Summary statistics from GWAS Catalog, SAIGE and UK Biobank Neale Round 2
More studies from GWAS Catalog and traits from UK Biobank
How can we try to make sense of the vast amount of association data between common genetic variants (e.g. SNPs from GWAS) and complex traits (e.g. chronic kidney disease) available out there? Can we identify the likely causal gene behind an association? Can we modulate this causal gene and possibly guide drug target identification?
We are a step closer towards answering these questions. For the first time, we now include statistical colocalisation analysis in Open Targets Genetics.
Colocalisation tests whether two independent association signals at a locus are consistent with having a shared causal variant. These are the two hypotheses we currently test:
H3: trait 1 and trait 2 have an independent association
H4 : trait 1 and trait 2 have a shared association
The comparison above can be either between a disease and a molecular trait or among diseases
We are working with Daniel Zerbino and his team at EMBL-EBI on a brand new Open Targets-EBI eQTL database, yet to go live.
However we have an early access to this resource, containing eQTL from a variety of cells, such as:
monocyte, neutrophil, T cells
Skin, fat, LCL
iPSC, neuron, etc
Summary statistics from GWAS Catalog and SAIGE
Over the last two years, GWAS Catalog has been hosting full p-value summary statistics for curated publications, if available from authors.
In addition to the p-value, summary statitics include odds ratio, beta coefficient, and confidence intervals, to name a few. Head to the supplementary data of the latest NHGRI-EBI GWAS Catalog paper, for the complete format of GWAS summary statistics.
In this new release, we are excited to announce that in addition to the 2,139 UK Biobank Neale round 2 sumstats, we have now incremented our fine mapping expansion analysis to provide a credible set of variants for:
1,281 UKBiobank Phecode binary phenotypes using SAIGE analysis