An Expanded View of Complex Traits: From Polygenic to Omnigenic
Open accessAbstract
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.
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Further challenges for non-coding variant interpretation include linkage disequilibrium, the possibility that more than one variant contributes to a given association, the need to match cell type with disease, and unclear mechanisms by which non-coding variants exert effects on disease risk.
“Further challenges include linkage disequilibrium, the possibility that more than one variant contributes to a given association, the need to match the cell type in which functional characterization and/or biochemical annotation is being carried out with the disease in question, and the fact that the mechanisms by which non-coding variants exert their effects on disease risk remain unclear223.”
From Towards a comprehensive catalogue of validated and target-linked human enhancers (2020) · cites this paper as [223]
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredMost medically and evolutionarily relevant traits are complex, with hundreds or thousands of loci across the genome contributing to them.
“most medically and evolutionarily relevant traits are complex, with hundreds or thousands of loci across the genome contributing to them34,35.”
From Quantifying the contribution of Neanderthal introgression to the heritability of complex traits (2021) · cites this paper as [35]
Recent evidence from quantitative genetics in humans suggests that polygenic traits tend to be omnigenic; heritable variation in traits is not mainly due to a core biological process or pathway, but causative variants may act on almost any gene expressed in a relevant cell type, mediated by trans-regulatory variation.
“Recent evidence from quantitative genetics (Boyle et al. 2017) in humans suggests that polygenic traits tend to be omnigenic. That is, heritable variation in traits is not mainly due to a core biological process or pathway, but causative variants may act on almost any gene expressed in a relevant cell type, mediated by trans-regulatory variation (Liu et al. 2019).”
From The neural crest cell hypothesis: no unified explanation for domestication (2021) · cites this paper as Boyle et al. 2017
Parental education and parental age were significantly correlated, more strongly for mothers (r2 = 0.06, P = 3.5 × 10−52) than fathers (r2 = 0.03, P = 1.3 × 10−23).
“We confirmed in our dataset a significant correlation of parental education and parental age47 that was stronger for mothers (r2 = 0.06, P = 3.5 × 10−52; Supplementary Table 19) than for fathers (r2 = 0.03, P = 1.3 × 10−23).”
From A phenotypic spectrum of autism is attributable to the combined effects of rare variants, polygenic risk and sex (2022) · cites this paper as [47]
This convergence and enrichment provides reassurance that genetic associations will coalesce around molecular and cellular processes with a core role in pathogenesis.
“This provides reassurance that, even as the number of association signals for a given disease proliferates, the genetic associations uncovered will coalesce around molecular and cellular processes with a core role in pathogenesis62,63.”
From A brief history of human disease genetics (2020) · cites this paper as [62]
Similar heterogeneity arises in patient data from nearly all common diseases, including cardiovascular, metabolic, and neurodevelopmental disorders, in which recurrent patterns are elusive, making reliable predictions difficult.
“Similar heterogeneity arises in patient data from nearly all common diseases, including cardiovascular, metabolic, and neurodevelopmental disorders, in which recurrent patterns are elusive, making it difficult to make reliable predictions (Boyle et al., 2017).”
From Visible Machine Learning for Biomedicine (2018) · cites this paper as Boyle et al., 2017
Machine-learning systems face input heterogeneity as a challenge that is particularly acute in biomedical applications.
“Machine-learning systems face two recognized challenges that become particularly acute in biomedical applications. The first is input heterogeneity.”
From Visible Machine Learning for Biomedicine (2018) · cites this paper as Boyle et al., 2017
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Visible Machine Learning for Biomedicine
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“…of pervasive positive selection, either in the form of soft or hard sweeps (Kern and Hahn, 2018) or of positive selection on polygenic traits (Boyle et al., 2017) in our genome could also lead to a correlation between genetic diversity and recombination, as the effect of selection on linked…”
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“Such pleiotropic effects are manifest in the genetic correlations among seemingly distinct traits [49,50] and to be expected from consideration of network pleiotropy (as outlined in [51,52]).”
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- Authors
- Boyle, E. A., Li, Y. I., & Pritchard, J. K.
- Affiliations
- Stanford University (US)
- Howard Hughes Medical Institute (US)
- Corresponding author
- Boyle, Evan A.
- Li, Yang I.
- Pritchard, Jonathan K.
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2017-06
- Volume
- 169
- Issue
- 7
- Pages
- 1177-1186
- ISSN
- 0092-8674 (ISSN-L)
- PMC
- PMC5536862
- OpenAlex
- W2626606138
- Cited by
- 2,851
- References
- 81
- Citations by year
- 2026: 215 · 2025: 228 · 2024: 260 · 2023: 263 · 2022: 307 · 2021: 423 · 2020: 389 · 2019: 371 · 2018: 292 · 2017: 97 · 2016: 3 · 2014: 1
- FWCI
- 40.44
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Cell (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Science Foundation
- National Human Genome Research Institute (R01HG008140, T32HG000044)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Keywords
- complex traits, omnigenic model, polygenic model, gene regulatory networks, heritability, genetic association signals, disease pathways
- MeSH terms
- Animals, Disease, Humans, Multifactorial Inheritance, Polymorphism, Single Nucleotide, Genomics, Genetic Diseases, Inborn, Genome-Wide Association Study
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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