Yes, he says, there will be “core genes” that follow this pattern. They will affect traits in ways that make biological sense. But genes don’t work in isolation. They influence each other in large networks, so that “if a variant changes any one gene, it could change an entire gene network,” says Boyle. He believes that these networks are so thoroughly interconnected that every gene is just a few degrees of separation away from every other. Which means that changes in basically any gene will ripple inwards to affect the core genes for a particular trait.

The Stanford trio call this the “omnigenic model.” In the simplest terms, they’re saying that most genes matter for most things.

More specifically, it means that all the genes that are switched on in a particular type of cell—say, a neuron or a heart muscle cell—are probably involved in almost every complex trait that involves those cells. So, for example, nearly every gene that’s switched on in neurons would play some role in defining a person’s intelligence, or risk of dementia, or propensity to learn. Some of these roles may be starring parts. Others might be mere cameos. But few genes would be left out of the production altogether.

This might explain why the search for genetic variants behind complex traits has been so arduous. For example, a giant study called… er… GIANT looked at the genomes of 250,000 people and identified 700 variants that affect our height. As predicted, each has a tiny effect, raising a person’s stature by just a millimeter. And collectively, they explain just 16 percent of the variation in heights that you see in people of European ancestry. That’s not very much, especially when scientists estimate that some 80 percent of all human height variation can be explained by genetic factors. Where’s that missing fraction?

Pritchard’s team re-analyzed the GIANT data and calculated that there are probably more than 100,000 variants that affect our height, and most of these shift it by just a seventh of a millimeter. They’re so minuscule in their effects that it’s hard to tell them apart from statistical noise, which is why geneticists typically ignore them. And yet, Pritchard’s team noted that many of these weak signals cropped up consistently across different studies, which suggests that they are real results. And since these variants are spread evenly across the entire genome, they implicate a “substantial fraction of all genes,” Pritchard says.

The team found more evidence for their omnigenic model by analyzing other large genetic studies of rheumatoid arthritis, schizophrenia, and Crohn’s disease. Many of the variants identified by these studies seem relevant to the disease in question. For example, some of the schizophrenia variants affect genes involved in the nervous system. But mostly, the variants affect genes that don’t make for compelling stories, and that do pretty generic things. According to the omnigenic model, they’re only contributing to the risk of disease in incidental ways, by rippling across to the more relevant core genes. “It’s the only model I can come up with that make all the data fit,” Pritchard says.