The Implicitome: A Resource for Rationalizing Gene-Disease Associations

Abstract

High-throughput experimental methods such as medical sequencing and genome-wide association studies (GWAS) identify increasingly large numbers of potential relations between genetic variants and diseases. Both biological complexity (millions of potential gene-disease associations) and the accelerating rate of data production necessitate computational approaches to prioritize and rationalize potential gene-disease relations. Here, we use concept profile technology to expose from the biomedical literature both explicitly stated gene-disease relations (the explicitome) and a much larger set of implied gene-disease associations (the implicitome). Implicit relations are largely unknown to, or are even unintended by the original authors, but they vastly extend the reach of existing biomedical knowledge for identification and interpretation of gene-disease associations. The implicitome can be used in conjunction with experimental data resources to rationalize both known and novel associations. We demonstrate the usefulness of the implicitome by rationalizing known and novel gene-disease associations, including those from GWAS. To facilitate the re-use of implicit gene-disease associations, we publish our data in compliance with FAIR Data Publishing recommendations [https://www.force11.org/group/fairgroup] using nanopublications. An online tool (http://knowledge.bio) is available to explore established and potential gene-disease associations in the context of other biomedical relations.

Increasing phenotypic annotation improves the diagnostic rate of exome sequencing in a rare neuromuscular disorder

“…In the discovery paradigm, there are of course other in silico strategies for variant prioritization open to the researcher, including assessment of tissue expression and protein:protein interactions, other tools incorporating phenotype (Pengelly et al, ), and strategies for uncovering implicit associations between gene and functional effects even when these have not yet been published through direct evidence (Hettne et al, ). The tools studied here may only form a part of the analysis strategy in such cases, and functional analysis in model organisms as well as confirmatory cases from second families remains the mainstay of confirming a novel gene discovery.…”

Advanced literature analysis in a Big Data world

“…Implicit relations that are largely unknown to, or are unintended by, the original authors can be systematically identified using the Implicitome, an algorithm that uses a weighted semantic network to expose all gene-disease associations forming a literature-wide association study. 6 The vast majority of these gene-disease associations are implicit, associated by their mutual association to intermediate concepts; this information is catalogued and available for analysis from KNOWL-EDGE.BIO. We downloaded the Implicit Relations for Seckel syndrome (as referred to by the authors in the paper describing this tool 6 ).…”

Knowledge.Bio: A Web Application for Exploring, Building and Sharing Webs of Biomedical Relationships Mined from PubMed

“…SemRep natural language processing system [3,4]. The predicted gene-disease associations are incorporated from the "Implicitome", a resource computed from PubMed abstracts using 'concept profile' technology [5].…”

“…Clicking on such citation sentences also displays the associated PubMed abstract. For implicit relationships, clicking on the score field of the relationship displays a "Co-Occurrence" list of so-called "B" associated concepts which contribute transitively to the given implicit relationship between the seed "A" concept and matching "C" implicit concept (following Swanson's ABC model [8] as implemented by the Implicitome [5]).…”

“…A collection of sample Knowledge.Bio maps that can be loaded into the application can be found at https://figshare.com/articles/Demonstration_knowledge_bio_gr aphs/3394114 . Figure 5 encapsulates one example discovery process that could lead to a testable hypothesis (also described in [5]). Beginning with a search for Smith-Lemli-Opitz Syndrome, the user first selects the implicit relations tab and identifies CYP2R1 as potentially relevant gene ( Figure 5A).…”