I’d like to share a kidney eQTL data. This table provides 1,179,179 significant SNP~gene pairs identified by a false discovery rate (FDR) threshold of <0.01 (Storey’s q method) after meta-analysis of four eQTL studies (Sheng et al., Ko et al., GTEx, NephQTL).
If use this data, Please cite: Liu et al. Kidney epigenome and transcriptome-based multi-stage prioritization defines core cell types, genes and targetable mechanisms for kidney disease.
Hi @Shicheng_Guo and thank you for your question!
We ingest eQTL data from the eQTL Catalogue. I have contacted the team to find out if they plan to include the sumstats in their catalogue.
Thank you very much for sharing, I will keep you posted on any updates.
Irene
Hi @Shicheng_Guo,
Thanks for highlighting this dataset! As a general rule, we prefer to uniformly re-process the raw RNA-seq and genotype data instead of importing summary statistics. This helps to ensure that eQTLs from different studies are as comparable as possible. That being said, we are open to importing summary statistics from selected large meta-analysis efforts such as the Kidney eQTL Atlas that you have highlighted, because reprocessing would not be feasible.
However, even for imported summary statistics we would still require that all tested SNP-gene pairs are present and not just the significant ones. For this reason we are currently unable to import the data from the Kidney eQTL Atlas.
Best wishes,
Kaur Alasoo
eQTL Catalogue team
Thank you Kaur, which pipeline/workflow will be used in eQTL reprocessing?
Thanks.
Shicheng