ArticlePLoS genetics2020
Protein-Protein interactions uncover candidate 'core genes' within omnigenic disease networks.
Article in PLoS genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 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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Who cites it
18 citing papers in PubMed, 30 citations in OpenAlex.
- From GWAS Signals to Molecular Mechanisms: Explainable AI for Causal Gene Prioritization and Biomolecular Target Interpretation.Biomolecules · 2026Review
- Translating genome-wide association studies at multiple scales: Drug target prioritization, cellular architectures, and organ imaging.Cell genomics · 2026Review
- 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
- Exploring the omnigenic architecture of selected complex traits.American journal of human genetics · 2025Article
- DNA-damage-associated protein co-expression network in cardiomyocytes informs on tolerance to genetic variation and disease.iScience · 2025Article
- Prioritization of causal genes from genome-wide association studies by Bayesian data integration across loci.PLoS computational biology · 2025Article
- An Integrated Neuromuscular Training Intervention Applied in Primary School Induces Epigenetic Modifications in Disease-Related Genes: A Genome-Wide DNA Methylation Study.Scandinavian journal of medicine & science in sports · 2025Article
- Protein-protein interaction network-based integration of GWAS and functional data for blood pressure regulation analysis.Human genomics · 2024Article
- Introducing critical proteins related to liver ischemia/reperfusion injury.Gastroenterology and hepatology from bed to bench · 2024Article
- The PENGUIN approach to reconstruct protein interactions at enhancer-promoter regions and its application to prostate cancer.Nature communications · 2023Article
- Implementing Core Genes and an Omnigenic Model for Behaviour Traits Prediction in Genomics.Genes · 2023Review
- Inferring the mode and strength of ongoing selection.Genome research · 2023Article
- AAAKB: A manually curated database for tracking and predicting genes of Abdominal aortic aneurysm (AAA).PloS one · 2023Article
- Persistent homology analysis of type 2 diabetes genome-wide association studies in protein-protein interaction networks.Frontiers in genetics · 2023Article
- Multi-omics peripheral and core regions of cancer.NPJ systems biology and applications · 2022Article
- Persistent dysregulation of genes in the development of endometriosis.Annals of translational medicine · 2022Article
- Integrative Analysis of Next-Generation Sequencing for Next-Generation Cancer Research toward Artificial Intelligence.Cancers · 2021Review
- Altered Gene Expression in the Testis of Infertile Patients with Nonobstructive Azoospermia.Computational and mathematical methods in medicine · 2021Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
Abstract
Genome wide association studies (GWAS) of human diseases have generally identified many loci associated with risk with relatively small effect sizes. The omnigenic model attempts to explain this observation by suggesting that diseases can be thought of as networks, where genes with direct involvement in disease-relevant biological pathways are named 'core genes', while peripheral genes influence disease risk via their interactions or regulatory effects on core genes. Here, we demonstrate a method for identifying candidate core genes solely from genes in or near disease-associated SNPs (GWAS hits) in conjunction with protein-protein interaction network data. Applied to 1,381 GWAS studies from 5 ancestries, we identify a total of 1,865 candidate core genes in 343 GWAS studies. Our analysis identifies several well-known disease-related genes that are not identified by GWAS, including BRCA1 in Breast Cancer, Amyloid Precursor Protein (APP) in Alzheimer's Disease, INS in A1C measurement and Type 2 Diabetes, and PCSK9 in LDL cholesterol, amongst others. Notably candidate core genes are preferentially enriched for disease relevance over GWAS hits and are enriched for both Clinvar pathogenic variants and known drug targets-consistent with the predictions of the omnigenic model. We subsequently use parent term annotations provided by the GWAS catalog, to merge related GWAS studies and identify candidate core genes in over-arching disease processes such as cancer-where we identify 109 candidate core genes.
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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.