An expanded view of complex traits: from polygenic to omnigenic

By Evan A. Boyle, Yang I Li, J. K. Pritchard. Cell, 2017

2,673 citations recorded by the source corpus.

Summary 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 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.

Subfields: Genetics

View Paper: https://www.semanticscholar.org/paper/1239b88111a5f1c20622d58406fb15bf02c31947

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Citations in the corpus

Sequential regulatory activity prediction across chromosomes with convolutional neural networks (2017, background): “Although many studies show strong relationships between variation in genotype and phenotype across a range of human diseases and traits, the mechanisms through which this relationship operates remain incompletely understood (Boyle et al. 2017) .”
Gene Regulatory Network Inference from Single-Cell Data Using Multivariate Information Measures (2016, background): “…(De Smet et al. 2010, Oates et al. 2012, Thorne et al. 2012, Thorne et al. 2013, Siegenthaler et al. 2014, Oates et al. 2014, Huang et al. 2014, Young et al. 2014), and it is now becoming a pivotal stepping stone in dissecting the molecular contributions of complex diseases (Boyle et al. 2017).”
Polygenic risk score for schizophrenia is more strongly associated with ancestry than with schizophrenia (2018, background): “This finding also impacts on the observation that for complex traits very large numbers of association signals are detected across the genome - the so-called "omnigenic" effect (Boyle et al., 2017).”
Open Community Challenge Reveals Molecular Network Modules with Key Roles in Diseases (2018, background): “An “omnigenic” model recently proposed by Boyle et al. (2017) explains this observation by the high interconnectivity of molecular networks, which implies that most of the expressed genes in a disease-relevant tissue are likely to be at least weakly connected to core genes and may thus have…”
All for one and one for all: Mental disorders in one dimension (2018, background): “At the molecular level, it appears that genetic effects on psychiatric disorders are both polygenic and pleiotropic (69).”