#AGBT – Jeff Barret, Open Innovation Partnerships to bridge the gap from GWAS to drug targets.

Jeff Barret, Welcome Trust Sanger Institute

Drug development almost always fails.  (Hay et al, 2014)  85%+ molecules never make it to clinic.  A large fraction of late phase failures happen because of lack of efficacy.

What are ways we can improve that rate?  Human genetics can be very helpful – The ones that have genetic evidence are dramatically more likely to work.  (Retrospective look, but can we use it to predict?)  Can we develop preclinical models that help?

GSK – EMBL-EBI and Sanger came together.  Added Biogen as a partner.  We need to all collaborate – based at Welcome Genome Campus in UK.

Two things they want to do:  1. Create a bioinformatics that integrates as many data sources as possible in a systematic way.  2. Do large scale investigation (High throughput genomics).

First one is hard: combining all these things is hard – had to develop a unified model for combining data sources.  TargetValidation.org.

[Very cool demo here!]

They don’t want to be the database of record for this – instead they are a portal and integration for others.


Target information is the key outcome.  Genome scale where possible -physiological  relevance to disease.  Key technologies: use strengths from partners (Ensembl)

20 open projects, 3 disease area, human cellular experiments, leveraging genetics to build and enable resources to do more experiments.  Encourage cycle that improves data, which improves platforms, etc.

Example using Genetic screens of Immune cell function. (Use iPS derived macrophages.)  Pragmatic approach to make it possible to do this.

Mission: pre-competitive approach, committed to rapid publication, non-exclusive partnerships.

Example: IBD.  Lots of data to sift through.  We have an amazing machine that finds gene associations, but we need a new one for understanding causal variants.

  • high marker density and big sample size to do fine mapping. (Using chips, 60,000 samples + imputation.)
  • Super clean dataset & novel stats technique.  Data QC is very important
  • Building cell-specific maps (cell specificity is critical)
  • Zoom in with Sequencing (WGS)
  • Disease relevant cellular models.

We are doing a good job of finding variants, and almost all of it is coding.  Non-coding may be altering expression.  eQTL done: Not much more found than you would expect by chance.  We don’t have a good handle on what expression changes are doing (in IBD.)

Some of what’s hiding this is that we’re looking in the wrong types of cells.  Effect of non-coding variants is going to significantly affect specific cell types.

Discussion of issues where targets in specific cell types make it dangerous to use drugs because they have adverse effects in other tissue types than desired.

All this data can be brought together – tissue sample all the way to in vitro testing.

What makes this unique?  Bringing everything together.

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