored. Therefore, this study investigated the causal relationship between HCM and AF using a two-sample and bidirectional Mendelian randomization (MR) approach. Additionally, this assessed the role of cardiovascular proteins (CPs) associated with cardiovascular diseases between HCM and AF by applying a two-step MR analysis. Methods Data for HCM, AF, and 90 CPs were obtained from the Finn Gen and IEU Open GWAS Project databases. MR-Egger, inverse variance weighting (IVW), weighted median estimator (WME), weighted mode, and simple mode were used to estimate causal inferences. Furthermore, Cochran's Q test, MR-Egger's intercept terms, and Leave-one-out methods determined the heterogeneity, horizontal pleiotropy, and sensitivity. Additionally, mediation effects were used to assess the role of CPs in the relationship between HCM and AF. Results Two-sample and bidirectional MR analysis revealed HCM as a risk factor for AF (odds ratio (OR) = 1.008, 95% confidence interval (CI): 1.001-1.016, p = 0.029) and AF was found to increase the risk of developing HCM (OR = 1.145, 95% CI: 0.963-1.361, p = 0.126). Moreover, Two-step MR analyses indicated that 5 CPs were causally associated with HCM; 12 CPs with AF and 1 CP (Melusin) with both HCM and AF. Additionally, Melusin was observed as a protective factor for both HCM and AF and may serve as a mediator variable for these two conditions (mediation effect 0.0004, mediation ratio 5.5178%, 95% CI: 5.4624-5.5731). Conclusion HCM may increase the risk of developing AF, with Melusin serving as a mediator for this risk.

Leveraging Mendelian randomization to inform drug discovery and development for ischemic stroke

Discovery and development of efficacious and safe pharmacological therapies is fraught with challenges. As proteins constitute the majority of drug targets and are encoded by genes, naturally occurring genetic variation within populations can provide valuable insights to inform drug discovery and development efforts. The drug target Mendelian randomization (MR) paradigm leverages these principles to investigate the causal effects of drug targets in humans. This review examines the application of drug target MR in informing the efficacy and development of therapeutics for ischemic stroke prevention and treatment. We consider applications of MR for existing and novel treatment strategies, including targeting blood pressure, lipid metabolism, coagulation, inflammation and glycemic control. Several of these genetically supported targets are under evaluation in late-stage clinical trials. Methodological limitations of drug target MR are addressed, followed by an outline of future research directions. We anticipate that careful application of drug target MR will enhance the efficiency of drug development for ischemic stroke, consequently accelerating the delivery of effective medications to patients.

Novel candidate plasma proteins for the pathogenesis and treatment of atopic dermatitis revealed by proteome-wide association study

Atopic dermatitis (AD) is an immune-related skin disease with a genetic background. Numerous loci have been identified associated with AD to better comprehend its complicated genetic mechanisms by genome‑wide association studies (GWASs). However, current studies reveal the underlying mechanisms of these loci in the pathogenesis of AD inadequately. Therefore, we integrated the GWAS statistics of AD with plasma proteins to explore candidate proteins correlated with the pathogenesis of AD based on protein-centered omics studies. Herein, we adopted the updated AD GWAS statistics (N = 864,982) and the dataset of plasma protein quantitative trait loci (pQTLs), comprising 1,348 proteins from individuals of European descent. We first conducted the AD-related proteome-wide association studies (PWASs) (N = 7,213) by integrating pQTLs with the AD GWAS statistics and identified twenty-six significant plasma proteins by PWAS (FDR < 0.05). Then, the potential causal proteins of AD were identified via Mendelian randomization (MR), and seventeen causal proteins of AD were discovered afterward. Following this, Bayesian colocalization analysis was then utilized to explore proteins sharing the same causal variants. Five causal proteins strongly associated with the pathogenesis of AD were eventually pinpointed. Finally, we discovered drugs that could be repurposed for AD with the plasma proteins that might contribute to the pathogenesis of AD in the Drug Gene Interaction Database.

Genome-wide Mendelian randomization identifies drugs associated with body height

Background

Mendelian randomization (MR) has been used to identify drug targets in many conditions. Height is a classic complex trait affected by genetic and early-life environmental factors. No systematic screening has been conducted to identify drugs that interact with height. We investigated the causal relationship between genes and height, and systematically screened for interactive drugs that may promote or delay growth.

Methods

We performed MR using summary statistics from the Genetic Investigation of ANthropometric Traits consortium (N=253,288), the UK Biobank (N=461,950), and the BioBank Japan Project (N=159,095). Gene expression-single-nucleotide polymorphism associations represented by cis-expression quantitative trait loci data were obtained from the Genotype-Tissue Expression study and were used as genetic instruments. We performed annotation and enrichment analyses of the genes. Interactive drugs were identified through drug-gene interactions.

Results

Of the 27,094 genes screened, 209 had causal associations with height, including genes associated with height and short stature phenotypes (AMZ1, GNA12, NPPC, UQCC1, and ZBTB38), genes associated with height in a few studies (ANKIB1, CEP250, DCAF16, HIST1H4E, and HLA-C), and genes without previous evidence (BTN2A2 and RBMS1P1). Enrichment analysis showed that transcriptional regulation by RUNX1 was the most enriched pathway. Interactive drugs were identified, including amoxicillin, atenolol, infliximab, colchicine, propionyl-L-carnitine, BMN-111, and tamoxifen, which were known to have a positive effect on height. We also identified drugs that had a negative effect on height, including antineoplastic drugs, corticosteroids, and antiepileptic drugs. Moreover, many interactive drugs have not been previously reported to be associated with height.

Conclusions

Our results suggest that many genes have causal effects on height. By interrogating drug-gene interactions, interactive drugs have been identified as having both positive and negative effects on growth, which would help make clinical decisions.

Causal inference in health and disease: a review of the principles and applications of Mendelian randomization

Mendelian randomization (MR) is a genetic epidemiological technique that uses genetic variation to infer causal relationships between modifiable exposures and outcome variables. Conventional observational epidemiological studies are subject to bias from a range of sources; MR analyses can offer an advantage in that they are less prone to bias as they use genetic variants inherited at conception as "instrumental variables", which are proxies of an exposure. However, as with all research tools, MR studies must be carefully designed to yield valuable insights into causal relationships between exposures and outcomes, and to avoid biased or misleading results that undermine the validity of the causal inferences drawn from the study. In this review, we outline Mendel's laws of inheritance, the assumptions and principles that underlie MR, MR study designs and methods, and how MR analyses can be applied and reported. Using the example of serum phosphate concentrations on liability to kidney stone disease we illustrate how MR estimates may be visualized and, finally, we contextualize MR in bone and mineral research including exemplifying how this technique could be employed to inform clinical studies and future guidelines concerning BMD and fracture risk. This review provides a framework to enhance understanding of how MR may be used to triangulate evidence and progress research in bone and mineral metabolism as we strive to infer causal effects in health and disease.

Common pitfalls in drug target Mendelian randomization and how to avoid them

BACKGROUND

Drug target Mendelian randomization describes the use of genetic variants as instrumental variables for studying the effects of pharmacological agents. The paradigm can be used to inform on all aspects of drug development and has become increasingly popular over the last decade, particularly given the time- and cost-efficiency with which it can be performed even before commencing clinical studies.

MAIN BODY

In this review, we describe the recent emergence of drug target Mendelian randomization, its common pitfalls, how best to address them, as well as potential future directions. Throughout, we offer advice based on our experiences on how to approach these types of studies, which we hope will be useful for both practitioners and those translating the findings from such work.

CONCLUSIONS

Drug target Mendelian randomization is nuanced and requires a combination of biological, statistical, genetic, epidemiological, clinical, and pharmaceutical expertise to be utilized to its full potential. Unfortunately, these skillsets are relatively infrequently combined in any given study.

Large-scale genome-wide interaction analyses on multiple cardiometabolic risk factors to identify age-specific genetic risk factors

The genetic landscape of cardiometabolic risk factors has been explored extensively. However, insight in the effects of genetic variation on these risk factors over the life course is sparse. Here, we performed genome-wide interaction studies (GWIS) on different cardiometabolic risk factors to identify age-specific genetic risks. This study included 270,276 unrelated European-ancestry participants from the UK Biobank (54.2% women, a median age of 58 [interquartile range (IQR): 50, 63] years). GWIS models with interaction terms between genetic variants and age were performed on apolipoprotein B (ApoB), low-density lipoprotein-cholesterol (LDL-C), log-transformed triglycerides (TG), body mass index (BMI) and systolic blood pressure (SBP). Replication was subsequently performed in the Copenhagen General Population Study (CGPS) and the Estonian Biobank (EstBB). Multiple lead variants were identified to have genome-wide significant interactions with age (Pinteraction < 1e - 08). In detail, rs429358 (tagging APOE4) was identified for ApoB (Pinteraction = 9.0e - 14) and TG (Pinteraction = 5.4e - 16). Three additional lead variants were identified for ApoB: rs11591147 (R46L in PCSK9, Pinteraction = 3.9e - 09), rs34601365 (near APOB, Pinteraction = 8.4e - 09) and rs17248720 (near LDLR, Pinteraction = 2.0e - 09). Effect sizes of the identified lead variants were generally closer to the null with increasing age. No variant-age interactions were identified for LDL-C, SBP and BMI. The significant interactions of rs429358 with age on ApoB and TG were replicated in both CGPS and EstBB. The majority of genetic effects on cardiometabolic risk factors remain relatively constant over age, with the noted exceptions of specific genetic effects on ApoB and TG.

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.

Characteristics of immune cells and causal relationship with chondromalacia: A two-sample, bidirectional mendelian randomization study

Chondromalacia, characterized by the softening of cartilage, is a prevalent condition affecting joint health with complex etiology. The immune system's role in its pathogenesis has been implicated but remains to be fully elucidated. To address a critical knowledge gap, we conducted a two-sample Mendelian randomization analysis of 731 immune cell phenotypes, assessing parameters like fluorescence, cell count, and morphology. After sensitivity and pleiotropy checks, and applying a false discovery rate correction, our study linked 17 phenotypes to chondromalacia (p < .05). Among them, seven immune cell phenotypes were found to have a protective effect against chondromalacia (IVW: p < .05, OR <1), while 10 were considered risk factors (IVW:p < .05, OR >1). Despite the constraints of sample size and possible genetic differences among populations, our research has identified a notable genetic correlation between specific immune cell indicators and chondromalacia. This breakthrough sheds light on the pathophysiological mechanisms of the condition. The identification of protective and risk-associated immune cell phenotypes provides a foundation for further exploration of immunological mechanisms in chondromalacia and may pave the way for targeted interventions. Future research is warranted to validate these findings and explore their clinical implications.

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.