ArticleFrontiers in genetics2011
Six Degrees of Epistasis: Statistical Network Models for GWAS.
Article in Frontiers in genetics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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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.
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.
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Who cites it
28 citing papers in PubMed.
- A novel explainable deep-learning approach for network analysis of epistatic interactions.NAR genomics and bioinformatics · 2026Article
- Identifying microbial drivers in biological phenotypes with a Bayesian network regression model.Ecology and evolution · 2024Article
- Genome-wide unraveling SNP pairwise epistatic effects associated with sheep body weight.Animal biotechnology · 2023Article
- 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
- Nearest-neighbor Projected-Distance Regression (NPDR) for detecting network interactions with adjustments for multiple tests and confounding.Bioinformatics (Oxford, England) · 2020Article
- STatistical Inference Relief (STIR) feature selection.Bioinformatics (Oxford, England) · 2019Article
- Epistasis detectably alters correlations between genomic sites in a narrow parameter window.PloS one · 2019Article
- Evolutionary footprint of epistasis.PLoS computational biology · 2018Article
- The search for gene-gene interactions in genome-wide association studies: challenges in abundance of methods, practical considerations, and biological interpretation.Annals of translational medicine · 2018Article
- Genomics of human pulmonary tuberculosis: from genes to pathways.Current genetic medicine reports · 2017Article
- Prioritizing tests of epistasis through hierarchical representation of genomic redundancies.Nucleic acids research · 2017Article
- The Human Microbiome and the Missing Heritability Problem.Frontiers in genetics · 2017Article
- Detecting epistasis in human complex traits.Nature reviews. Genetics · 2014Review
- Spurious transcription factor binding: non-functional or genetically redundant?BioEssays : news and reviews in molecular, cellular and developmental biology · 2014Article
- Network.assisted analysis to prioritize GWAS results: principles, methods and perspectives.Human genetics · 2014Review
- Article
- Identifying disease associated genes by network propagation.BMC systems biology · 2014Article
- Pathways to neurodegeneration: mechanistic insights from GWAS in Alzheimer's disease, Parkinson's disease, and related disorders.American journal of neurodegenerative disease · 2013Review
- Encore: Genetic Association Interaction Network centrality pipeline and application to SLE exome data.Genetic epidemiology · 2013Article
- Inferring gene function and network organization in Drosophila signaling by combined analysis of pleiotropy and epistasis.G3 (Bethesda, Md.) · 2013Article
Corrections and comments
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2 authors.
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Abstract
There is growing evidence that much more of the genome than previously thought is required to explain the heritability of complex phenotypes. Recent studies have demonstrated that numerous common variants from across the genome explain portions of genetic variability, spawning various avenues of research directed at explaining the remaining heritability. This polygenic structure is also the motivation for the growing application of pathway and gene set enrichment techniques, which have yielded promising results. These findings suggest that the coordination of genes in pathways that are known to occur at the gene regulatory level also can be detected at the population level. Although genes in these networks interact in complex ways, most population studies have focused on the additive contribution of common variants and the potential of rare variants to explain additional variation. In this brief review, we discuss the potential to explain additional genetic variation through the agglomeration of multiple gene-gene interactions as well as main effects of common variants in terms of a network paradigm. Just as is the case for single-locus contributions, we expect each gene-gene interaction edge in the network to have a small effect, but these effects may be reinforced through hubs and other connectivity structures in the network. We discuss some of the opportunities and challenges of network methods for analyzing genome-wide association studies (GWAS) such as the study of hubs and motifs, and integrating other types of variation and environmental interactions. Such network approaches may unveil hidden variation in GWAS, improve understanding of mechanisms of disease, and possibly fit into a network paradigm of evolutionary genetics.
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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.