Mendelian randomization: a novel approach for the prediction of adverse drug events and drug repurposing opportunities

Mendelian randomization study to investigate the causal relationship between plasma homocysteine and chronic obstructive pulmonary disease

BACKGROUND

Several observational studies have shown an association between homocysteine (Hcy) levels and chronic obstructive pulmonary disease (COPD), but causal relationships are not clear. Our study aimed to explore the causal relationship between plasma Hcy and COPD by two-sample Mendelian randomization (MR).

METHODS

A two-sample MR study was performed to infer the causal link. Genetically predicted plasma Hcy was selected as an instrumental variable (IV) from published genome-wide association study (GWAS) meta-analyses. COPD with different etiologies was extracted as outcome variables from other GWAS meta-analyses. The main MR analysis was performed using the inverse-variance weighted (IVW) method. Additional analyses were further performed using Cochran's Q-test and MR-Egger regression to evaluate the heterogeneity or horizontal pleiotropy of our findings.

RESULTS

MR analysis showed no significant association between plasma Hcy and COPD. The results of the groups were consistent with the sensitivity analysis and repeated analysis, without heterogeneity or horizontal pleiotropy. The IVW results showed COPD hospital admissions (odds ratio [OR] 1.06, 95% confidence interval [CI] 0.91-1.24, P=0.42), asthma/COPD (OR 0.97, 95% CI 0.89-1.06, P=0.55), COPD-related chronic infection (OR 1.50, 95% CI 0.57-3.99, P=0.41), COPD/asthma/interstitial lung disease (ILD)-related pneumonia or pneumonia-derived septicemia (OR 0.93, 95% CI 0.86-1.02, P=0.13), and COPD-related respiratory insufficiency (OR 1.00, 95% CI 0.7-1.44, P=0.99).

CONCLUSION

There is no direct causal relationship between plasma Hcy and COPD in our study. As Hcy is known to have deleterious effects on endothelial function and vascular homeostasis, further studies are needed to investigate whether additional factors mediate the association between Hcy and COPD.

Genome-wide association and Mendelian randomization study of fibroblast growth factor 21 reveals causal associations with hyperlipidemia and possibly NASH

BACKGROUND

Fibroblast growth factor 21 (FGF21) is a hepatokine that produces metabolic benefits, such as improvements of lipid profile. We performed a genome-wide association study (GWAS) to identify genetic variants associated with circulating FGF21 and investigated the causal effects of FGF21 on pertinent outcomes using Mendelian randomization (MR).

METHODS

We conducted a GWAS testing ∼7.8 million DNA sequence variants with circulating FGF21 in a discovery cohort of 6259 Swedish adults with replication in 4483 Swedish women. We then performed two-sample MR analyses of genetically predicted circulating FGF21 in relation to alcohol and nutrient intake, cardiovascular and metabolic biomarkers and diseases, and liver function biomarkers using publicly available GWAS summary statistics data.

RESULTS

Our GWAS identified multiple single-nucleotide polymorphisms with genome-wide significant associations (P < 5 × 10^-8) with circulating FGF21 on chromosomes 2 and 19 in or near the GCKR and FGF21 genes, respectively. The strongest signal at the FGF21 locus (rs2548957, β = 0.181, P < 2.18 × 10^-42) displayed in two-sample MR analyses robust associations with lower alcohol intake, lower circulating low-density lipoprotein cholesterol, apolipoprotein B, C-reactive protein, gamma-glutamyl transferase, and galectin-3 concentrations, and higher circulating insulin-like growth factor-I and alkaline phosphatase concentrations after correcting for multiple testing (P < 0.0018) whereas associations with fat mass, type 2 diabetes, and cardiovascular disease were largely null.

CONCLUSIONS

We identified robust associations of certain genetic variants in or near the GCKR and FGF21 genes with circulating FGF21 concentrations. Furthermore, our results support a strong causal effect of FGF21 on improved lipid profile, reduced alcohol consumption and C-reactive protein concentrations, and liver function biomarkers including fibrosis. We found largely null or weak positive associations with fat mass, diabetes, and cardiovascular disease as well as higher insulin-like growth factor-I concentrations, which could indicate a compensatory increase to regulate the above FGF21 resistant states in humans.

Genetic support of a causal relationship between iron status and atrial fibrillation: a Mendelian randomization study

Background

Atrial fibrillation is the most common arrhythmia disease. Animal and observational studies have found a link between iron status and atrial fibrillation. However, the causal relationship between iron status and AF remains unclear. The purpose of this investigation was to use Mendelian randomization (MR) analysis, which has been widely applied to estimate the causal effect, to reveal whether systemic iron status was causally related to atrial fibrillation.

Methods

Single nucleotide polymorphisms (SNPs) strongly associated (P < 5 × 10−8) with four biomarkers of systemic iron status were obtained from a genome-wide association study involving 48,972 subjects conducted by the Genetics of Iron Status consortium. Summary-level data for the genetic associations with atrial fibrillation were acquired from the AFGen (Atrial Fibrillation Genetics) consortium study (including 65,446 atrial fibrillation cases and 522,744 controls). We used a two-sample MR analysis to obtain a causal estimate and further verified credibility through sensitivity analysis.

Results

Genetically instrumented serum iron [OR 1.09; 95% confidence interval (CI) 1.02–1.16; p = 0.01], ferritin [OR 1.16; 95% CI 1.02–1.33; p = 0.02], and transferrin saturation [OR 1.05; 95% CI 1.01–1.11; p = 0.01] had positive effects on atrial fibrillation. Genetically instrumented transferrin levels [OR 0.90; 95% CI 0.86–0.97; p = 0.006] were inversely correlated with atrial fibrillation.

Conclusion

In conclusion, our results strongly elucidated a causal link between genetically determined higher iron status and increased risk of atrial fibrillation. This provided new ideas for the clinical prevention and treatment of atrial fibrillation.

Association between Genetically Proxied Inhibition of HMG-CoA Reductase and Age at Onset of Huntington’s Disease

Background: Previous studies have found that statins may play a potential role in the age at onset (AAO) of Huntington’s disease (HD). We performed this Mendelian randomization (MR) study to assess the association between genetically proxied inhibition of 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase and low-density lipoprotein (LDL) cholesterol with age at onset of HD. Methods: Single-nucleotide polymorphisms (SNPs) in HMG-CoA reductase associated with LDL cholesterol in a genome-wide association study (GWAS) analysis were used. The summary data of residual AAO of HD were obtained from a GWAS meta-analysis (n = 9064 HD patients). MR estimates representing lifelong inhibition of drug targets were generated using random-effects inverse-variance weighted analysis. Results: Genetically proxied plasma LDL cholesterol (β = 0.039, 95% CI = −0.454 to 0.531) and HMG-CoA reductase inhibition equivalent to a 1 mmol/L (38.7 mg/dL) reduction in LDL cholesterol (β = −2.228, 95% CI = −4.830 to 0.374) were not associated with age at onset of HD. Conclusion: The plasma LDL cholesterol levels and the reduction of plasma LDL cholesterol levels by the inhibition of HMG-CoA reductase (i.e., statins) were not associated with the age of HD onset.

Evaluating the impact of metformin targets on the risk of osteoarthritis: a mendelian randomization study

OBJECTIVE

To provide some causal evidence concerning the effects of metformin on osteoarthritis (OA) using two metformin targets, namely AMP-activated protein kinase (AMPK) and growth differentiation factor 15 (GDF-15) as metformin proxies.

METHODS

This is a 2-sample Mendelian randomization design. We constructed 44 AMPK-related variants genetically predicted in HbA1c (%) as instruments for AMPK and five variants strongly predicted GDF-15 as instruments for GDF-15. Summary-level data for three OA phenotypes, including OA at any site, knee OA, and hip OA were obtained from the largest genome-wide meta-analysis across the UK Biobank and arcOGEN with 455,211 Europeans. Main analyses were conducted using the inverse-variance weighted method. Weighted median and MR-Egger were conducted as sensitivity analyses to assess the robustness of our results.

RESULTS

Genetically predicted AMPK were negatively associated with OA at any site (OR: 0.60; 95% CI: 0.43-0.83) and hip OA (OR: 0.42; 95% CI: 0.22-0.80), but with not knee OA (OR: 0.85; 95% CI: 0.49-1.50). Higher levels of genetically predicted GDF-15 reduced the risk of hip OA (OR: 0.95; 95% CI: 0.90-0.99), but not OA at any site (OR: 1.00; 95% CI: 0.98-1.02) and knee OA (OR: 1.02; 95% CI: 0.98-1.07).

CONCLUSION

This study indicates that AMPK and GDF-15 can be potential therapeutic targets for OA, especially for hip OA, and metformin would be repurposed for OA therapy which needs to be verified in randomized controlled trials.

Identification and single-base gene-editing functional validation of a cis-EPO variant as a genetic predictor for EPO-increasing therapies

Hypoxia-inducible factor prolyl hydroxylase inhibitors (HIF-PHIs) are currently under clinical development for treating anemia in chronic kidney disease (CKD), but it is important to monitor their cardiovascular safety. Genetic variants can be used as predictors to help inform the potential risk of adverse effects associated with drug treatments. We therefore aimed to use human genetics to help assess the risk of adverse cardiovascular events associated with therapeutically altered EPO levels to help inform clinical trials studying the safety of HIF-PHIs. By performing a genome-wide association meta-analysis of EPO (n = 6,127), we identified a cis-EPO variant (rs1617640) lying in the EPO promoter region. We validated this variant as most likely causal in controlling EPO levels by using genetic and functional approaches, including single-base gene editing. Using this variant as a partial predictor for therapeutic modulation of EPO and large genome-wide association data in Mendelian randomization tests, we found no evidence (at p < 0.05) that genetically predicted long-term rises in endogenous EPO, equivalent to a 2.2-unit increase, increased risk of coronary artery disease (CAD, OR [95% CI] = 1.01 [0.93, 1.07]), myocardial infarction (MI, OR [95% CI] = 0.99 [0.87, 1.15]), or stroke (OR [95% CI] = 0.97 [0.87, 1.07]). We could exclude increased odds of 1.15 for cardiovascular disease for a 2.2-unit EPO increase. A combination of genetic and functional studies provides a powerful approach to investigate the potential therapeutic profile of EPO-increasing therapies for treating anemia in CKD.

The Association of Serum Uric Acid Level, Gout, and Alzheimer's Disease: A Bidirectional Mendelian Randomization Study

BACKGROUND

The relationship between serum uric acid (UA) and Alzheimer's disease (AD) risk still remained ambiguous despite extensive attempts.

OBJECTIVE

Via the two-sample Mendelian randomization (MR) design, we aimed to examine the bidirectional causal relationships of serum UA, gout, and the risk of AD.

METHODS

Genetic variants of UA, gout, and AD were extracted from published genome-wide association summary statistics. The inverse-variance weighted (IVW, the primary method), and several sensitivity methods (MR-Egger, weighted median, and weighted mode) were used to calculate the effect estimates. Egger regression, MR-PRESSO and leave-one-SNP-out analysis were performed to identify potential violations.

RESULTS

Genetic proxies for serum UA concentration [odds ratio (ORIVW) = 1.09, 95% confidence interval (CI) = 1.01-1.19, p = 0.031] were related with an increased risk of AD using 25 single nucleotide polymorphisms (SNPs). This causal effect was confirmed by sensitivity analyses including MR-Egger (1.22, 1.06-1.42, p = 0.014), weighted median (1.18, 1.05-1.33, p = 0.006), and weighted mode (1.20, 1.07-1.35, p = 0.005) methods. No evidence of notable directional pleiotropy and heterogeneity were identified (p >  0.05). Three SNPs (rs2078267, rs2231142, and rs11722228) significantly drove the observed causal effects. Supportive causal effect of genetically determined gout on AD risk was demonstrated using two SNPs (ORIVW = 1.05, 95% CI = 1.00-1.11, p = 0.057). No reverse causal effects of AD on serum UA levels and gout risk were found.

CONCLUSION

The findings revealed a causal relationship between elevated serum UA level and AD risk. However, further research is still warranted to investigate whether serum UA could be a reliable biomarker and therapeutic target for AD.

Phenotypic Causal Inference Using Genome-Wide Association Study Data: Mendelian Randomization and Beyond

statistics for genome-wide association studies (GWAS) are increasingly available for downstream analyses. Meanwhile, the popularity of causal inference methods has grown as we look to gather robust evidence for novel medical and public health interventions. This has led to the development of methods that use GWAS summary statistics for causal inference. Here, we describe these methods in order of their escalating complexity, from genetic associations to extensions of Mendelian randomization that consider thousands of phenotypes simultaneously. We also cover the assumptions and limitations of these approaches before considering the challenges faced by researchers performing causal inference using GWAS data. GWAS summary statistics constitute an important data source for causal inference research that offers a counterpoint to nongenetic methods when triangulating evidence. Continued efforts to address the challenges in using GWAS data for causal inference will allow the full impact of these approaches to be realized.

Association Between C-Reactive Protein and Risk of Amyotrophic Lateral Sclerosis: A Mendelian Randomization Study

Background: Until now, the relationship between C-reactive protein (CRP) levels and amyotrophic lateral sclerosis (ALS) risk has not been fully established. It is necessary to assess whether there is a causal relationship between C-reactive protein levels and ALS risk. Objective and Methods: We aimed to determine whether CRP has causal effects on risk of ALS. In this present study, summary-level data for ALS (20,806 cases and 59,804 controls) was obtained from large analyses of genome-wide association studies. For instrumental variables, 37 single nucleotide polymorphisms that had been previously identified to be related to CRP levels were used, including 4 SNPs of conservative CRP genetic variants and 33 SNPs of liberal CRP genetic variants. MR estimates were calculated using the inverse-variance weighted method, supplemented by MR-Egger, weighted median, and MR-PRESSO methods. Results: There was no significant causal relationship between genetically predicted CRP levels and ALS risk (OR = 1.123, 95% CI = 0.963-1.309, p = 0.139) and results for the conservative CRP instruments were consistent (OR = 0.964, 95% CI = 0.830-1.119, p = 0.628). Pleiotropic bias was not observed in this study. Conclusions: This study suggests that genetically predicted CRP levels may not be a causal risk factor for ALS.

Robust inference of bi-directional causal relationships in presence of correlated pleiotropy with GWAS summary data

To infer a causal relationship between two traits, several correlation-based causal direction (CD) methods have been proposed with the use of SNPs as instrumental variables (IVs) based on GWAS summary data for the two traits; however, none of the existing CD methods can deal with SNPs with correlated pleiotropy. Alternatively, reciprocal Mendelian randomization (MR) can be applied, which however may perform poorly in the presence of (unknown) invalid IVs, especially for bi-directional causal relationships. In this paper, first, we propose a CD method that performs better than existing CD methods regardless of the presence of correlated pleiotropy. Second, along with a simple but yet effective IV screening rule, we propose applying a closely related and state-of-the-art MR method in reciprocal MR, showing its almost identical performance to that of the new CD method when their model assumptions hold; however, if the modeling assumptions are violated, the new CD method is expected to better control type I errors. Notably bi-directional causal relationships impose some unique challenges beyond those for uni-directional ones, and thus requiring special treatments. For example, we point out for the first time several scenarios where a bi-directional relationship, but not a uni-directional one, can unexpectedly cause the violation of some weak modeling assumptions commonly required by many robust MR methods. We also offer some numerical support and a modeling justification for the application of our new methods (and more generally MR) to binary traits. Finally we applied the proposed methods to 12 risk factors and 4 common diseases, confirming mostly well-known uni-directional causal relationships, while identifying some novel and plausible bi-directional ones such as between body mass index and type 2 diabetes (T2D), and between diastolic blood pressure and stroke.

Mendelian randomization in pharmacogenomics: The unforeseen potentials

Mendelian randomization (MR) is an epidemiological method that uses genetic variants to proxy an exposure predicting its causal association with an outcome. It occupies a valuable niche between observational studies and randomized trials. MR applications expanded lately, facilitated by the availability of big data, to include disease risk causation prediction, supporting evidence of prior observational data, identifying new drug targets, and drug repurposing. Concurrently, the last decade witnessed the growth of pharmacogenomics (PGx) research as a cornerstone in precision medicine. PGx research, conducted at discovery and implementation levels, resulted in validated PGx biomarkers and tests. Despite many clinically relevant PGx associations that could be translated into clinical applications, worldwide implementation is lagging far behind. The current review examines the intersection zones between MR and PGx research. MR can provide supporting evidence that allows generalizing PGx findings supporting its implementation. Interchangeability, PGx research can fuel MR studies with libraries of genetic variants of validated biological relevance. Furthermore, PGx and MR exhibit a synergistic relationship in drug discovery that can accelerate identifying new targets and repurposing old drugs. Interdisciplinary research applied by PGx researchers, epidemiologists with MR experience, and data scientists' collaborations can unlock unforeseen opportunities in accelerating precision medicine acquisition.

Mendelian Randomization Study of Heart Failure and Stroke Subtypes

Background

Whether heart failure (HF) is an independent risk factor of ischemic stroke (IS) and hemorrhagic stroke remains controversial. We employed a multivariable Mendelian randomization (MR) to further investigate the causal effects of HF on the risk of stroke and stroke subtypes.

Methods

Genetically predicted HF was selected as an instrumental variable (IV) from published genome-wide association studies (GWAS) meta-analyses. Stroke data with different etiologies were extracted as outcome variables from another two GWAS meta-analyses. The random-effects inverse variance-weighted (IVW) model was applied as the main method, along with sensitivity analysis. Atrial fibrillation (AF), coronary heart disease (CHD), and systolic blood pressure (SBP) were controlled for mediating effects in multivariable MR.

Results

Genetically predicted HF was significantly associated with any IS [odds ratio (OR), 1.39; 95% CI, 1.12–1.74; p = 0.03], large artery stroke (LAS; OR, 1.84; 95% CI, 1.27–2.65; p = 0.001), and cardioembolic stroke (CES; OR, 1.73; 95% CI, 1.21–2.47; p = 0.003), but without small vessel stroke (SVS; OR, 1.1; 95% CI, 0.80–1.52; p = 0.56) and intracerebral hemorrhage (ICH; OR, 0.86; 95% CI, 0.41–1.83; p = 0.699) in univariable MR. However, these significant associations were attenuated to the null after adjusting for confounding factor in multivariable MR.

Conclusion

There was no direct causal association between HF and stroke in our study. The association between HF and IS can be driven by AF, CHD, and SBP.

Diet-Derived Circulating Antioxidants and Risk of Stroke: A Mendelian Randomization Study

Background

Oxidative stress is crucial in stroke pathogenesis. Many cohort-based studies suggested that the intake of exogenous antioxidants originated from food may prevent stroke. However, the corresponding randomized controlled trials did not show diet-derived antioxidants have a protective effect on stroke.

Objectives

To examine the association of genetically proxied diet-derived antioxidants with stroke risk using Mendelian randomization.

Methods

We performed a two-sample Mendelian randomization (MR) analysis to evaluate the causal effect of diet-derived antioxidants on stroke risk. For exposure data, we extracted genetic variants as instrumental variables (IVs) that are strongly associated with frequently used diet-derived antioxidants, including vitamin C, vitamin E (α-tocopherol, γ-tocopherol), carotene, retinol, zinc, and selenium, from a large-scale genome-wide association study (GWAS). We obtained IVs' corresponding effect estimates on the risk of total stroke and ischemic stroke from a GWAS meta-analysis with 40,585 cases and 406,111 controls. Finally, we applied five types of Mendelian randomization analysis to obtain preliminary MR results and performed four three kinds of sensitivity analysis to verify them.

Results

According to the primary MR estimations and further sensitivity analyses, we established two robust associations after Bonferroni correction: genetically proxied circulating γ-tocopherol was causally associated with total stroke [odds ratio (OR) = 0.68, 95% confidence interval (CI) (0.52-0.88), p = 3.78E − 03] and ischemic stroke [OR = 0.66, 95% CI (0.51-0.86), p = 2.34E − 03]. There was no evidence to support the causal effect of other diet-derived antioxidants on the risk of total stroke and ischemic stroke.

Conclusion

Our study revealed a protective impact of genetic susceptibility to high circulating γ-tocopherol levels on stroke risk, providing new information on the potential therapeutic targets for primary stroke prevention.

Educational level as a cause of type 2 diabetes mellitus: Caution from triangulation of observational and genetic evidence

BACKGROUND AND OBJECTIVE: Education might be causal to type 2 diabetes mellitus (T2DM). We triangulated cohort and genetic evidence to consolidate the causality between education and T2DM.

METHODS

We obtained observational evidence from the English Longitudinal Study of Ageing (ELSA). Self-reporting educational attainment was categorised as high (post-secondary and higher), middle (secondary), and low (below secondary or no academic qualifications) in 6,786 community-dwelling individuals aged ≥ 50 years without diabetes at ELSA wave 2, who were followed until wave 8 for the first diabetes diagnosis. Additionally, we performed two-sample Mendelian randomisation (MR) using an inverse-variance weighted (IVW), MR-Egger, weighted median (WM), and weighted mode-based estimate (WMBE) method. Steiger filtering was further applied to exclude single-nucleotide polymorphisms (SNPs) that were correlated with an outcome (T2DM) stronger than exposure (education attainment).

RESULTS

We observed 598 new diabetes cases after 10.4 years of follow-up. The adjusted hazard ratios (95% CI) of T2DM were 1.20 (0.97-1.49) and 1.58 (1.28-1.96) in the middle- and low-education groups, respectively, compared to the high-education group. Low education was also associated with increased glycated haemoglobin levels. Psychosocial resources, occupation, and health behaviours fully explained these inverse associations. In the MR analysis of 210 SNPs (R² = 0.0161), the odds ratio of having T2DM per standard deviation-decreasing years (4.2 years) of schooling was 1.33 (1.01-1.75; IVW), 1.23 (0.37-4.17; MR-Egger), 1.56 (1.09-2.27; WM), and 2.94 (0.98-9.09; WMBE). However, applying Steiger filtering attenuated most MR results towards the null.

CONCLUSIONS

Our inconsistent findings between cohort and genetic evidence did not support the causality between education and T2DM.

Target Discovery for Drug Development Using Mendelian Randomization

Making drug development more efficient by identifying promising drug targets can contribute to resource savings. Identifying promising drug targets using human genetic approaches can remove barriers related to translation. In addition, genetic information can be used to identify potentially causal relationships between a drug target and disease. Mendelian randomization (MR) is a class of approaches used to identify causal associations between pairs of genetically predicted traits using data from human genetic studies. MR can be used to prioritize candidate drug targets by predicting disease outcomes and adverse events that could result from the manipulation of a drug target. The theory behind MR is reviewed, including a discussion of MR assumptions, different MR analytical methods, tests for violations of assumptions, and MR methods that can be robust to some violations of MR assumptions. A protocol to perform two-sample MR (2SMR) with summary genome-wide association study (GWAS) results is described. An example of 2SMR examining the causal relationship between low-density lipoprotein (LDL) and coronary artery disease (CAD) is provided as an illustration of the protocol.

HMG-Coenzyme A Reductase as a Drug Target for the Prevention of Ankylosing Spondylitis

Statins are an inhibitor of 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGCR). Growing evidence indicates that statins may have an anti-inflammatory effect. Whether genetically proxied HMGCR inhibition can reduce the risk of ankylosing spondylitis is unknown. We constructed an HMGCR genetic score comprising nearly randomly inherited variants significantly associated with LDL cholesterol levels within ± 100 kb from HMGCR to proxy for inhibition of HMGCR. We also constructed PCSK9 and NPC1L1 scores as well as the LDL polygenetic score to proxy for the inhibition of these drug targets as well as serum LDL cholesterol levels, respectively. We then compared the associations of these genetic scores with the risk of ankylosing spondylitis. Of 33,998 participants in the primary cohort, 12,596 individuals had been diagnosed with ankylosing spondylitis. Genetically proxied inhibition of HMGCR scaled to per mmol/L decrease in LDL cholesterol levels by the HMGCR score was associated with a lower risk of ankylosing spondylitis (OR, 0.57; 95% CI, 0.38–0.85; P value = 5.7 × 10^–3). No significant association with ankylosing spondylitis was observed for the PCSK9 score (OR, 0.89; 95% CI, 0.68–1.16) and the NPC1L1 score (OR, 1.50; 95% CI, 0.39–5.77). For the LDL score, genetically determined per mmol/L decrease in LDL cholesterol levels led to a reduced risk of ankylosing spondylitis (OR, 0.64; 95% CI, 0.43–0.94), with significant heterogeneity and pleiotropy in the estimate. Exploratory analyses showed that genetically proxied inhibition of HMGCR appeared to have a similar effect to long-term statin therapy in modifying the risk of coronary artery disease and type 2 diabetes, suggesting that the HMGCR score might be a reliable model to assess the effect of statin. Genetically proxied inhibition of HMGCR was associated with a decreased risk of ankylosing spondylitis. This mechanism-based estimate was in line with existing observations suggesting the clinical benefits of statin therapy for ankylosing spondylitis.

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.

Beyond factor H: The impact of genetic-risk variants for age-related macular degeneration on circulating factor-H-like 1 and factor-H-related protein concentrations

Age-related macular degeneration (AMD) is a leading cause of vision loss; there is strong genetic susceptibility at the complement factor H (CFH) locus. This locus encodes a series of complement regulators: factor H (FH), a splice variant factor-H-like 1 (FHL-1), and five factor-H-related proteins (FHR-1 to FHR-5), all involved in the regulation of complement factor C3b turnover. Little is known about how AMD-associated variants at this locus might influence FHL-1 and FHR protein concentrations. We have used a bespoke targeted mass-spectrometry assay to measure the circulating concentrations of all seven complement regulators and demonstrated elevated concentrations in 352 advanced AMD-affected individuals for all FHR proteins (FHR-1, p = 2.4 × 10-10; FHR-2, p = 6.0 × 10-10; FHR-3, p = 1.5 × 10-5; FHR-4, p = 1.3 × 10-3; FHR-5, p = 1.9 × 10-4) and FHL-1 (p = 4.9 × 10-4) when these individuals were compared to 252 controls, whereas no difference was seen for FH (p = 0.94). Genome-wide association analyses in controls revealed genome-wide-significant signals at the CFH locus for all five FHR proteins, and univariate Mendelian-randomization analyses strongly supported the association of FHR-1, FHR-2, FHR-4, and FHR-5 with AMD susceptibility. These findings provide a strong biochemical explanation for how genetically driven alterations in circulating FHR proteins could be major drivers of AMD and highlight the need for research into FHR protein modulation as a viable therapeutic avenue for AMD.

Sclerostin Downregulation Globally by Naturally Occurring Genetic Variants, or Locally in Atherosclerotic Plaques, Does Not Associate With Cardiovascular Events in Humans

Inhibition of sclerostin increases bone formation and decreases bone resorption, leading to increased bone mass, bone mineral density, and bone strength and reduced fracture risk. In a clinical study of the sclerostin antibody romosozumab versus alendronate in postmenopausal women (ARCH), an imbalance in adjudicated serious cardiovascular (CV) adverse events driven by an increase in myocardial infarction (MI) and stroke was observed. To explore whether there was a potential mechanistic plausibility that sclerostin expression, or its inhibition, in atherosclerotic (AS) plaques may have contributed to this imbalance, sclerostin was immunostained in human plaques to determine whether it was detected in regions relevant to plaque stability in 94 carotid and 50 femoral AS plaques surgically collected from older female patients (mean age 69.6 ± 10.4 years). Sclerostin staining was absent in most plaques (67%), and when detected, it was of reduced intensity compared with normal aorta and was located in deeper regions of the plaque/wall but was not observed in areas considered relevant to plaque stability (fibrous cap and endothelium). Additionally, genetic variants associated with lifelong reduced sclerostin expression were explored for associations with phenotypes including those related to bone physiology and CV risk factors/events in a population-based phenomewide association study (PheWAS). Natural genetic modulation of sclerostin by variants with a significant positive effect on bone physiology showed no association with lifetime risk of MI or stroke. These data do not support a causal association between the presence of sclerostin, or its inhibition, in the vasculature and increased risk of serious cardiovascular events. © 2021 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

Repurposing antihypertensive drugs for the prevention of Alzheimer's disease: a Mendelian randomization study

Background

Evidence concerning the potential repurposing of antihypertensives for Alzheimer’s disease prevention is inconclusive. We used Mendelian randomization, which can be more robust to confounding by indication and patient characteristics, to investigate the effects of lowering systolic blood pressure, via the protein targets of different antihypertensive drug classes, on Alzheimer’s disease.

Methods

We used summary statistics from genome-wide association studies of systolic blood pressure and Alzheimer’s disease in a two-sample Mendelian randomization analysis. We identified single-nucleotide polymorphisms (SNPs) that mimic the action of antihypertensive protein targets and estimated the effect of lowering systolic blood pressure on Alzheimer’s disease in three ways: (i) combining the protein targets of antihypertensive drug classes, (ii) combining all protein targets and (iii) without consideration of the protein targets.

Results

There was limited evidence that lowering systolic blood pressure, via the protein targets of antihypertensive drug classes, affected Alzheimer’s disease risk. For example, the protein targets of calcium channel blockers had an odds ratio (OR) per 10 mmHg lower systolic blood pressure of 1.53 [95% confidence interval (CI): 0.94 to 2.49; p = 0.09; SNPs = 17]. We also found limited evidence for an effect when combining all protein targets (OR per 10 mmHg lower systolic blood pressure: 1.14; 95% CI: 0.83 to 1.56; p = 0.41; SNPs = 59) and without consideration of the protein targets (OR per 10 mmHg lower systolic blood pressure: 1.04; 95% CI: 0.95 to 1.13; p = 0.45; SNPs = 153).

Conclusions

Mendelian randomization suggests that lowering systolic blood pressure via the protein targets of antihypertensive drugs is unlikely to affect the risk of developing Alzheimer’s disease. Consequently, if specific antihypertensive drug classes do affect the risk of Alzheimer’s disease, they may not do so via systolic blood pressure.

Using genetic variants to evaluate the causal effect of cholesterol lowering on head and neck cancer risk: A Mendelian randomization study

Head and neck squamous cell carcinoma (HNSCC), which includes cancers of the oral cavity and oropharynx, is a cause of substantial global morbidity and mortality. Strategies to reduce disease burden include discovery of novel therapies and repurposing of existing drugs. Statins are commonly prescribed for lowering circulating cholesterol by inhibiting HMG-CoA reductase (HMGCR). Results from some observational studies suggest that statin use may reduce HNSCC risk. We appraised the relationship of genetically-proxied cholesterol-lowering drug targets and other circulating lipid traits with oral (OC) and oropharyngeal (OPC) cancer risk using two-sample Mendelian randomization (MR). For the primary analysis, germline genetic variants in HMGCR, NPC1L1, CETP, PCSK9 and LDLR were used to proxy the effect of low-density lipoprotein cholesterol (LDL-C) lowering therapies. In secondary analyses, variants were used to proxy circulating levels of other lipid traits in a genome-wide association study (GWAS) meta-analysis of 188,578 individuals. Both primary and secondary analyses aimed to estimate the downstream causal effect of cholesterol lowering therapies on OC and OPC risk. The second sample for MR was taken from a GWAS of 6,034 OC and OPC cases and 6,585 controls (GAME-ON). Analyses were replicated in UK Biobank, using 839 OC and OPC cases and 372,016 controls and the results of the GAME-ON and UK Biobank analyses combined in a fixed-effects meta-analysis. We found limited evidence of a causal effect of genetically-proxied LDL-C lowering using HMGCR, NPC1L1, CETP or other circulating lipid traits on either OC or OPC risk. Genetically-proxied PCSK9 inhibition equivalent to a 1 mmol/L (38.7 mg/dL) reduction in LDL-C was associated with an increased risk of OC and OPC combined (OR 1.8 95%CI 1.2, 2.8, p = 9.31 x10-05), with good concordance between GAME-ON and UK Biobank (I2 = 22%). Effects for PCSK9 appeared stronger in relation to OPC (OR 2.6 95%CI 1.4, 4.9) than OC (OR 1.4 95%CI 0.8, 2.4). LDLR variants, resulting in genetically-proxied reduction in LDL-C equivalent to a 1 mmol/L (38.7 mg/dL), reduced the risk of OC and OPC combined (OR 0.7, 95%CI 0.5, 1.0, p = 0.006). A series of pleiotropy-robust and outlier detection methods showed that pleiotropy did not bias our findings. We found limited evidence for a role of cholesterol-lowering in OC and OPC risk, suggesting previous observational results may have been confounded. There was some evidence that genetically-proxied inhibition of PCSK9 increased risk, while lipid-lowering variants in LDLR, reduced risk of combined OC and OPC. This result suggests that the mechanisms of action of PCSK9 on OC and OPC risk may be independent of its cholesterol lowering effects; however, this was not supported uniformly across all sensitivity analyses and further replication of this finding is required.

Systematic identification of risk factors and drug repurposing options for Alzheimer's disease

INTRODUCTION

Several Mendelian randomization studies have been conducted that identified multiple risk factors for Alzheimer's disease (AD). However, they typically focus on a few pre-selected risk factors.

METHODS

A two-sample Mendelian randomization (MR) study was used to systematically examine the potential causal associations of 1037 risk factors/medical conditions and 31 drugs with the risk of late-onset AD. To correct for multiple comparisons, the false discovery rate was set at < 0.05.

RESULTS

There was strong evidence of a causal association between glioma risk, reduced trunk fat-free mass, lower education levels, lower intelligence and a higher risk of AD. For 31 investigated treatments (such as antihypertensive drugs), we found limited evidence for their associations.

DISCUSSION

MR found robust evidence of causal associations between glioma, trunk fat-free, and AD. Our study also confirms that higher educational attainment and higher intelligence are associated with a reduced risk of AD.

Tumor Necrosis Factor Inhibition and Parkinson Disease: A Mendelian Randomization Study

Objective

To evaluate the effects of long-term tumor necrosis factor (TNF) inhibition on the risk and age at onset of Parkinson disease (PD), we performed a 2-sample Mendelian randomization study using genome-wide association studies (GWAS) summary statistics.

Methods

Genetic variants in the vicinity of TNFRSF1A, the gene encoding TNF receptor 1 (TNFR1), were identified as predictive of pharmacologic blockade of TNFR1 signaling by anti-TNF therapy, based on genetic associations with lower circulating C-reactive protein (CRP; GWAS n = 204,402). The effects of TNF-TNFR1 inhibition were estimated for PD risk (n_cases/_controls = 37,688/981,372) and age at PD onset (n = 28,568) using GWAS data from the International Parkinson's Disease Genomics Consortium and 23andMe, Inc. To validate variants as proxies of long-term anti-TNF treatment, we also assessed whether variant associations reflected anticipated effects of TNFR1 inhibition on Crohn disease, ulcerative colitis, and multiple sclerosis risk (n = 38,589-45,975).

Results

TNF-TNFR1 signaling inhibition was not estimated to affect PD risk (odds ratio [OR] per 10% lower circulating CRP = 0.99; 95% confidence interval [CI] 0.91–1.08) or age at onset (0.13 years later onset; 95% CI −0.66 to 0.92). In contrast, genetically indexed TNF-TNFR1 signaling blockade predicted reduced risk of Crohn disease (OR 0.75; 95% CI 0.65–0.86) and ulcerative colitis (OR 0.84; 95% CI 0.74–0.97) and increased multiple sclerosis risk (OR 1.57; 95% CI 1.36–1.81). Findings were consistent across models using different genetic instruments and Mendelian randomization estimators.

Conclusions

Our findings do not imply that TNF-TNFR1 signaling inhibition will prevent or delay PD onset.

Classification of Evidence

This study provides Class II evidence that TNF-TNFR1 signaling inhibition is not associated with the risk or age at onset of PD.

Mendelian randomization for studying the effects of perturbing drug targets

Drugs whose targets have genetic evidence to support efficacy and safety are more likely to be approved after clinical development. In this paper, we provide an overview of how natural sequence variation in the genes that encode drug targets can be used in Mendelian randomization analyses to offer insight into mechanism-based efficacy and adverse effects. Large databases of summary level genetic association data are increasingly available and can be leveraged to identify and validate variants that serve as proxies for drug target perturbation. As with all empirical research, Mendelian randomization has limitations including genetic confounding, its consideration of lifelong effects, and issues related to heterogeneity across different tissues and populations. When appropriately applied, Mendelian randomization provides a useful empirical framework for using population level data to improve the success rates of the drug development pipeline.

Genetically determined blood pressure, antihypertensive medications, and risk of Alzheimer's disease: a Mendelian randomization study

BACKGROUND

Observational studies suggest that the use of antihypertensive medications (AHMs) is associated with a reduced risk of Alzheimer's disease (AD); however, these findings may be biased by confounding and reverse causality. We aimed to explore the effects of blood pressure (BP) and lowering systolic BP (SBP) via the protein targets of different AHMs on AD through a two-sample Mendelian randomization (MR) approach.

METHODS

Genetic proxies from genome-wide association studies of BP traits and BP-lowering variants in genes encoding AHM targets were extracted. Estimates were calculated by inverse-variance weighted method as the main model. MR Egger regression and leave-one-out analysis were performed to identify potential violations.

RESULTS

There was limited evidence that genetically predicted SBP/diastolic BP level affected AD risk based on 400/398 single nucleotide polymorphisms (SNPs), respectively (all P > 0.05). Suitable genetic variants for β-blockers (1 SNP), angiotensin receptor blockers (1 SNP), calcium channel blockers (CCBs, 45 SNPs), and thiazide diuretics (5 SNPs) were identified. Genetic proxies for CCB [odds ratio (OR) = 0.959, 95% confidence interval (CI) = 0.941-0.977, P = 3.92 × 10-6] and overall use of AHMs (OR = 0.961, 95% CI = 0.944-0.978, P = 5.74 × 10-6, SNPs = 52) were associated with a lower risk of AD. No notable heterogeneity and directional pleiotropy were identified (all P > 0.05). Additional analyses partly support these results. No single SNP was driving the observed effects.

CONCLUSIONS

This MR analysis found evidence that genetically determined lowering BP was associated with a lower risk of AD and CCB was identified as a promising strategy for AD prevention.

Mendelian randomization for studying the effects of perturbing drug targets

Drugs whose targets have genetic evidence to support efficacy and safety are more likely to be approved after clinical development. In this paper, we provide an overview of how natural sequence variation in the genes that encode drug targets can be used in Mendelian randomization analyses to offer insight into mechanism-based efficacy and adverse effects. Large databases of summary level genetic association data are increasingly available and can be leveraged to identify and validate variants that serve as proxies for drug target perturbation. As with all empirical research, Mendelian randomization has limitations including genetic confounding, its consideration of lifelong effects, and issues related to heterogeneity across different tissues and populations. When appropriately applied, Mendelian randomization provides a useful empirical framework for using population level data to improve the success rates of the drug development pipeline.

PCSK9 genetic variants and cognitive abilities: a large-scale Mendelian randomization study

INTRODUCTION

PCSK9 inhibitors lower low-density lipoprotein (LDL) cholesterol and are efficacious at reducing vascular disease, however questions remain about potential effects on cognitive function.

METHODS

We examined the association of genetic variants in PCSK9 with continuous measures of cognitive ability in UK Biobank. Six independent polymorphisms in PCSK9 were used in up to 337,348 individuals.

RESULTS

The PCSK9 allele score was associated with a lower risk of CHD, and weakly with worse log reaction time.

CONCLUSIONS

We are unable to rule out meaningful associations of PCSK9 genetic variants with cognition, emphasising the potential need for continued pharmacovigilance for patients currently treated with PCSK9 inhibitors.

The Interface of Therapeutics and Genomics in Cardiovascular Medicine

Pharmacogenomics has a burgeoning role in cardiovascular medicine, from warfarin dosing to antiplatelet choice, with recent developments in sequencing bringing the promise of personalised medicine ever closer to the bedside. Further scientific evidence, real-world clinical trials, and economic modelling are needed to fully realise this potential. Additionally, tools such as polygenic risk scores, and results from Mendelian randomisation analyses, are only in the early stages of clinical translation and merit further investigation. Genetically targeted rational drug design has a strong evidence base and, due to the nature of genetic data, academia, direct-to-consumer companies, healthcare systems, and industry may meet in an unprecedented manner. Data sharing navigation may prove problematic. The present manuscript addresses these issues and concludes a need for further guidance to be provided to prescribers by professional bodies to aid in the consideration of such complexities and guide translation of scientific knowledge to personalised clinical action, thereby striving to improve patient care. Additionally, technologic infrastructure equipped to handle such large complex data must be adapted to pharmacogenomics and made user friendly for prescribers and patients alike.

Genomic Medicine: Lessons Learned From Monogenic and Complex Bone Disorders

Current genetic studies of monogenic and complex bone diseases have broadened our understanding of disease pathophysiology, highlighting the need for medical interventions and treatments tailored to the characteristics of patients. As genomic research progresses, novel insights into the molecular mechanisms are starting to provide support to clinical decision-making; now offering ample opportunities for disease screening, diagnosis, prognosis and treatment. Drug targets holding mechanisms with genetic support are more likely to be successful. Therefore, implementing genetic information to the drug development process and a molecular redefinition of skeletal disease can help overcoming current shortcomings in pharmaceutical research, including failed attempts and appalling costs. This review summarizes the achievements of genetic studies in the bone field and their application to clinical care, illustrating the imminent advent of the genomic medicine era.

Identifying Drug Repurposing Opportunities in Oncology

The never-ending explosion in the cost of new oncology drugs is reducing in many countries the access to the most recent, effective anticancer therapies and represents a significant obstacle to the design and realization of combinatorial trials. Already approved, anticancer and nonanticancer drugs can be considered for in silico, preclinical, and clinical repurposing approaches and offer the significant advantages of a potentially cheaper, faster, and safer validation. This review discusses recent advances and challenges in the field.

Within family Mendelian randomization studies

Mendelian randomization (MR) is increasingly used to make causal inferences in a wide range of fields, from drug development to etiologic studies. Causal inference in MR is possible because of the process of genetic inheritance from parents to offspring. Specifically, at gamete formation and conception, meiosis ensures random allocation to the offspring of one allele from each parent at each locus, and these are unrelated to most of the other inherited genetic variants. To date, most MR studies have used data from unrelated individuals. These studies assume that genotypes are independent of the environment across a sample of unrelated individuals, conditional on covariates. Here we describe potential sources of bias, such as transmission ratio distortion, selection bias, population stratification, dynastic effects and assortative mating that can induce spurious or biased SNP-phenotype associations. We explain how studies of related individuals such as sibling pairs or parent-offspring trios can be used to overcome some of these sources of bias, to provide potentially more reliable evidence regarding causal processes. The increasing availability of data from related individuals in large cohort studies presents an opportunity to both overcome some of these biases and also to evaluate familial environmental effects.

Evaluating the cardiovascular safety of sclerostin inhibition using evidence from meta-analysis of clinical trials and human genetics

Inhibition of sclerostin is a therapeutic approach to lowering fracture risk in patients with osteoporosis. However, data from phase 3 randomized controlled trials (RCTs) of romosozumab, a first-in-class monoclonal antibody that inhibits sclerostin, suggest an imbalance of serious cardiovascular events, and regulatory agencies have issued marketing authorizations with warnings of cardiovascular disease. Here, we meta-analyze published and unpublished cardiovascular outcome trial data of romosozumab and investigate whether genetic variants that mimic therapeutic inhibition of sclerostin are associated with higher risk of cardiovascular disease. Meta-analysis of up to three RCTs indicated a probable higher risk of cardiovascular events with romosozumab. Scaled to the equivalent dose of romosozumab (210 milligrams per month; 0.09 grams per square centimeter of higher bone mineral density), the SOST genetic variants were associated with lower risk of fracture and osteoporosis (commensurate with the therapeutic effect of romosozumab) and with a higher risk of myocardial infarction and/or coronary revascularization and major adverse cardiovascular events. The same variants were also associated with increased risk of type 2 diabetes mellitus and higher systolic blood pressure and central adiposity. Together, our findings indicate that inhibition of sclerostin may elevate cardiovascular risk, warranting a rigorous evaluation of the cardiovascular safety of romosozumab and other sclerostin inhibitors.

Turning genome-wide association study findings into opportunities for drug repositioning

Drug development is a very costly and lengthy process, while repositioned or repurposed drugs could be brought into clinical practice within a shorter time-frame and at a much reduced cost. Numerous computational approaches to drug repositioning have been developed, but methods utilizing genome-wide association studies (GWASs) data are less explored.

The past decade has observed a massive growth in the amount of data from GWAS; the rich information contained in GWAS has great potential to guide drug repositioning or discovery. While multiple tools are available for finding the most relevant genes from GWAS hits, searching for top susceptibility genes is only one way to guide repositioning, which has its own limitations.

Here we provide a comprehensive review of different computational approaches that employ GWAS data to guide drug repositioning. These methods include selecting top candidate genes from GWAS as drug targets, deducing drug candidates based on drug-drug and disease-disease similarities, searching for reversed expression profiles between drugs and diseases, pathway-based methods as well as approaches based on analysis of biological networks. Each method is illustrated with examples, and their respective strengths and limitations are discussed. We also discussed several areas for future research.

Leveraging Human Genetics to Identify Safety Signals Prior to Drug Marketing Approval and Clinical Use

INTRODUCTION

When a new drug or biologic product enters the market, its full spectrum of side effects is not yet fully understood, as use in the real world often uncovers nuances not suggested within the relatively narrow confines of preapproval preclinical and trial work.

OBJECTIVE

We describe a new, phenome-wide association study (PheWAS)- and evidence-based approach for detection of potential adverse drug effects.

METHODS

We leveraged our established platform, which integrates human genetic data with associated phenotypes in electronic health records from 29,722 patients of European ancestry, to identify gene-phenotype associations that may represent known safety issues. We examined PheWAS data and the published literature for 16 genes, each of which encodes a protein targeted by at least one drug or biologic product.

RESULTS

Initial data demonstrated that our novel approach (safety ascertainment using PheWAS [SA-PheWAS]) can replicate published safety information across multiple drug classes, with validated findings for 13 of 16 gene-drug class pairs.

CONCLUSIONS

By connecting and integrating in vivo and in silico data, SA-PheWAS offers an opportunity to supplement current methods for predicting or confirming safety signals associated with therapeutic agents.

ACE and Type 2 Diabetes Risk: A Mendelian Randomization Study

OBJECTIVE

To determine whether ACE inhibitors reduce the risk of type 2 diabetes using a Mendelian randomization (MR) approach.

RESEARCH DESIGN AND METHODS

A two-sample MR analysis included 17 independent genetic variants associated with ACE serum concentration in 4,147 participants from the Outcome Reduction with Initial Glargine INtervention (ORIGIN) (clinical trial reg. no. NCT00069784) trial, and their effects on type 2 diabetes risk were estimated from 18 studies of the DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) consortium. A genetic risk score (GRS) underpinning lower ACE concentration was then tested for association with type 2 diabetes prevalence in 341,872 participants, including 16,320 with type 2 diabetes, from the UK Biobank. MR estimates were compared after standardization for blood pressure change, with the estimate obtained from a randomized controlled trial (RCT) meta-analysis of ACE inhibitors versus placebo (n = 31,200).

RESULTS

Genetically lower ACE concentrations were associated with a lower risk of type 2 diabetes (odds ratio [OR] per SD 0.92 [95% CI 0.89-0.95]; P = 1.79 × 10^-7). This result was replicated in the UK Biobank (OR per SD 0.97 [0.96-0.99]; P = 8.73 × 10^-4). After standardization, the ACE GRS was associated with a larger decrease in type 2 diabetes risk per 2.4-mmHg lower mean arterial pressure (MAP) compared with that obtained from an RCT meta-analysis (OR per 2.4-mmHg lower MAP 0.19 [0.07-0.51] vs. 0.76 [0.60-0.97], respectively; P = 0.007 for difference).

CONCLUSIONS

These results support the causal protective effect of ACE inhibitors on type 2 diabetes risk and may guide therapeutic decision making in clinical practice.

Using Mendelian randomization to understand and develop treatments for neurodegenerative disease

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.

The promise and reality of therapeutic discovery from large cohorts

Technological advances in rapid data acquisition have transformed medical biology into a data mining field, where new data sets are routinely dissected and analyzed by statistical models of ever-increasing complexity. Many hypotheses can be generated and tested within a single large data set, and even small effects can be statistically discriminated from a sea of noise. On the other hand, the development of therapeutic interventions moves at a much slower pace. They are determined from carefully randomized and well-controlled experiments with explicitly stated outcomes as the principal mechanism by which a single hypothesis is tested. In this paradigm, only a small fraction of interventions can be tested, and an even smaller fraction are ultimately deemed therapeutically successful. In this Review, we propose strategies to leverage large-cohort data to inform the selection of targets and the design of randomized trials of novel therapeutics. Ultimately, the incorporation of big data and experimental medicine approaches should aim to reduce the failure rate of clinical trials as well as expedite and lower the cost of drug development.

Can genetics reveal the causes and consequences of educational attainment?

There is an extensive literature on the causes of educational inequalities, and the life course consequences of educational attainment. Mendelian randomization, where genetic variants associated with exposures of interest are used as proxies for those exposures, often within an instrumental variables framework, has proven highly effective at elucidating the causal effects of several risk factors in the biomedical sciences. We discuss the potential for this approach to be used in the context of social and socio-economic exposures and outcomes, such as educational attainment.

A transcriptome-wide Mendelian randomization study to uncover tissue-dependent regulatory mechanisms across the human phenome

Developing insight into tissue-specific transcriptional mechanisms can help improve our understanding of how genetic variants exert their effects on complex traits and disease. In this study, we apply the principles of Mendelian randomization to systematically evaluate transcriptome-wide associations between gene expression (across 48 different tissue types) and 395 complex traits. Our findings indicate that variants which influence gene expression levels in multiple tissues are more likely to influence multiple complex traits. Moreover, detailed investigations of our results highlight tissue-specific associations, drug validation opportunities, insight into the likely causal pathways for trait-associated variants and also implicate putative associations at loci yet to be implicated in disease susceptibility. Similar evaluations can be conducted at http://mrcieu.mrsoftware.org/Tissue_MR_atlas/.

Mendelian randomization indicates that TNF is not causally associated with Alzheimer's disease

Epidemiological research has suggested that inhibition of tumor necrosis factor (TNF)-α in patients with rheumatoid arthritis (RA) reduces the overall risk of Alzheimer's disease (AD). TNF-α antagonists have been suggested as a potential treatment for AD. We used a two-sample Mendelian randomization design to examine the causal relationship between blood TNF expression, serum TNF-α levels, and RA on AD risk. Our results do not support a causal relationship between TNF expression, serum TNF-α levels, and RA on AD risk.

Use of Genetic Variants Related to Antihypertensive Drugs to Inform on Efficacy and Side Effects

BACKGROUND

Drug effects can be investigated through natural variation in the genes for their protein targets. The present study aimed to use this approach to explore the potential side effects and repurposing potential of antihypertensive drugs, which are among the most commonly used medications worldwide.

METHODS

Genetic proxies for the effect of antihypertensive drug classes were identified as variants in the genes for the corresponding targets that associated with systolic blood pressure at genome-wide significance. Mendelian randomization estimates for drug effects on coronary heart disease and stroke risk were compared with randomized, controlled trial results. A phenome-wide association study in the UK Biobank was performed to identify potential side effects and repurposing opportunities, with findings investigated in the Vanderbilt University biobank (BioVU) and in observational analysis of the UK Biobank.

RESULTS

Suitable genetic proxies for angiotensin-converting enzyme inhibitors, β-blockers, and calcium channel blockers (CCBs) were identified. Mendelian randomization estimates for their effect on coronary heart disease and stroke risk, respectively, were comparable to results from randomized, controlled trials against placebo. A phenome-wide association study in the UK Biobank identified an association of the CCB standardized genetic risk score with increased risk of diverticulosis (odds ratio, 1.02 per standard deviation increase; 95% CI, 1.01-1.04), with a consistent estimate found in BioVU (odds ratio, 1.01; 95% CI, 1.00-1.02). Cox regression analysis of drug use in the UK Biobank suggested that this association was specific to nondihydropyridine CCBs (hazard ratio 1.49 considering thiazide diuretic agents as a comparator; 95% CI, 1.04-2.14) but not dihydropyridine CCBs (hazard ratio, 1.04; 95% CI, 0.83-1.32).

CONCLUSIONS

Genetic variants can be used to explore the efficacy and side effects of antihypertensive medications. The identified potential effect of nondihydropyridine CCBs on diverticulosis risk could have clinical implications and warrants further investigation.

Using genetic data to strengthen causal inference in observational research

Causal inference is essential across the biomedical, behavioural and social sciences.By progressing from confounded statistical associations to evidence of causal relationships, causal inference can reveal complex pathways underlying traits and diseases and help to prioritize targets for intervention. Recent progress in genetic epidemiology - including statistical innovation, massive genotyped data sets and novel computational tools for deep data mining - has fostered the intense development of methods exploiting genetic data and relatedness to strengthen causal inference in observational research. In this Review, we describe how such genetically informed methods differ in their rationale, applicability and inherent limitations and outline how they should be integrated in the future to offer a rich causal inference toolbox.

Genetic Insights for Drug Development in NAFLD

Drug development is a costly, time-consuming, and challenging endeavour, with only a few agents reaching the threshold of approval for clinical use. Therefore, approaches to more efficiently identify targets that are likely to translate to clinical benefit are required. Interrogation of the human genome in large patient cohorts has rapidly advanced our knowledge of the genetic architecture and underlying mechanisms of many diseases, including nonalcoholic fatty liver disease (NAFLD). There are no approved pharmacotherapies for NAFLD currently. Genetic insights provide a powerful and new approach to infer and prioritise candidate drugs, with such selection avoiding myriad pitfalls, while defining likely benefits. In this review, we discuss the prospects and challenges for the optimal utilisation of genetic findings for improving and accelerating the NAFLD drug discovery pipeline.

GWAS Identifies Risk Locus for Erectile Dysfunction and Implicates Hypothalamic Neurobiology and Diabetes in Etiology

Erectile dysfunction (ED) is a common condition affecting more than 20% of men over 60 years, yet little is known about its genetic architecture. We performed a genome-wide association study of ED in 6,175 case subjects among 223,805 European men and identified one locus at 6q16.3 (lead variant rs57989773, OR 1.20 per C-allele; p = 5.71 × 10⁻¹⁴), located between MCHR2 and SIM1. In silico analysis suggests SIM1 to confer ED risk through hypothalamic dysregulation. Mendelian randomization provides evidence that genetic risk of type 2 diabetes mellitus is a cause of ED (OR 1.11 per 1-log unit higher risk of type 2 diabetes). These findings provide insights into the biological underpinnings and the causes of ED and may help prioritize the development of future therapies for this common disorder.

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

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.

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

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.