ArticlePloS one2011
A network-based approach to prioritize results from genome-wide association studies.
Article in PloS one, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 2 of them syntheses that pooled it.
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Who cites it
35 citing papers in PubMed, 2 syntheses or guidelines pooled it.
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- Abnormal hemispheric asymmetry of both brain function and structure in attention deficit/hyperactivity disorder: a meta-analysis of individual participant data.Brain imaging and behavior · 2022Pooled it
- Multimodal Integration of Protein Interactomes With Genomic and Molecular Data Discovers Distinct Rheumatoid Arthritis Endotypes.Arthritis & rheumatology (Hoboken, N.J.) · 2026Article
- networkGWAS: a network-based approach to discover genetic associations.Bioinformatics (Oxford, England) · 2023Article
- Multiomics approach reveals a role of translational machinery in shaping maize kernel amino acid composition.Plant physiology · 2022Article
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- Network Medicine in Pathobiology.The American journal of pathology · 2019Review
- Data Integration in Poplar: 'Omics Layers and Integration Strategies.Frontiers in genetics · 2019Review
- Network approaches to systems biology analysis of complex disease: integrative methods for multi-omics data.Briefings in bioinformatics · 2018Review
- Leveraging human genetic and adverse outcome pathway (AOP) data to inform susceptibility in human health risk assessment.Mammalian genome : official journal of the International Mammalian Genome Society · 2018Article
- Convergence between biological, behavioural and genetic determinants of obesity.Nature reviews. Genetics · 2017Article
- Tissue-specific network-based genome wide study of amygdala imaging phenotypes to identify functional interaction modules.Bioinformatics (Oxford, England) · 2017Article
- Systems biology approach to late-onset Alzheimer's disease genome-wide association study identifies novel candidate genes validated using brain expression data and Caenorhabditis elegans experiments.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2017Article
- Incorporating networks in a probabilistic graphical model to find drivers for complex human diseases.PLoS computational biology · 2017Article
- GWAB: a web server for the network-based boosting of human genome-wide association data.Nucleic acids research · 2017Article
- NETWORK-BASED GENOME WIDE STUDY OF HIPPOCAMPAL IMAGING PHENOTYPE IN ALZHEIMER'S DISEASE TO IDENTIFY FUNCTIONAL INTERACTION MODULES.Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference) · 2017Article
- An integrative approach to investigate the respective roles of single-nucleotide variants and copy-number variants in Attention-Deficit/Hyperactivity Disorder.Scientific reports · 2016Article
- Pathway Analysis Incorporating Protein-Protein Interaction Networks Identified Candidate Pathways for the Seven Common Diseases.PloS one · 2016Article
- Oncogenes and tumor suppressor genes: comparative genomics and network perspectives.BMC genomics · 2015Article
Corrections and comments
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Authors and funding
16 authors.
Funding
Abstract
Genome-wide association studies (GWAS) are a valuable approach to understanding the genetic basis of complex traits. One of the challenges of GWAS is the translation of genetic association results into biological hypotheses suitable for further investigation in the laboratory. To address this challenge, we introduce Network Interface Miner for Multigenic Interactions (NIMMI), a network-based method that combines GWAS data with human protein-protein interaction data (PPI). NIMMI builds biological networks weighted by connectivity, which is estimated by use of a modification of the Google PageRank algorithm. These weights are then combined with genetic association p-values derived from GWAS, producing what we call 'trait prioritized sub-networks.' As a proof of principle, NIMMI was tested on three GWAS datasets previously analyzed for height, a classical polygenic trait. Despite differences in sample size and ancestry, NIMMI captured 95% of the known height associated genes within the top 20% of ranked sub-networks, far better than what could be achieved by a single-locus approach. The top 2% of NIMMI height-prioritized sub-networks were significantly enriched for genes involved in transcription, signal transduction, transport, and gene expression, as well as nucleic acid, phosphate, protein, and zinc metabolism. All of these sub-networks were ranked near the top across all three height GWAS datasets we tested. We also tested NIMMI on a categorical phenotype, Crohn's disease. NIMMI prioritized sub-networks involved in B- and T-cell receptor, chemokine, interleukin, and other pathways consistent with the known autoimmune nature of Crohn's disease. NIMMI is a simple, user-friendly, open-source software tool that efficiently combines genetic association data with biological networks, translating GWAS findings into biological hypotheses.
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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.