Using the MR-Base platform to investigate risk factors and drug targets for thousands of phenotypes.
Ontology highlight
ABSTRACT:
SUBMITTER: Walker VM
PROVIDER: S-EPMC6694718 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Results from genome-wide association studies (GWAS) can be used to infer causal relationships between phenotypes, using a strategy known as 2-sample Mendelian randomization (2SMR) and bypassing the need for individual-level data. However, 2SMR methods are evolving rapidly and GWAS results are often insufficiently curated, undermining efficient implementation of the approach. We therefore developed MR-Base (<ext-link ext-link-type="uri" xlink:href="http://www.mrbase.org">http://www.mrbase.org</ext-link>): a platform that integrates a curated database of complete GWAS results (no restrictions according to statistical significance) with an application programming interface, web app and R packages that automate 2SMR. The software includes several sensitivity analyses for assessing the impact of horizontal pleiotropy and other violations of assumptions. The database currently comprises 11 billion single nucleotide polymorphism-trait associations from 1673 GWAS and is updated on a regular basis. Integrating data with software ensures more rigorous application of hypothesis-driven analyses and allows millions of potential causal relationships to be efficiently evaluated in phenome-wide association studies.
| S-EPMC5976434 | biostudies-literature
Project description:Summary:Results from hundreds of genome-wide association studies (GWAS) are now freely available and offer a catalogue of the association between phenotypes across medicine with variants in the genome. With the aim of using this data to better understand therapeutic mechanisms, we have developed Drug Targetor, a web interface that allows the generation and exploration of drug-target networks of hundreds of phenotypes using GWAS data. Drug Targetor networks consist of drug and target nodes ordered by genetic association and connected by drug-target or drug-gene relationship. We show that Drug Targetor can help prioritize drugs, targets and drug-target interactions for a specific phenotype based on genetic evidence. Availability and implementation:Drug Targetor v1.21 is a web application freely available online at drugtargetor.com and under MIT license. The source code can be found at https://github.com/hagax8/drugtargetor. Supplementary information:Supplementary data are available at Bioinformatics online.
| S-EPMC6612852 | biostudies-other
Project description:MotivationMethods for computational drug target identification use information from diverse information sources to predict or prioritize drug targets for known drugs. One set of resources that has been relatively neglected for drug repurposing is animal model phenotype.ResultsWe investigate the use of mouse model phenotypes for drug target identification. To achieve this goal, we first integrate mouse model phenotypes and drug effects, and then systematically compare the phenotypic similarity between mouse models and drug effect profiles. We find a high similarity between phenotypes resulting from loss-of-function mutations and drug effects resulting from the inhibition of a protein through a drug action, and demonstrate how this approach can be used to suggest candidate drug targets.Availability and implementationAnalysis code and supplementary data files are available on the project Web site at https://drugeffects.googlecode.com.
| S-EPMC3933875 | biostudies-literature
Project description:Schizophrenia is a severe psychiatric disorder characterized by emotional, behavioral and cognitive disturbances, and the treatment of schizophrenia is often complicated by noncompliance and pharmacoresistance. The search for the pathophysiological mechanisms underlying schizophrenia has resulted in the proposal of several hypotheses to explain the impacts of environmental, genetic, neurodevelopmental, immune and inflammatory factors on disease onset and progression. This review discusses the newest insights into the pathophysiology of and risk factors for schizophrenia and notes novel approaches in antipsychotic treatment and potential diagnostic and theranostic biomarkers. The current hypotheses focusing on neuromediators (dopamine, glutamate, and serotonin), neuroinflammation, the cannabinoid hypothesis, the gut-brain axis model, and oxidative stress are summarized. Key genetic features, including small nucleotide polymorphisms, copy number variations, microdeletions, mutations and epigenetic changes, are highlighted. Current pharmacotherapy of schizophrenia relies mostly on dopaminergic and serotonergic antagonists/partial agonists, but new findings in the pathophysiology of schizophrenia have allowed the expansion of novel approaches in pharmacotherapy and the establishment of more reliable biomarkers. Substances with promising results in preclinical and clinical studies include lumateperone, pimavanserin, xanomeline, roluperidone, agonists of trace amine-associated receptor 1, inhibitors of glycine transporters, AMPA allosteric modulators, mGLUR2-3 agonists, D-amino acid oxidase inhibitors and cannabidiol. The use of anti-inflammatory agents as an add-on therapy is mentioned.
| S-EPMC8311514 | biostudies-literature
Project description:The Open Targets Platform (https://www.targetvalidation.org/) provides users with a queryable knowledgebase and user interface to aid systematic target identification and prioritisation for drug discovery based upon underlying evidence. It is publicly available and the underlying code is open source. Since our last update two years ago, we have had 10 releases to maintain and continuously improve evidence for target-disease relationships from 20 different data sources. In addition, we have integrated new evidence from key datasets, including prioritised targets identified from genome-wide CRISPR knockout screens in 300 cancer models (Project Score), and GWAS/UK BioBank statistical genetic analysis evidence from the Open Targets Genetics Portal. We have evolved our evidence scoring framework to improve target identification. To aid the prioritisation of targets and inform on the potential impact of modulating a given target, we have added evaluation of post-marketing adverse drug reactions and new curated information on target tractability and safety. We have also developed the user interface and backend technologies to improve performance and usability. In this article, we describe the latest enhancements to the Platform, to address the fundamental challenge that developing effective and safe drugs is difficult and expensive.
| S-EPMC7779013 | biostudies-literature
Project description:BackgroundTwo-sample Mendelian randomization (2SMR) is an increasingly popular epidemiological method that uses genetic variants as instruments for making causal inferences. Clear reporting of methods employed in such studies is important for evaluating their underlying quality. However, the quality of methodological reporting of 2SMR studies is currently unclear. We aimed to assess the reporting quality of studies that used MR-Base, one of the most popular platforms for implementing 2SMR analysis.MethodsWe created a bespoke reporting checklist to evaluate reporting quality of 2SMR studies. We then searched Web of Science Core Collection, PsycInfo, MEDLINE, EMBASE and Google Scholar citations of the MR-Base descriptor paper to identify published MR studies that used MR-Base for any component of the MR analysis. Study screening and data extraction were performed by at least two independent reviewers.ResultsIn the primary analysis, 87 studies were included. Reporting quality was generally poor across studies, with a mean of 53% (SD = 14%) of items reported in each study. Many items required for evaluating the validity of key assumptions made in MR were poorly reported: only 44% of studies provided sufficient details for assessing if the genetic variant associates with the exposure ('relevance' assumption), 31% for assessing if there are any variant-outcome confounders ('independence' assumption), 89% for the assessing if the variant causes the outcome independently of the exposure ('exclusion restriction' assumption) and 32% for assumptions of falsification tests. We did not find evidence of a change in reporting quality over time or a difference in reporting quality between studies that used MR-Base and a random sample of MR studies that did not use this platform.ConclusionsThe quality of reporting of two-sample Mendelian randomization studies in our sample was generally poor. Journals and researchers should consider using the STROBE-MR guidelines to improve reporting quality.
| S-EPMC9749715 | biostudies-literature
Project description:MotivationIn order to discover quantitative trait loci, multi-dimensional genomic datasets combining DNA-seq and ChiP-/RNA-seq require methods that rapidly correlate tens of thousands of molecular phenotypes with millions of genetic variants while appropriately controlling for multiple testing.ResultsWe have developed FastQTL, a method that implements a popular cis-QTL mapping strategy in a user- and cluster-friendly tool. FastQTL also proposes an efficient permutation procedure to control for multiple testing. The outcome of permutations is modeled using beta distributions trained from a few permutations and from which adjusted P-values can be estimated at any level of significance with little computational cost. The Geuvadis & GTEx pilot datasets can be now easily analyzed an order of magnitude faster than previous approaches.Availability and implementationSource code, binaries and comprehensive documentation of FastQTL are freely available to download at http://fastqtl.sourceforge.net/Contactemmanouil.dermitzakis@unige.ch or olivier.delaneau@unige.chSupplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC4866519 | biostudies-literature
Project description:Brain-machine interfaces hold promise for the restoration of sensory and motor function and the treatment of neurological disorders, but clinical brain-machine interfaces have not yet been widely adopted, in part, because modest channel counts have limited their potential. In this white paper, we describe Neuralink's first steps toward a scalable high-bandwidth brain-machine interface system. We have built arrays of small and flexible electrode "threads," with as many as 3072 electrodes per array distributed across 96 threads. We have also built a neurosurgical robot capable of inserting six threads (192 electrodes) per minute. Each thread can be individually inserted into the brain with micron precision for avoidance of surface vasculature and targeting specific brain regions. The electrode array is packaged into a small implantable device that contains custom chips for low-power on-board amplification and digitization: The package for 3072 channels occupies less than 23×18.5×2 mm3. A single USB-C cable provides full-bandwidth data streaming from the device, recording from all channels simultaneously. This system has achieved a spiking yield of up to 70% in chronically implanted electrodes. Neuralink's approach to brain-machine interface has unprecedented packaging density and scalability in a clinically relevant package.
| S-EPMC6914248 | biostudies-literature
Project description:Efflux pumps are a significant challenge for the development of new antibacterial agents. Overcoming efflux requires an in-depth understanding of efflux pump functions, substrate specificities, and the development of inhibitors. However, the complexities of drug efflux networks have limited such studies. To address these challenges, we report the generation of Efflux KnockOut-35 (EKO-35), a highly susceptible Escherichia coli strain lacking 35 efflux pumps. We demonstrate the utility of this strain by constructing an efflux platform consisting of strains individually expressing genes encoding efflux pumps forming tripartite complexes with the outer membrane channel TolC. This platform was profiled against a curated diverse compound collection, which enabled us to define physicochemical properties that contribute to transport. We also show the E. coli drug efflux network is conditionally essential for growth, and that the platform can be used to investigate efflux pump inhibitor specificities and also efflux pump interplay. We believe EKO-35 and the efflux platform will have widespread application for the study of drug efflux.
2022-08-31 | PXD033975 | Pride
Project description:Pancreatic cancer (PC) is one of the most challenging tumor entities worldwide, characterized as a highly aggressive disease with dismal overall prognosis and an incidence rate equalling mortality rate. Over the last decade, substantial progress has been made to define the morphological changes and key genetic events in pancreatic carcinogenesis. And yet, it is still unclear what factors trigger PC. Some risk factors appear to be associated with sex, age, race/ethnicity, or other rare genetic conditions. Additionally, modifying factors such as smoking, obesity, diabetes, occupational risk factors, etc., increase the potential for acquiring genetic mutations that may result in PC. Another hallmark of PC is its poor response to radio- and chemo-therapy. Current chemotherapeutic regimens could not provide substantial survival benefit with a clear increase in overall survival. Recently, several new approaches to significantly improve the clinical outcome of PC have been described involving downstream signaling cascades desmoplasia and stromal response as well as tumor microenvironment, immune response, vasculature, and angiogenesis. This review summarizes major risk factors for PC and tries to illuminate relevant targets considerable for new therapeutic approaches.
| S-EPMC3831165 | biostudies-other