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.

scholarly journals 026.Endometriosis — linkage, positional cloning and genome wide association

2004 ◽  
Vol 16 (9) ◽  
pp. 26
Author(s):  
G. W. Montgomery ◽  
J. Wicks ◽  
Z. Z. Zhao ◽  
D. R. Nyholt ◽  
N. G. Martin ◽  
...  
Keyword(s):  
High Throughput ◽  
Positional Cloning ◽  
Complex Disease ◽  
Association Studies ◽  
Snp Markers ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Single Experiment ◽  
Priority List ◽  
Genome Wide

Endometriosis is a complex disease which affects up to 10% of women in their reproductive years. Common symptoms include severe dysmenorrhea and pelvic pain. The disease is associated with subfertility and some malignancies. Genetic and environmental factors both influence endometriosis. The aim of our studies is to identify genetic variation contributing to endometriosis and define pathways leading to disease. We recruited a large cohort of affected sister pair (ASP) families where two sisters have had surgically confirmed disease and conducted a 10�cM genome scan. The results of the linkage analysis identified one chromosomal region with significant linkage and one region of suggestive linkage. The regions implicated by these studies are generally of the order of 20–30�cM and include several hundred genes. Locating the gene or genes contributing to disease within the region is a challenging task. The best approach to the problem is association studies using a high density of SNP markers. The recent development of human SNP maps and high throughput SNP genotyping platforms makes this task easier. We have developed high throughput SNP typing at QIMR using the Sequenom MassARRAY platform. The method allows multiple SNP assays to be genotyped on the same sample in a single experiment. Throughput and genotyping costs depend critically on this level of multiplexing and we routinely genotype 6–8 SNPs in a single assay. We are using bioinformatics and functional approaches to develop a priority list of genes to screen early in the project. SNP markers in these genes are being genotyped using the MassARRAY platform to search for genes contributing to endometriosis. In the future, genome wide association studies with our families may locate additional genes contributing to endometriosis.

scholarly journals Discovering genetic interactions bridging pathways in genome-wide association studies

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Gang Fang ◽  
Wen Wang ◽  
Vanja Paunic ◽  
Hamed Heydari ◽  
Michael Costanzo ◽  
...  
Keyword(s):  
Statistical Power ◽  
Complex Disease ◽  
Association Studies ◽  
Genetic Network ◽  
Genetic Interactions ◽  
Gwas Data ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Cancer Breast ◽  
Genome Wide

Abstract Genetic interactions have been reported to underlie phenotypes in a variety of systems, but the extent to which they contribute to complex disease in humans remains unclear. In principle, genome-wide association studies (GWAS) provide a platform for detecting genetic interactions, but existing methods for identifying them from GWAS data tend to focus on testing individual locus pairs, which undermines statistical power. Importantly, a global genetic network mapped for a model eukaryotic organism revealed that genetic interactions often connect genes between compensatory functional modules in a highly coherent manner. Taking advantage of this expected structure, we developed a computational approach called BridGE that identifies pathways connected by genetic interactions from GWAS data. Applying BridGE broadly, we discover significant interactions in Parkinson’s disease, schizophrenia, hypertension, prostate cancer, breast cancer, and type 2 diabetes. Our novel approach provides a general framework for mapping complex genetic networks underlying human disease from genome-wide genotype data.

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 In Search of Complex Disease Risk through Genome Wide Association Studies

Mathematics ◽  
2021 ◽  
Vol 9 (23) ◽  
pp. 3083
Author(s):  
Lorena Alonso ◽  
Ignasi Morán ◽  
Cecilia Salvoro ◽  
David Torrents
Keyword(s):  
Genetic Variants ◽  
Complex Disease ◽  
Disease Risk ◽  
Association Studies ◽  
Personalised Medicine ◽  
Genome Wide Association ◽  
Phenotypic Traits ◽  
Genome Wide Association Studies ◽  
Treatment Protocols ◽  
Genome Wide

The identification and characterisation of genomic changes (variants) that can lead to human diseases is one of the central aims of biomedical research. The generation of catalogues of genetic variants that have an impact on specific diseases is the basis of Personalised Medicine, where diagnoses and treatment protocols are selected according to each patient’s profile. In this context, the study of complex diseases, such as Type 2 diabetes or cardiovascular alterations, is fundamental. However, these diseases result from the combination of multiple genetic and environmental factors, which makes the discovery of causal variants particularly challenging at a statistical and computational level. Genome-Wide Association Studies (GWAS), which are based on the statistical analysis of genetic variant frequencies across non-diseased and diseased individuals, have been successful in finding genetic variants that are associated to specific diseases or phenotypic traits. But GWAS methodology is limited when considering important genetic aspects of the disease and has not yet resulted in meaningful translation to clinical practice. This review presents an outlook on the study of the link between genetics and complex phenotypes. We first present an overview of the past and current statistical methods used in the field. Next, we discuss current practices and their main limitations. Finally, we describe the open challenges that remain and that might benefit greatly from further mathematical developments.

scholarly journals Leveraging Human Genetics to Guide Cancer Drug Development

2018 ◽  
pp. 1-11 ◽  
Author(s):  
Ben Kinnersley ◽  
Amit Sud ◽  
Elizabeth A. Coker ◽  
Joseph E. Tym ◽  
Patrizio Di Micco ◽  
...  
Keyword(s):  
Drug Development ◽  
Phase Ii ◽  
Genetic Information ◽  
Association Studies ◽  
Genome Wide Association ◽  
Cancer Drug ◽  
Precision Oncology ◽  
Genome Wide Association Studies ◽  
Fold Enrichment ◽  
Genome Wide

Purpose The high attrition rate of cancer drug development programs is a barrier to realizing the promise of precision oncology. We have examined whether the genetic insights from genome-wide association studies of cancer can guide drug development and repurposing in oncology. Materials and Methods Across 37 cancers, we identified 955 genetic risk variants from the National Human Genome Research Institute-European Bioinformatics Institute genome-wide association study catalog. We linked these variants to target genes using strategies that were based on linkage disequilibrium, DNA three-dimensional structure, and integration of predicted gene function and expression. With the use of the Informa Pharmaprojects database, we identified genes that are targets of unique drugs and assessed the level of enrichment that would be afforded by incorporation of genetic information in preclinical and phase II studies. For targets not under development, we implemented machine learning approaches to assess druggability. Results For all preclinical targets incorporating genetic information, a 2.00-fold enrichment of a drug being successfully approved could be achieved (95% CI, 1.14- to 3.48-fold; P = .02). For phase II targets, a 2.75-fold enrichment could be achieved (95% CI, 1.42- to 5.35-fold; P < .001). Application of genetic information suggests potential repurposing of 15 approved nononcology drugs. Conclusion The findings illustrate the value of using insights from the genetics of inherited cancer susceptibility discovery projects as part of a data-driven strategy to inform drug discovery. Support for cancer germline genetic information for prospective targets is available online from the Institute of Cancer Research.

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