scholarly journals Drug Targetor: a web interface to investigate the human druggome for over 500 phenotypes

2018 ◽  
Vol 35 (14) ◽  
pp. 2515-2517 ◽  
Author(s):  
Héléna A Gaspar ◽  
Christopher Hübel ◽  
Gerome Breen
Keyword(s):  
Web Application ◽  
Drug Target ◽  
Association Studies ◽  
Source Code ◽  
Genome Wide Association ◽  
Supplementary Information ◽  
Genome Wide Association Studies ◽  
Web Interface ◽  
Genome Wide ◽  
Therapeutic Mechanisms

Abstract 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 licence. The source code can be found at https://github.com/hagax8/drugtargetor. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals GWASpro: a high-performance genome-wide association analysis server

2018 ◽  
Vol 35 (14) ◽  
pp. 2512-2514 ◽  
Author(s):  
Bongsong Kim ◽  
Xinbin Dai ◽  
Wenchao Zhang ◽  
Zhaohong Zhuang ◽  
Darlene L Sanchez ◽  
...  
Keyword(s):  
High Performance ◽  
Large Scale ◽  
Linear Mixed Model ◽  
Association Studies ◽  
Learning Curves ◽  
Experimental Designs ◽  
Genome Wide Association ◽  
Supplementary Information ◽  
Genome Wide Association Studies ◽  
Genome Wide

Abstract Summary We present GWASpro, a high-performance web server for the analyses of large-scale genome-wide association studies (GWAS). GWASpro was developed to provide data analyses for large-scale molecular genetic data, coupled with complex replicated experimental designs such as found in plant science investigations and to overcome the steep learning curves of existing GWAS software tools. GWASpro supports building complex design matrices, by which complex experimental designs that may include replications, treatments, locations and times, can be accounted for in the linear mixed model. GWASpro is optimized to handle GWAS data that may consist of up to 10 million markers and 10 000 samples from replicable lines or hybrids. GWASpro provides an interface that significantly reduces the learning curve for new GWAS investigators. Availability and implementation GWASpro is freely available at https://bioinfo.noble.org/GWASPRO. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals bGWAS: an R package to perform Bayesian genome wide association studies

2020 ◽  
Vol 36 (15) ◽  
pp. 4374-4376
Author(s):  
Ninon Mounier ◽  
Zoltán Kutalik
Keyword(s):  
Mendelian Randomization ◽  
Causal Effect ◽  
Association Studies ◽  
R Package ◽  
Genome Wide Association ◽  
Supplementary Information ◽  
Genome Wide Association Studies ◽  
Biological Mechanisms ◽  
Genome Wide ◽  
Related Risk

Abstract Summary Increasing sample size is not the only strategy to improve discovery in Genome Wide Association Studies (GWASs) and we propose here an approach that leverages published studies of related traits to improve inference. Our Bayesian GWAS method derives informative prior effects by leveraging GWASs of related risk factors and their causal effect estimates on the focal trait using multivariable Mendelian randomization. These prior effects are combined with the observed effects to yield Bayes Factors, posterior and direct effects. The approach not only increases power, but also has the potential to dissect direct and indirect biological mechanisms. Availability and implementation bGWAS package is freely available under a GPL-2 License, and can be accessed, alongside with user guides and tutorials, from https://github.com/n-mounier/bGWAS. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals pyseer: a comprehensive tool for microbial pangenome-wide association studies

2018 ◽  
Author(s):  
John A Lees ◽  
Marco Galardini ◽  
Stephen D Bentley ◽  
Jeffrey N Weiser ◽  
Jukka Corander
Keyword(s):  
Input Data ◽  
Association Studies ◽  
Genome Wide Association ◽  
Supplementary Information ◽  
Genome Wide Association Studies ◽  
Supplementary Data ◽  
New Methods ◽  
Link Type ◽  
Genome Wide

AbstractSummaryGenome-wide association studies (GWAS) in microbes face different challenges to eukaryotes and have been addressed by a number of different methods. pyseer brings these techniques together in one package tailored to microbial GWAS, allows greater flexibility of the input data used, and adds new methods to interpret the association results.Availability and Implementationpyseer is written in python and is freely available at https://github.com/mgalardini/pyseer, or can be installed through pip. Documentation and a tutorial are available at http://[email protected] and [email protected] informationSupplementary data are available online.

scholarly journals PopCluster: an algorithm to identify genetic variants with ethnicity-dependent effects

2019 ◽  
Vol 35 (17) ◽  
pp. 3046-3054 ◽  
Author(s):  
Anastasia Gurinovich ◽  
Harold Bae ◽  
John J Farrell ◽  
Stacy L Andersen ◽  
Stefano Monti ◽  
...  
Keyword(s):  
Genetic Variants ◽  
Association Studies ◽  
False Positive Rate ◽  
Principal Component ◽  
True Positive Rate ◽  
Genome Wide Association ◽  
Supplementary Information ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Positive Rate

Abstract Motivation Over the last decade, more diverse populations have been included in genome-wide association studies. If a genetic variant has a varying effect on a phenotype in different populations, genome-wide association studies applied to a dataset as a whole may not pinpoint such differences. It is especially important to be able to identify population-specific effects of genetic variants in studies that would eventually lead to development of diagnostic tests or drug discovery. Results In this paper, we propose PopCluster: an algorithm to automatically discover subsets of individuals in which the genetic effects of a variant are statistically different. PopCluster provides a simple framework to directly analyze genotype data without prior knowledge of subjects’ ethnicities. PopCluster combines logistic regression modeling, principal component analysis, hierarchical clustering and a recursive bottom-up tree parsing procedure. The evaluation of PopCluster suggests that the algorithm has a stable low false positive rate (∼4%) and high true positive rate (>80%) in simulations with large differences in allele frequencies between cases and controls. Application of PopCluster to data from genetic studies of longevity discovers ethnicity-dependent heterogeneity in the association of rs3764814 (USP42) with the phenotype. Availability and implementation PopCluster was implemented using the R programming language, PLINK and Eigensoft software, and can be found at the following GitHub repository: https://github.com/gurinovich/PopCluster with instructions on its installation and usage. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Efficient multivariate analysis algorithms for longitudinal genome-wide association studies

2019 ◽  
Vol 35 (23) ◽  
pp. 4879-4885 ◽  
Author(s):  
Chao Ning ◽  
Dan Wang ◽  
Lei Zhou ◽  
Julong Wei ◽  
Yuanxin Liu ◽  
...  
Keyword(s):  
Longitudinal Data ◽  
Software Package ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Association Studies ◽  
Genome Wide Association ◽  
Supplementary Information ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Computational Speed

Abstract Motivation Current dynamic phenotyping system introduces time as an extra dimension to genome-wide association studies (GWAS), which helps to explore the mechanism of dynamical genetic control for complex longitudinal traits. However, existing methods for longitudinal GWAS either ignore the covariance among observations of different time points or encounter computational efficiency issues. Results We herein developed efficient genome-wide multivariate association algorithms for longitudinal data. In contrast to existing univariate linear mixed model analyses, the proposed method has improved statistic power for association detection and computational speed. In addition, the new method can analyze unbalanced longitudinal data with thousands of individuals and more than ten thousand records within a few hours. The corresponding time for balanced longitudinal data is just a few minutes. Availability and implementation A software package to implement the efficient algorithm named GMA (https://github.com/chaoning/GMA) is available freely for interested users in relevant fields. Supplementary information Supplementary data are available at Bioinformatics online.

EpiGEN: an epistasis simulation pipeline

2020 ◽  
Vol 36 (19) ◽  
pp. 4957-4959
Author(s):  
David B Blumenthal ◽  
Lorenzo Viola ◽  
Markus List ◽  
Jan Baumbach ◽  
Paolo Tieri ◽  
...  
Keyword(s):  
Arbitrary Order ◽  
Association Studies ◽  
Simulated Data ◽  
Genome Wide Association ◽  
Supplementary Information ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Supplementary Data ◽  
Single Nucleotide ◽  
Genome Wide

Abstract Summary Simulated data are crucial for evaluating epistasis detection tools in genome-wide association studies. Existing simulators are limited, as they do not account for linkage disequilibrium (LD), support limited interaction models of single nucleotide polymorphisms (SNPs) and only dichotomous phenotypes or depend on proprietary software. In contrast, EpiGEN supports SNP interactions of arbitrary order, produces realistic LD patterns and generates both categorical and quantitative phenotypes. Availability and implementation EpiGEN is implemented in Python 3 and is freely available at https://github.com/baumbachlab/epigen. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Mixed Logistic Regression in Genome-Wide Association Studies

2020 ◽  
Author(s):  
Jacqueline Milet ◽  
Hervé Perdry
Keyword(s):  
Logistic Regression ◽  
Linear Models ◽  
Association Studies ◽  
Score Test ◽  
R Package ◽  
Genome Wide Association ◽  
Supplementary Information ◽  
Genome Wide Association Studies ◽  
Mixed Linear Models ◽  
Genome Wide

AbstractMotivationMixed linear models (MLM) have been widely used to account for population structure in case-control genome-wide association studies, the status being analyzed as a quantitative phenotype. Chen et al. proved that this method is inappropriate and proposed a score test for the mixed logistic regression (MLR). However this test does not allow an estimation of the variants’ effects.ResultsWe propose two computationally efficient methods to estimate the variants’ effects. Their properties are evaluated on two simulations sets, and compared with other methods (MLM, logistic regression). MLR performs the best in all circumstances. The variants’ effects are well evaluated by our methods, with a moderate bias when the effect sizes are large. Additionally, we propose a stratified QQ-plot, enhancing the diagnosis of p-values inflation or deflation, when population strata are not clearly identified in the sample.AvailabilityAll methods are implemented in the R package milorGWAS available at https://github.com/genostats/[email protected] informationSupplementary data are available at Bioinformatics online.

scholarly journals The druggable genome and support for target identification and validation in drug development

2016 ◽  
Author(s):  
Chris Finan ◽  
Anna Gaulton ◽  
Felix Kruger ◽  
Tom Lumbers ◽  
Tina Shah ◽  
...  
Keyword(s):  
Drug Development ◽  
Drug Target ◽  
Complex Disease ◽  
Target Identification ◽  
Association Studies ◽  
Target Selection ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Druggable Genome

Target identification (identifying the correct drug targets for each disease) and target validation (demonstrating the effect of target perturbation on disease biomarkers and disease end-points) are essential steps in drug development. We showed previously that biomarker and disease endpoint associations of single nucleotide polymorphisms (SNPs) in a gene encoding a drug target accurately depict the effect of modifying the same target with a pharmacological agent; others have shown that genomic support for a target is associated with a higher rate of drug development success. To delineate drug development (including repurposing) opportunities arising from this paradigm, we connected complex disease- and biomarker-associated loci from genome wide association studies (GWAS) to an updated set of genes encoding druggable human proteins, to compounds with bioactivity against these targets and, where these were licensed drugs, to clinical indications. We used this set of genes to inform the design of a new genotyping array, to enable druggable genome-wide association studies for drug target selection and validation in human disease.

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