In addition, we found that both models contribute to the genome-wide effect. One essential aspect of this is that many traits are perturbed much more in one direction than the other. We call this phenomenon “trait buffering”, as the curves are all buffered against one trait direction.

We hypothesize that genes with non-monotone effects likely affect the complex trait through multiple pathways. We explored one such gene in detail. TWAS-type methods, which assume a linear relationship between expression and trait, might not pick up on such genes.

First we wanted to know whether non-monotone genes, like in Model 2, even exist. Surprisingly, we found around 40% of gene-trait pairs have a non-monotonic relationship. That is, both deletion and duplication of these genes have the same effect on the trait!

Here are examples of top hits:

To model this, we introduce the gene dosage response curve (GDRC), which we define as the continuous relationship between gene dosage and average trait value. We propose two models that may explain the directional genome-wide effects. Both include a kind of directional buffering.

Our biological prior is that deleting a gene should have the opposite effect of duplicating the same gene. But somehow, aggregating this effect across a bunch of genes, results in a SAME-direction effect on average, genome-wide. Why?

For many traits there is a correlation between the number of duplications or loss-of-function (LoF) mutations someone carries, and their phenotype. Curiously, for most traits, these effects are aligned in the SAME direction. Why?