Chromatin interactions and expression quantitative trait loci reveal genetic drivers of multimorbidities

Abstract

Clinical studies of non-communicable diseases identify multimorbidities that reflect our relatively limited fixed metabolic capacity. Despite the fact that we have [~]24000 genes, we do not understand the genetic pathways that contribute to the development of multimorbid non-communicable disease. We created a \"multimorbidity atlas\" of traits based on pleiotropy of spatially regulated genes using convex biclustering. Using chromatin interaction and expression Quantitative Trait Loci (eQTL) data, we analysed 20,782 variants (p < 5 x 10-6) associated with 1,351 phenotypes, to identify 16,248 putative eQTL-eGene pairs that are involved in 76,013 short- and long-range regulatory interactions (FDR < 0.05) in different human tissues. Convex biclustering of eGenes that are shared between phenotypes identified complex inter-relationships between nominally different phenotype associated SNPs. Notably, the loci at the centre of these inter-relationships were subject to complex tissue and disease specific regulatory effects. The largest cluster, 40 phenotypes that are related to fat and lipid metabolism, inflammatory disorders, and cancers, is centred on the FADS1-FADS3 locus (chromosome 11). Our novel approach enables the simultaneous elucidation of variant interactions with genes that are drivers of multimorbidity and those that contribute to unique phenotype associated characteristics.