Open Targets Genetics: version 8 is out!

Release Notes Oct 13, 2022

The latest release of Open Targets Genetics — version 8 — is now available at

The L2G scores from this latest release have already been integrated into the Open Targets Platform in our most recent update — 22.09.

Key points

Additional notes and metrics are available on the Open Targets Community.

sQTLs from GTEx

We have integrated Splicing Quantitative Trait Loci (sQTLs) from GTEx version 8. These are derived from the leafcutter method.

The existing analyses for QTLs, including credible sets, and GWAS-Molecular Trait colocalisation, are now available for sQTLs.

A single gene can code for multiple proteins through a process known as alternative splicing. The DNA in a gene is made up of exons (the parts that can be used to make proteins) and introns (the parts that will never be transcribed). Alternative splicing combines exons in different ways, such that the resulting proteins have different amino acid sequences, and potentially different biological functions. Splice QTLs are genetic variants that affect the alternative splicing of a gene.

Importantly, the effects of splice QTLs are not always captured by expression QTLs or protein QTLs. Incorporating sQTLs into Open Targets Genetics will provide additional information to help identify target genes at disease-associated regions in the genome.

A set of sQTL colocalisations reported in the literature were independently verified by our colocalisation pipelines.

For example, the sQTL for CD33 colocalises with Alzheimer’s disease, confirming results found in Schwartzentruber et al. 2021

The gene prioritisation using colocalisation analysis widget for Alzheimer’s disease or family history of Alzheimer’s disease and locus around 19_51224706_C_A shows CD33 as the top prioritised gene, with the strongest evidence from the GTEx-sQTL.

Data updates: GWAS Catalog and FinnGen R6

The additional data in this release of Genetics brings the total number of studies in the portal to 57,244. Of these, 8,894 have full summary statistics/finemapping.

GWAS Catalog

We have ingested 6,591 new studies from 70 publications, without summary statistics.

FinnGen R6

We have incorporated data from FinnGen’s R6 data freeze.

FinnGen, an academia-industry partnership launched in 2017, combines genetic information and digital health care data from Finnish Biobank participants.

The R6 data freeze had data from over 260,000 individuals, analysed almost 17 million variants for over 2,800 disease endpoints (phenotypes). This data was made available to the public in January 2022. Read more about the results in FinnGen’s documentation.

We map the endpoints to the Experimental Factor Ontology, allowing users to compare GWAS results across studies.

Implementation of a Forest plot in the variant page

We have added forest plots to our variant pages, thanks to work from developers at the Hyve.

The forest plot enables users to filter and cross-compare the effect sizes of multiple trait categories associated with a given variant. This information complements the PheWAS plot to better understand the phenotypic effect of a given variant across studies.

Forest plot on the 4_102276925_A_G variant page in Open Targets Genetics, comparing the variant’s association with traits related to nervous system disease (in orange) and pancreas disease (in green). The colours correspond to the colours in the PheWAS plot.

We hope that the ability to contrast multiple traits will help users find new insights.

New API page

To help users understand the Genetics API, we have built an API page, available at

You can find example API queries, and modify them within the playground environment. We have tried to include a range of use cases, for example:

  • Which studies and lead variants are associated with Jak2?
  • Which genes were prioritised by the L2G pipeline at this locus from a given study?

The API page is accessible from the Genetics homepage. Browse the available queries, run them, and modify them in the playground space.

Like its sister page in the Open Targets Platform, this page was implemented using GraphiQL’s React component.

Our aim is to help users familiarise themselves with the Genetics API. Additional support is available in our data access documentation, and in the Open Targets Community forum, where you can also find out more about our variety of data access options to cater to different users needs.

Thoughts, comments, queries? Let us know, either by commenting below or on the Open Targets Community!