%0 Generic %A Fadason, Tayaza %D 2018 %T Visualisation files for multimorbidity analysis %U https://auckland.figshare.com/articles/dataset/Visualisation_files_for_multimorbidity_analysis/7308944 %R 10.17608/k6.auckland.7308944.v1 %2 https://auckland.figshare.com/ndownloader/files/13502573 %K multimorbidity %K comorbidity %K GWAS %K pleiotropy %K chromatin interaction %K complex disease %K Hi-C %K eQTL %K convex biclustering %K Medical Genetics (excl. Cancer Genetics) %K Quantitative Genetics (incl. Disease and Trait Mapping Genetics) %K Genetics %X 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) in >1,350 phenotypes in the GWAS Catalog. Using convex biclustering, we segregated phenotypes based on the eGenes they share, which implies a common underlying molecular mechanism. Understanding the roles the eGenes play has potential applications in understanding multimorbidities and drug repurposing.
This datasource underlie the figures generated in the study
%I The University of Auckland