10.17608/k6.auckland.7295681.v1
Tayaza Fadason
Tayaza
Fadason
William Schierding
William
Schierding
Thomas Lumley
Thomas
Lumley
Justin O'Sullivan
Justin
O'Sullivan
eGenes identified in GWAS traits
The University of Auckland
2018
eGenes
eQTLs
SNPs
GWAS
comorbidity
multimorbidy
Hi-C
complex diseases
Quantitative Genetics (incl. Disease and Trait Mapping Genetics)
Medical Genetics (excl. Cancer Genetics)
Genetics
2018-11-04 22:11:38
Dataset
https://auckland.figshare.com/articles/dataset/eGenes_identified_in_GWAS_traits/7295681
Chromatin interactions (using Hi-C) and functional (using eQTL) data were used to identify long-range regulatory associations involving >20,000 GWAS variants and their target genes (i.e. eGenes). This dataset contains eGenes identified in >1,350 traits, together with their associated eQTL SNPs.