Systematic survey reveals general applicability of "guilt-by-association" within gene coexpression networks

By Cecily J. Wolfe, Isaac S. Kohane, Atul J. Butte. BMC Bioinformatics, 2005

431 citations recorded by the source corpus.

BackgroundBiological processes are carried out by coordinated modules of interacting molecules. As clustering methods demonstrate that genes with similar expression display increased likelihood of being associated with a common functional module, networks of coexpressed genes provide one framework for assigning gene function. This has informed the guilt-by-association (GBA) heuristic, widely invoked in functional genomics. Yet although the idea of GBA is accepted, the breadth of GBA applicability is uncertain.ResultsWe developed methods to systematically explore the breadth of GBA across a large and varied corpus of expression data to answer the following question: To what extent is the GBA heuristic broadly applicable to the transcriptome and conversely how broadly is GBA captured by a priori knowledge represented in the Gene Ontology (GO)? Our study provides an investigation of the functional organization of five coexpression networks using data from three mammalian organisms. Our method calculates a probabilistic score between each gene and each Gene Ontology category that reflects coexpression enrichment of a GO module. For each GO category we use Receiver Operating Curves to assess whether these probabilistic scores reflect GBA. This methodology applied to five different coexpression networks demonstrates that the signature of guilt-by-association is ubiquitous and reproducible and that the GBA heuristic is broadly applicable across the population of nine hundred Gene Ontology categories. We also demonstrate the existence of highly reproducible patterns of coexpression between some pairs of GO categories.ConclusionWe conclude that GBA has universal value and that transcriptional control may be more modular than previously realized. Our analyses also suggest that methodologies combining coexpression measurements across multiple genes in a biologically-defined module can aid in characterizing gene function or in characterizing whether pairs of functions operate together.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/2f1e11284b1743fd81e8df7046e48452384fa91f

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Citations in the corpus

Systems medicine: the future of medical genomics and healthcare (2009, background): “Taking advantage of expression-profiling surveys performed in extended human populations [171-174], systems biologists have started integrating physiopathology, network biology and DNA variations [175-177], providing novel insights into the mechanisms of various diseases, such as diabetes [178] and ”
Comparison of co-expression measures: mutual information, correlation, and model based indices (2012, background): “The network modules often have a biological interpretation in the sense that the modules are highly enriched in genes with a common functional annotation (gene ontology categories, cell type markers, etc) [3,30,31].”
Inferring cellular networks – a review (2007, background): “[6] argue that networks of coexpressed genes provide a widely applicable framework for assigning gene function.”
The Complex Regulation of Senescence (2012, background): “The basic idea of co-expression analysis is that genes with similar functions should have similar expression patterns (also termed “guilt-by-association”; Wolfe et al., 2005; Usadel et al., 2009).”
Multiscale Embedded Gene Co-expression Network Analysis (2015, background): “Coexpressed gene clusters represent coherent unique functional pathways not only in normal conditions [10, 11], but also in disease states[9, 12–14].”