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Genepanel.iobio - an easy to use web tool for generating disease- and phenotype-associated gene lists
BMC Medical Genomics volume 12, Article number: 190 (2019)
When ordering genetic testing or triaging candidate variants in exome and genome sequencing studies, it is critical to generate and test a comprehensive list of candidate genes that succinctly describe the complete and objective phenotypic features of disease. Significant efforts have been made to curate gene:disease associations both in academic research and commercial genetic testing laboratory settings. However, many of these valuable resources exist as islands and must be used independently, generating static, single-resource gene:disease association lists. Here we describe genepanel.iobio (https://genepanel.iobio.io) an easy to use, free and open-source web tool for generating disease- and phenotype-associated gene lists from multiple gene:disease association resources, including the NCBI Genetic Testing Registry (GTR), Phenolyzer, and the Human Phenotype Ontology (HPO). We demonstrate the utility of genepanel.iobio by applying it to complex, rare and undiagnosed disease cases that had reached a diagnostic conclusion. We find that genepanel.iobio is able to correctly prioritize the gene containing the diagnostic variant in roughly half of these challenging cases. Importantly, each component resource contributed diagnostic value, showing the benefits of this aggregate approach. We expect genepanel.iobio will improve the ease and diagnostic value of generating gene:disease association lists for genetic test ordering and whole genome or exome sequencing variant prioritization.
A tremendous amount of genetic and biomedical knowledge has been deposited in both curated and computationally-derived gene:disease association database resources such as NCBI’s Genetic Testing Registry (GTR) , Phenolyzer , and the Human Phenotype Ontology (HPO) . However, each resource has its own format, data structure and output, making it difficult for a genetics professional to navigate between resources; and especially difficult to merge the outputs of these resources into a concise, non-redundant list that encompasses the phenotypic features of disease. As such, there is a strong need in the genetics and genomics community for a tool capable of consolidating and harmonizing these gene:disease association resources and providing genetics professionals with a single prioritized disease-candidate gene list.
Within a typical genepanel.iobio usage, a user provides relevant terms to each resource tool. For instance, starting with the NCBI Genetic Testing Registry input step, the user will enter one or more presumed genetic disorder terms, selecting the term from the typeahead drop down. In the Phenolyzer input step, the user searches and then selects one or more disease-relevant phenotype terms. In the HPO input step the user can either directly input HPO terms or copy/paste a clinical note and allow the built-in ClinPhen  tool to identify and rank relevant HPO terms from the clinical note (Fig. 1a). These input steps can be performed independently of one another, allowing the user to utilize some or all of the available resources. Within each input step the user can apply advanced resource-specific filters to further customize and refine the genes generated by that resource. For example, in the GTR step, the user can filter on commercial testing providers and/or disease modes of inheritance, etc. Following data input genepanel.iobio presents the user with a summary page of the prioritized union of genes across all resources where data has been inputted. This final summary page also allows the user to further refine and modify the gene list and export a final list of genes to a text file, comma-separated file or copy them to the system clipboard (Fig. 1b).
Following development, we tested the efficacy of genepanel.iobio to correctly prioritize diagnostic variants in a clinical genetics setting. We chose an ambitious test setting, selecting cases for which diagnostic conclusions had been reached previously at the Penelope Undiagnosed and Rare Disease Program at the University of Utah. The rate of diagnosis in this setting remains between 35 and 45%, often complicated by complex clinical presentations and ambiguous or blended phenotypes [1, 3]. Two analysts, blinded to the causal variants, independently analyzed 16 previously diagnosed clinical cases to see if genepanel.iobio could correctly prioritize the gene containing the diagnostic variant within the final gene list. These analysts, with no speciality or expert knowledge of the genetic disorders or the genes associated with them, were able to correctly prioritize the diagnostic gene in their final gene list in 7 of the 16 clinical cases (44%). The total number of genes in the final gene list were typically around 150 genes, a number we deemed reasonable for gene panel testing and genetic sequencing variant review workflows. The diagnostic gene was the number one ranked gene in the final gene list for 2 (analyst 1) and 3 (analyst 2) of the clinical cases. Additionally, for over one third of cases (44% analyst 1, 38% analyst 2), the diagnostic gene was in the top 50 genes of the final gene list (Fig. 1c). Importantly, each resource contributed to the analysts’ ability to correctly prioritize the diagnostic gene (Fig. 1d). For the cases where genepanel.iobio failed to correctly prioritize the diagnostic gene, we attribute a large degree of these failures to the lack of objective findings in the phenotype descriptions (Additional file 1: Table S2). These challenging cases were largely described in less descriptive terms such as developmental delay, growth delay, hypotonia and macrocephaly. Additional metrics about the gene lists generated and phenotype descriptions given to the analysts can be found in Additional file 1: Table S1 and S2.
These results demonstrate the benefit of genepanel.iobio and its aggregate approach of using multiple gene:disease association resources. We are actively developing and maintaining genepanel.iobio and will be incorporating new features and resources in the future.
Genomic medicine has greatly benefited from the increasing wealth of knowledge in gene:disease association databases and resources. However, it remains difficult to harmonize results across multiple such resources. To address this difficulty we developed genepanel.iobio, a free, open-source, platform independent web application capable of generating a comprehensive list of genes associated with a user-provided set of suspected disorders and phenotypes. We demonstrate the utility of genepanel.iobio in a clinical genetics setting by its ability to generate a gene list containing a reasonable number of genes that described the clinical phenotype and most importantly contained the diagnostic gene. We anticipate adoption of genepanel.iobio into clinical genetics workflows will improve the diagnostic value of genetic test ordering as well as variant/gene prioritization in genetic sequencing studies.
Availability and requirements
Project name: genepanel.iobio
Project home page: https://genepanel.iobio.io and https://github.com/iobio/genepanel.iobio
Operating system(s): Platform independent
Other requirements: Chrome browser version 76 or greater
Any restrictions to use by non-academics: none
Availability of data and materials
Application programming interface
NCBI Genetic Testing Registry
Human Phenotype Ontology
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R01HG009000 from NHGRI to G.T.M., R01HG009712 from NHGRI to G.T.M. - The funding bodies played no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.
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Additional file 1: Table S1.
Summary of the diagnostic analysis for 16 previously diagnosed clinical cases, Table S2. Phenotype descriptions that were given to each analyst, whether each analyst was able to correctly identify the diagnostic gene using genepanel.iobio and a likely rationale for the analysts’.
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Ekawade, A., Velinder, M., Ward, A. et al. Genepanel.iobio - an easy to use web tool for generating disease- and phenotype-associated gene lists. BMC Med Genomics 12, 190 (2019). https://doi.org/10.1186/s12920-019-0641-1