Institute for Molecular Bioscience, University of Queensland
Establishing the proportion of shared genetic control between expressed transcripts can be used to help identify pathways and genetic mechanisms that underlie disease susceptibility or severity. Using a bivariate GREML approach, we estimated the genetic correlations between each pairwise combination of 2,469 transcripts that are both highly heritable and expressed in whole blood in a cohort of 1,748 unrelated individuals of European ancestry. We identified 556 pairs with a significant genetic correlation at a Bonferroni study-wide threshold, of which 77% were located on different chromosomes to one another. Using eQTL data from an independent cohort (n=2,112) we subsequently identified 934 incidences where the eSNP for one probe had significant (p < 4.1×10–8) shared effect on the paired probe, providing further verification of our results. We investigated the genetic regulatory mechanisms underlying the co-regulation of highly correlated transcript pairs. Our findings reveal (i) a significant enrichment of highly interconnected transcription factors (p=3.43×10–25), (ii) shared eSNP-mediated transcriptional regulation and (iii) significant enrichment of genetically correlated transcripts pairs and regions of chromatin interaction (empirical p<0.001). We used estimates of genetic correlations to construct graph networks of interconnected transcripts which revealed the direction of shared genetic control between transcript isoforms and their correlated transcripts. Our findings demonstrate the utility of using genetic correlations to investigate transcriptional co-regulation and to gain valuable insight into the nature of the underlying genetic architecture of regulation. All results are made publicly available at http://computationalgenomics.com.au/shiny/rg/.