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.