Big leap for the @OpenTargets platform. Gene burden from 450k+ @uk_biobank exomes from @AstraZeneca and @Regeneron, ClinVar structural variants, NLP classification of clinical trials stop reasons and drug label indications,etc. Systematically filling the gaps one-by-one 🧵
https://twitter.com/opentargets/status/1519667850159570944
On our mission to identify potential causal targets from genetics 🧬, the strongly-powered @uk_biobank burden tests provide us an opportunity to close the gap between rare disease variation, and common variation from GWAS studies - as post-processed by the Genetics Portal
The 2 complementary analyses expand our view on the mechanistic effect of LoF variants, quantitative traits and genetics in more diverse populations. Big thanks to the authors for data sharing and @GWASCatalog for archiving and harmonising the information
On a different front, we include for the first time variation from structural variants deposited in #ClinVar. Associating targets linked to this events is not free from challenges. Kudos to @evarchive
We have also made a push to update our clinical data. @lesya_rzvvsk and @TRozday had both used #NLP to improve the classification of clinical trial stop reasons and drug label indications, respectively. More detail soon to come on these really cool machine-learning projects.
Overall, this release continues our efforts on integrating systematic data on our platform public version. Something only possible by the strong support and guidance of our 6 @OpenTargets partners: @bmsnews @emblebi @GSK @sangerinstitute @sanofi @pfizer
• • •
Missing some Tweet in this thread? You can try to
force a refresh
