Regulatory network topology and the genetic architecture of gene expression
Matthew Aguirre1, Jeffrey P Spence2, Guy Sella3
1Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
Abstract:
Most genetic variance in gene expression is due to trans-acting expression quantitative trait loci (eQTLs) spread across the genome. However, these loci are generally hard to map due to limited discovery power. Here, we simulate how local properties of expression regulation and global properties of regulatory networks alter the genome-wide proportions of cis- and trans-heritability. We find that network motifs and modular groups can reduce or enhance the effects of trans-eQTLs and that hub regulators shorten paths across the network and act as key sources of trans-acting variance. Critically, networks with all these features best recapitulate the observed distribution of cis- and trans-heritability. Taken together, our results suggest that the genome-wide genetic architecture of gene expression involves fewer regulators for each gene but implicates the same regulators more often across genes (i.e., is less polygenic and more pleiotropic) than previously anticipated.
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