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Related to the recent discussions on this forum about the potential for genetically engineering humans in the future, researchers from Stanford University recently published a fascinating article in the journal Cell, looking into the genetics of complex traits, like height, as well as the genetics of complex diseases, like schizophrenia, rheumatoid arthritis, and Crohn's disease. From their analysis they propose an "omnigenetic" model for complex traits:
If true, the results would have important implications for understanding complex diseases like schizophrenia and for efforts to genetically engineer humans. For example, the results suggests that looking for mutations or genetic variants that predispose individuals to higher risks of complex diseases may not shed any light into the underlying biology of the disease because such studies would ultimately find that every gene is implicated in the disease. Similarly, if every gene expressed in a cell contributes to every complex trait, we should be very careful when editing any gene as there is the potential for the edit to have unintended consequences on many different traits.
More detail about the model and the data supporting it are available in the paper cited below:
Boyle, Li and Pritchard. 2017. An expanded view of complex traits: From polygenetic to omnigenetic. Cell 169: 1177. doi:10.1016/j.cell.2017.05.038
Abstract:
https://www.theatlantic.com/science/archive/2017/06/its-like-all-connected-man/530532/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 staring 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.
If true, the results would have important implications for understanding complex diseases like schizophrenia and for efforts to genetically engineer humans. For example, the results suggests that looking for mutations or genetic variants that predispose individuals to higher risks of complex diseases may not shed any light into the underlying biology of the disease because such studies would ultimately find that every gene is implicated in the disease. Similarly, if every gene expressed in a cell contributes to every complex trait, we should be very careful when editing any gene as there is the potential for the edit to have unintended consequences on many different traits.
More detail about the model and the data supporting it are available in the paper cited below:
Boyle, Li and Pritchard. 2017. An expanded view of complex traits: From polygenetic to omnigenetic. Cell 169: 1177. doi:10.1016/j.cell.2017.05.038
Abstract:
A central goal of genetics is to understand the links between genetic variation and disease. Intuitively, one might expect disease-causing variants to cluster into key pathways that drive disease etiology. But for complex traits, association signals tend to be spread across most of the genome—including near many genes without an obvious connection to disease. We propose that gene regulatory networks are sufficiently interconnected such that all genes expressed in disease-relevant cells are liable to affect the functions of core disease-related genes and that most heritability can be explained by effects on genes outside core pathways. We refer to this hypothesis as an “omnigenic” model.