Boyle, E. A., Li, Y. I., & Pritchard, J. K. (2017). An Expanded View of Complex Traits: From Polygenic to Omnigenic. Cell. https://doi.org/10.1016/j.cell.2017.05.038
Boyle EA, Li YI, Pritchard JK. An Expanded View of Complex Traits: From Polygenic to Omnigenic. Cell. 2017. doi:10.1016/j.cell.2017.05.038
Boyle, Evan A., et al. "An Expanded View of Complex Traits: From Polygenic to Omnigenic." Cell, 2017.
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RRM Academy Synopsis
Omnigenic model proposes genes in disease-relevant cells affect risk
A 2017 Cell perspective proposes that genes outside core disease pathways may carry most of the heritability of complex traits. Heritability is the part of a trait's variation that inherited differences explain. The authors analyzed genetic data on height and three diseases.
Key Findings
In the height data, 697 loci reached genome-wide significance and together explained 16% of the phenotypic variance. Common variants together explained 86% of the expected heritability.
The authors estimated that 62% of common SNPs, single-letter DNA variants, link to some effect on height, counting variants that sit near causal ones.
The median SNP had an effect of 0.14 mm on height. The median genome-wide significant SNP had 1.43 mm, so the typical SNP had about one tenth that effect.
Putting the evidence together, the authors estimated that over 100,000 SNPs have independent causal effects on height.
On average, regions active in most cell types gave roughly as much heritability as cell type-specific regions (significantly different only in rheumatoid arthritis); inactive regions gave little or none.
Interpretation
The paper is a perspective article. The authors analyzed existing genome-wide association data and added a theoretical model, which they call the omnigenic model. They present the model as a hypothesis that needs testing. The estimates of causal variants for height rest on simplifying assumptions. The height analyses used a large meta-analysis, and the chromatin analyses covered Crohn's disease, rheumatoid arthritis and schizophrenia. The authors state that knowledge of cellular networks is incomplete. They add that core and peripheral genes may fall on a graduated scale.
RRM Context
Researchers also study endometriosis with genome-wide association methods. Restorative reproductive medicine is a cause-based field. It looks for what drives a condition in each woman and her partner, with cycle charting as one source of information.
Our editorial summary of this paper, not the article's abstract.
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.
