ArticleProceedings of the National Academy of Sciences of the United States of America2024
Quantitative omnigenic model discovers interpretable genome-wide associations.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Evolutionary adaptation proceeds through a small number of phenotypic modules.Molecular biology and evolution · 2026Article
- Regulatory network topology and the genetic architecture of gene expression.Cell genomics · 2026Article
- Identification of novel reproducible combinatorial genetic risk factors for myalgic encephalomyelitis in the DecodeME patient cohort and commonalities with long COVID.Journal of translational medicine · 2026Article
- Back to the future 2: the implications of germplasm structure on the balance between short- and long-term genetic gain in a changing target population of environments.G3 (Bethesda, Md.) · 2026Article
- The interaction of biological network topology and mutation effects in complex trait evolution.Frontiers in systems biology · 2026Review
- Large-scale causal discovery using interventional data sheds light on gene network structure in k562 cells.Nature communications · 2025Article
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Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
As their statistical power grows, genome-wide association studies (GWAS) have identified an increasing number of loci underlying quantitative traits of interest. These loci are scattered throughout the genome and are individually responsible only for small fractions of the total heritable trait variance. The recently proposed omnigenic model provides a conceptual framework to explain these observations by postulating that numerous distant loci contribute to each complex trait via effect propagation through intracellular regulatory networks. We formalize this conceptual framework by proposing the "quantitative omnigenic model" (QOM), a statistical model that combines prior knowledge of the regulatory network topology with genomic data. By applying our model to gene expression traits in yeast, we demonstrate that QOM achieves similar gene expression prediction performance to traditional GWAS with hundreds of times less parameters, while simultaneously extracting candidate causal and quantitative chains of effect propagation through the regulatory network for every individual gene. We estimate the fraction of heritable trait variance in
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.