10.17608/k6.auckland.7295711.v2 Tayaza Fadason Tayaza Fadason William Schierding William Schierding Thomas Lumley Thomas Lumley Justin O'Sullivan Justin O'Sullivan eQTLs in the poly-unsaturated fat metabolism cluster The University of Auckland 2018 poly-unsaturated fatty acid metabolism eQTLs multimorbidity FADS1 FADS2 FADS3 TMEM258 Hi-C Medical Genetics (excl. Cancer Genetics) Quantitative Genetics (incl. Disease and Trait Mapping Genetics) Genetics 2018-11-07 13:51:54 Dataset https://auckland.figshare.com/articles/dataset/Supplementary_Data_4_eQTLs_in_the_poly-unsaturated_fat_metabolism_cluster/7295711 <div>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). Convex biclustering was then used to segregate phenotypes that share eGenes, which implies a common underlying molecular mechanism. In the cluster that is built around the <i>FADS</i> locus, SNPs have inverse eQTL effects on <i>FADS1</i>, <i>FADS2</i>, and the other genes in the region. Here, the reference and alternate alleles of the SNP positions are given with their frequencies.</div>