Taken together, modularity and degree dispersion mean that the genetic architecture of gene expression is likely more sparse and pleiotropic than would be expected under a totally random GRN model (matched for median cis-h2%).

(8/)

Master regulators add motifs too, but they also make the GRN “shallower” by shortening path lengths between genes. This tends to decrease the % of trans-h2.

(7/)

It’s nice to know that about GRNs, but why do these properties matter? I’m glad you asked.

Modularity adds local structural motifs (i.e., triangles and diamonds), which can increase or decrease the effects of trans-eQTLs (depending on how many genes are activators).

(6/)

Long story short, some of our simulated GRNs look like real data.

These networks tend to have modular groups, master regulators, a high proportion of activators, and a fixed ratio of sparsity and regulatory strength.

(5/)

Inferring GRNs is tough, but it’s easy enough to simulate them.

Here, we use a linear model of expression on randomly generated DAGs, with the goal of finding GRNs that match the observed distribution of cis-h2 fraction.

(4/)

Expression QTLs are a nice way to map genetic effects onto genes (e.g., SNP → E → Trait). But a lot of expression variance (~70% of h2) is spread across the genome, ergo hard to discover statistically.

Data from pubmed.ncbi.nlm.nih.gov/31558840/

(2/)