ReviewHuman genetics2014
Network.assisted analysis to prioritize GWAS results: principles, methods and perspectives.
Review in Human genetics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers, 4 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
60 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Deciphering the genetic landscape of obesity: a data-driven approach to identifying plausible causal genes and therapeutic targets.Journal of human genetics · 2023Pooled it
- A Review of Statistical Methods for Identifying Trait-Relevant Tissues and Cell Types.Frontiers in genetics · 2020Pooled it
- Common variants in the MKL1 gene confer risk of schizophrenia.Schizophrenia bulletin · 2015Pooled it
- Systematic prioritization and integrative analysis of copy number variations in schizophrenia reveal key schizophrenia susceptibility genes.Schizophrenia bulletin · 2014Pooled it
- From GWAS Signals to Molecular Mechanisms: Explainable AI for Causal Gene Prioritization and Biomolecular Target Interpretation.Biomolecules · 2026Review
- Genomic Signatures of Domestication in European Seabass (Ecology and evolution · 2024Article
- Pharmacogenomics of clinical response to Natalizumab in multiple sclerosis: a genome-wide multi-centric association study.Journal of neurology · 2024Article
- Rare and Common Variants Associated with Alcohol Consumption Identify a Conserved Molecular Network.bioRxiv : the preprint server for biology · 2024Article
- Protein-protein interaction network-based integration of GWAS and functional data for blood pressure regulation analysis.Human genomics · 2024Article
- scGraph2Vec: a deep generative model for gene embedding augmented by graph neural network and single-cell omics data.GigaScience · 2024Article
- Allelic variation in transcription factor PtoWRKY68 contributes to drought tolerance in Populus.Plant physiology · 2023Article
- Genome-wide association studies of human and rat BMI converge on synapse, epigenome, and hormone signaling networks.Cell reports · 2023Article
- NetMix2: A Principled Network Propagation Algorithm for Identifying Altered Subnetworks.Journal of computational biology : a journal of computational molecular cell biology · 2022Article
- COVID-GWAB: A Web-Based Prediction of COVID-19 Host Genes via Network Boosting of Genome-Wide Association Data.Biomolecules · 2022Article
- From random to predictive: a context-specific interaction framework improves selection of drug protein-protein interactions for unknown drug pathways.Integrative biology : quantitative biosciences from nano to macro · 2022Article
- Distinct effect of prenatal and postnatal brain expression across 20 brain disorders and anthropometric social traits: a systematic study of spatiotemporal modularity.Briefings in bioinformatics · 2021Article
- A computational approach for detecting physiological homogeneity in the midst of genetic heterogeneity.American journal of human genetics · 2021Article
- EyeDiseases: an integrated resource for dedicating to genetic variants, gene expression and epigenetic factors of human eye diseases.NAR genomics and bioinformatics · 2021Article
- Pathway analysis for genome-wide genetic variation data: Analytic principles, latest developments, and new opportunities.Journal of genetics and genomics = Yi chuan xue bao · 2021Review
- A framework for pathway knowledge driven prioritization in genome-wide association studies.Genetic epidemiology · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Genome-wide association studies (GWAS) have rapidly become a powerful tool in genetic studies of complex diseases and traits. Traditionally, single marker-based tests have been used prevalently in GWAS and have uncovered tens of thousands of disease-associated SNPs. Network-assisted analysis (NAA) of GWAS data is an emerging area in which network-related approaches are developed and utilized to perform advanced analyses of GWAS data in order to study various human diseases or traits. Progress has been made in both methodology development and applications of NAA in GWAS data, and it has already been demonstrated that NAA results may enhance our interpretation and prioritization of candidate genes and markers. Inspired by the strong interest in and high demand for advanced GWAS data analysis, in this review article, we discuss the methodologies and strategies that have been reported for the NAA of GWAS data. Many NAA approaches search for subnetworks and assess the combined effects of multiple genes participating in the resultant subnetworks through a gene set analysis. With no restriction to pre-defined canonical pathways, NAA has the advantage of defining subnetworks with the guidance of the GWAS data under investigation. In addition, some NAA methods prioritize genes from GWAS data based on their interconnections in the reference network. Here, we summarize NAA applications to various diseases and discuss the available options and potential caveats related to their practical usage. Additionally, we provide perspectives regarding this rapidly growing research area.
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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 Socratic graph.