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>