scholarly journals Genetic drug target validation using Mendelian randomization

2019 ◽  
Author(s):  
A F Schmidt ◽  
C Finan ◽  
M Gordillo-Marañón ◽  
F W Asselbergs ◽  
D F Freitag ◽  
...  
Keyword(s):  
Risk Factors ◽  
Drug Target ◽  
Drug Targets ◽  
Mendelian Randomization ◽  
Analytical Framework ◽  
Causal Chain ◽  
Mendelian Randomisation ◽  
Mathematical Framework ◽  
Alternative Pathways ◽  
Drug Target Validation

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

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

2019 ◽  
Vol 4 ◽  
pp. 113 ◽  
Author(s):  
Venexia M Walker ◽  
Neil M Davies ◽  
Gibran Hemani ◽  
Jie Zheng ◽  
Philip C Haycock ◽  
...  
Keyword(s):  
Risk Factors ◽  
Web Application ◽  
Drug Targets ◽  
Genome Wide Association Study ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
R Package ◽  
Sensitivity Analyses ◽  
Link Type ◽  
Study Results

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

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

2021 ◽  
Author(s):  
Yitang Sun ◽  
Jingqi Zhou ◽  
Kaixiong Ye
Keyword(s):  
Risk Factors ◽  
Fat Mass ◽  
Blood Cells ◽  
Drug Targets ◽  
Large Scale ◽  
Mendelian Randomization ◽  
White Blood Cells ◽  
Fat Free Mass ◽  
Cell Counts ◽  
Increased Risk

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

scholarly journals Genetic drug target validation using Mendelian randomisation

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Amand F. Schmidt ◽  
Chris Finan ◽  
Maria Gordillo-Marañón ◽  
Folkert W. Asselbergs ◽  
Daniel F. Freitag ◽  
...  
Keyword(s):  
Drug Target ◽  
Mendelian Randomisation ◽  
Target Validation ◽  
Drug Target Validation

scholarly journals A Systems-Level Framework for Drug Discovery Identifies Csf1R As A Novel Anti-Epileptic Drug Target

2017 ◽  
Author(s):  
Prashant K Srivastava ◽  
Jonathan van Eyll ◽  
Patrice Godard ◽  
Manuela Mazzuferi ◽  
Benedicte Danis ◽  
...  
Keyword(s):  
Causal Reasoning ◽  
Drug Target ◽  
Drug Targets ◽  
Target Identification ◽  
Analytical Framework ◽  
Membrane Receptors ◽  
New Drugs ◽  
Tyrosine Kinase Receptor ◽  
Drug Target Identification ◽  
Novel Therapeutic

ABSTRACTThe identification of mechanistically novel drug targets is highly challenging, particularly for diseases of the central nervous system. To address this problem we developed and experimentally validated a new computational approach to drug target identification that combines gene-regulatory information with a causal reasoning framework (“causal reasoning analytical framework for target discovery” – CRAFT). Starting from gene expression data, CRAFT provides a predictive functional genomics framework for identifying membrane receptors with a direction-specified influence over network expression. As proof-of-concept we applied CRAFT to epilepsy, and predicted the tyrosine kinase receptor Csf1R as a novel therapeutic target for epilepsy. The predicted therapeutic effect of Csf1R blockade was validated in two pre-clinical models of epilepsy using a small molecule inhibitor of Csf1R. These results suggest Csf1R blockade as a novel therapeutic strategy in epilepsy, and highlight CRAFT as a systems-level framework for predicting mechanistically new drugs and targets. CRAFT is applicable to disease settings other than epilepsy.

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

2021 ◽  
Author(s):  
Michael G. Levin ◽  
Verena Zuber ◽  
Venexia M. Walker ◽  
Derek Klarin ◽  
Julie Lynch ◽  
...  
Keyword(s):  
Risk Factors ◽  
Drug Targets ◽  
Particle Concentration ◽  
Mendelian Randomization ◽  
Inclusion Probability ◽  
Lipoprotein Subfractions ◽  
Artery Disease ◽  
Lowering Drug ◽  
Cad Risk

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

NADPH Producing Enzymes as Promising Drug Targets for Chagas Disease

2019 ◽  
Vol 26 (36) ◽  
pp. 6564-6571
Author(s):  
Artur T. Cordeiro
Keyword(s):  
Drug Target ◽  
Drug Targets ◽  
Nicotinamide Adenine Dinucleotide Phosphate ◽  
Acid Synthesis ◽  
Pentose Phosphate ◽  
New Drugs ◽  
Trypanosomatid Parasites ◽  
Drug Target Validation ◽  
Reduced Nicotinamide Adenine Dinucleotide ◽  
Glucose 6 Phosphate Dehydrogenase

Reduced Nicotinamide Adenine Dinucleotide Phosphate (NADPH) is a cofactor used in different anabolic reactions, such as lipid and nucleic acid synthesis, and for oxidative stress defense. NADPH is essential for parasite growth and viability. In trypanosomatid parasites, NADPH is supplied by the oxidative branch of the pentose phosphate pathway and by enzymes associated with the citric acid cycle. The present article will review recent achievements that suggest glucose-6-phosphate dehydrogenase and the cytosolic isoform of the malic enzyme as promising drug targets for the discovery of new drugs against Trypanosoma cruzi and T. brucei. Topics involving an alternative strategy in accelerating T. cruzi drug-target validation and the concept of drug-target classification will also be revisited.

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

Stroke ◽  
2021 ◽  
Author(s):  
Marios K. Georgakis ◽  
Dipender Gill
Keyword(s):  
Risk Factors ◽  
Genetic Variants ◽  
Drug Targets ◽  
Mendelian Randomization ◽  
Genetic Data ◽  
Lipid Lowering ◽  
Coagulation Cascade ◽  
Random Allocation ◽  
Large Artery ◽  
Stroke Subtypes

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.

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

2019 ◽  
Vol 4 ◽  
pp. 113 ◽  
Author(s):  
Venexia M Walker ◽  
Neil M Davies ◽  
Gibran Hemani ◽  
Jie Zheng ◽  
Philip C Haycock ◽  
...  
Keyword(s):  
Risk Factors ◽  
Web Application ◽  
Drug Targets ◽  
Genome Wide Association Study ◽  
Mendelian Randomization ◽  
R Package ◽  
Sensitivity Analyses ◽  
Link Type ◽  
Genome Wide ◽  
Study Results

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

Sign in / Sign up

Export Citation Format

Share Document