ArticleGenome research2011
Prioritizing candidate disease genes by network-based boosting of genome-wide association data.
Article in Genome research, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 371 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
371 citing papers in PubMed, 2 syntheses or guidelines pooled it, 713 citations in OpenAlex.
- A comprehensive meta-analysis and prioritization study to identify vitiligo associated coding and non-coding SNV candidates using web-based bioinformatics tools.Scientific reports · 2022Pooled it
- A systems biology approach uncovers cell-specific gene regulatory effects of genetic associations in multiple sclerosis.Nature communications · 2019Pooled it
- Genetic landscape of an in vivo protein interactome.Nature genetics · 2026Article
- A module-based approach for post-omics, post-GWAS network-based gene classification.Bioinformatics (Oxford, England) · 2026Article
- Human genetics across levels of biological organization.Nature reviews. Genetics · 2026Review
- A tissue-specific atlas of protein-protein associations enables prioritization of candidate disease genes.Nature biotechnology · 2026Article
- A Co-essentiality Network of Cancer Driver Genes Better Prioritizes Anticancer Drugs.Genomics, proteomics & bioinformatics · 2025Article
- Denoising self-supervised learning for disease-gene association prediction.BMC bioinformatics · 2025Article
- The Importance of Regulatory Network Structure for Complex Trait Heritability and Evolution.Molecular biology and evolution · 2025Article
- RNA-Seq Uncovers Association of Endocrine-Disrupting Chemicals with Hub Genes and Transcription Factors in Aggressive Prostate Cancer.International journal of molecular sciences · 2025Article
- The Functional Comparison of Eukaryotic Proteomes: Implications for Choosing an Appropriate Model Organism to Probe Human Biology.Methods in molecular biology (Clifton, N.J.) · 2025Article
- A hypergraph neural network for prioritizing Alzheimer's disease risk genes.Frontiers in genetics · 2025Article
- Prioritization of causal genes from genome-wide association studies by Bayesian data integration across loci.PLoS computational biology · 2025Article
- The Ihara zeta function as a partition function for network structure characterisation.Scientific reports · 2024Article
- A-to-I RNA co-editing predicts clinical outcomes and is associated with immune cells infiltration in hepatocellular carcinoma.Communications biology · 2024Article
- HGTMDA: A Hypergraph Learning Approach with Improved GCN-Transformer for miRNA-Disease Association Prediction.Bioengineering (Basel, Switzerland) · 2024Article
- Specific circulating miRNAs are associated with plasma lipids in a healthy American cohort.Physiological genomics · 2024Article
- HGSMDA: miRNA-Disease Association Prediction Based on HyperGCN and Sørensen-Dice Loss.Non-coding RNA · 2024Article
- Network propagation for GWAS analysis: a practical guide to leveraging molecular networks for disease gene discovery.Briefings in bioinformatics · 2024Review
- ReHoGCNES-MDA: prediction of miRNA-disease associations using homogenous graph convolutional networks based on regular graph with random edge sampler.Briefings in bioinformatics · 2024Article
311 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
Abstract
Network "guilt by association" (GBA) is a proven approach for identifying novel disease genes based on the observation that similar mutational phenotypes arise from functionally related genes. In principle, this approach could account even for nonadditive genetic interactions, which underlie the synergistic combinations of mutations often linked to complex diseases. Here, we analyze a large-scale, human gene functional interaction network (dubbed HumanNet). We show that candidate disease genes can be effectively identified by GBA in cross-validated tests using label propagation algorithms related to Google's PageRank. However, GBA has been shown to work poorly in genome-wide association studies (GWAS), where many genes are somewhat implicated, but few are known with very high certainty. Here, we resolve this by explicitly modeling the uncertainty of the associations and incorporating the uncertainty for the seed set into the GBA framework. We observe a significant boost in the power to detect validated candidate genes for Crohn's disease and type 2 diabetes by comparing our predictions to results from follow-up meta-analyses, with incorporation of the network serving to highlight the JAK-STAT pathway and associated adaptors GRB2/SHC1 in Crohn's disease and BACH2 in type 2 diabetes. Consideration of the network during GWAS thus conveys some of the benefits of enrolling more participants in the GWAS study. More generally, we demonstrate that a functional network of human genes provides a valuable statistical framework for prioritizing candidate disease genes, both for candidate gene-based and GWAS-based studies.
Indexed as
Identifiers
What OpenQuestion holds
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.