Data

Tools

Indexes

Applications

Experiments

Open Source Science

genetics-docs

https://github.com/opentargets/genetics-docs

Science Score: 18.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ○
    codemeta.json file
  • ○
    .zenodo.json file
  • ○
    DOI references
  • ○
    Academic publication links
  • ○
    Academic email domains
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (9.8%) to scientific vocabulary

Keywords

docs gitbook
Last synced: 6 months ago · JSON representation ·

Repository

Basic Info
  • Host: GitHub
  • Owner: opentargets
  • License: apache-2.0
  • Default Branch: master
  • Size: 14.3 MB
Statistics
  • Stars: 11
  • Watchers: 17
  • Forks: 1
  • Open Issues: 1
  • Releases: 0
Topics
docs gitbook
Created over 7 years ago · Last pushed almost 2 years ago
Metadata Files
Readme Changelog License Citation

README.md

Introduction

Overview

Open Targets Genetics is a tool highlighting variant-centric statistical evidence to allow both prioritisation of candidate causal variants at trait-associated loci and identification of potential drug targets.

It is well established that proximity is a poor basis on which to prioritise 'causal' genes at a trait-associated locus. Rather, integrating functional and biological data from multiple heterogeneous sources allows functionally implicated genes to be highlighted. Our portal aggregates and merges genetic associations from the literature, newly-derived loci from UK Biobank — including functional genomics data (e.g. chromatin conformation, chromatin interactions) — and quantitative trait loci (eQTLs, pQTLs and sQTLs). We apply statistical fine-mapping across thousands of trait-associated loci to resolve association signals, and link each variant to its proximal and distal target gene(s), using a single evidence score. Integrated cross-trait colocalisation analyses and linking to detailed pharmaceutical compounds extend the capacity of the Open Targets Genetics portal to explore drug repositioning opportunities and shared genetic architecture. Take a look at our data pipeline and data sources for more detailed information.

Whatever your starting point — gene, trait or variant — Open Targets Genetics enables detailed biological insight and causal gene prioritisation and informs target decision making. It can be used to answer specific biological and target hypotheses or as an exploratory too (e.g. to prioritise genes at associated loci in a new GWAS).

For additional help with Open Targets Genetics, or to report bugs, data issues, or submit a feature request, please post on the Open Targets Community, using the relevant categories and tags. If the request you would like to make has already been posted, please like the post to indicate you would like this to be prioritised.

{% hint style="success" %} Open Targets Genetics is an open source, freely available research tool that is available for both academic and commercial purposes. If you use our data or our computation pipelines in your work, please cite our latest publication. {% endhint %}

Owner

  • Name: Open Targets
  • Login: opentargets
  • Kind: organization

An innovative, multi-year, industry-academia partnership that generates research insights to support systematic drug target identification and prioritisation

Citation (citation.md) 

# Citation

## Latest publication

If you use Open Targets Genetics in your work, please cite our latest publications:

Ghoussaini M, Mountjoy E, Carmona M, Peat G, Schmidt EM, Hercules A, Fumis L, Miranda A, Carvalho-Silva D, Buniello A, Burdett T, Hayhurst J, Baker J, Ferrer J, Gonzalez-Uriarte A, Jupp S, Karim MA, Koscielny G, Machlitt-Northen S, Malangone C, Pendlington ZM, Roncaglia P, Suveges D, Wright D, Vrousgou O, Papa E, Parkinson H, MacArthur JAL, Todd JA, Barrett JC, Schwartzentruber J, Hulcoop DG, Ochoa D, McDonagh EM, Dunham I. **Open Targets Genetics: systematic identification of trait-associated genes using large-scale genetics and functional genomics**. Nucleic Acids Res. 2021 Jan 8;49(D1):D1311-D1320. DOI: [10.1093/nar/gkaa840](https://doi.org/10.1093/nar/gkaa840). PMID: [33045747](https://pubmed.ncbi.nlm.nih.gov/33045747/); PMCID: [PMC7778936](https://europepmc.org/article/PMC/7778936).

Mountjoy E, Schmidt EM, Carmona M, Schwartzentruber J, Peat G, Miranda A, Fumis L, Hayhurst J, Buniello A, Karim MA, Wright D, Hercules A, Papa E, Fauman EB, Barrett JC, Todd JA, Ochoa D, Dunham I, Ghoussaini M. **An open approach to systematically prioritize causal variants and genes at all published human GWAS trait-associated loci**. Nat Genet. 2021 Nov;53(11):1527-1533. doi: [10.1038/s41588-021-00945-5](https://doi.org/10.1038/s41588-021-00945-5). Epub 2021 Oct 28. PMID: [34711957](https://pubmed.ncbi.nlm.nih.gov/34711957/); PMCID: [PMC7611956](https://europepmc.org/article/PMC/PMC7611956).

## Other publications

### Preprints

Mountjoy E, Schmidt EM, Carmona M, et al. **Open Targets Genetics: An open approach to systematically prioritize causal variants and genes at all published human GWAS trait-associated loc**i. bioRxiv. 2020. DOI: [10.1101/2020.09.16.299271](https://doi.org/10.1101/2020.09.16.299271). PPR: PPR215497.

GitHub Events

Total
Last Year