A major hurdle in re-using published eQTL datasets to interpret GWAS results is that summary statistics are often unavailable of incomplete (e.g. missing betas, effect alleles or non-significant associations). 2/9
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This is especially relevant for transcript-level and splicing QTLs where the results vary greatly depending on which reference annotations and quantification methods are used. 3/9
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We have re-analysed raw gene expression and genotype data from 19 published eQTL studies (8,115 RNA-seq samples and 4,631 microarray samples from 4,685 unique donors). 4/9pic.twitter.com/iQUXedYpJh
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We have mapped QTLs at the level of gene expression, exon expression, transcript usage, and promoter, splice junction and 3ʹ end usage. 5/9
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All summary statistics can be downloaded by FTP or accessed via a REST API: http://www.ebi.ac.uk/eqtl/Data_access/ … 6/9
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A subset of the results have already been integrated to the
@targetvalidate Genetics Portal (https://genetics.opentargets.org/ ). 7/9Prikaži ovu nit -
This would not have been possible without the great team:
@kerimovOn,@dzerbino, James Hayhurst,@irenepapatheodo,@simonjupp,@DrP_stuff,@pinin4fjords,@TonyBurdett. Support from@targetvalidate,@emblebi and@UniTartuCS. 8/9Prikaži ovu nit -
Finally, if you want see how our results can be used to prioritise target genes for Alzheimer’s disease, check out the latest paper from
@jschwart37 https://www.medrxiv.org/content/10.1101/2020.01.22.20018424v1 … 9/9Prikaži ovu nit
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