Phen2Gene: Rapid Phenotype-Driven Gene Prioritization for Rare Diseases

Abstract

Human Phenotype Ontology (HPO) terms are increasingly used in diagnostic settings to aid in the characterization of patient phenotypes. The HPO annotation database is updated frequently and can provide detailed phenotype knowledge on various human diseases, and many HPO terms are now mapped to candidate causal genes with binary relationships. To further improve the genetic diagnosis of rare diseases, we incorporated these HPO annotations, gene-disease databases, and gene-gene databases in a probabilistic model to build a novel HPO-driven gene prioritization tool, Phen2Gene. Phen2Gene accesses a database built upon this information called the HPO2Gene Knowledgebase (H2GKB), which provides weighted and ranked gene lists for every HPO term. Phen2Gene is then able to access the H2GKB for patient-specific lists of HPO terms or PhenoPackets descriptions supported by GA4GH

Advances in the development of PubCaseFinder, including the new application programming interface and matching algorithm

“…The Monarch Initiative (Shefchek et al, 2020) and Orphadata (Nguengang Wakap et al, 2020) have gathered manually annotated GPA and DPA that are extracted from literature and databases, such as Online Mendelian Inheritance in Man (OMIM) and Orphanet, and encoded those phenotypes with the corresponding HPO terms. Using these HPO‐based annotations and biomedical ontologies, many gene‐prioritizing systems, such as the Automated Mendelian Literature Evaluation (Birgmeier et al, 2020), Phen2Gene (Zhao et al, 2020), and Variant Interpretation using Biomedical Literature Evidence (van der Velde et al, 2020), and phenotype‐driven differential diagnosis systems, such as Phenomizer (Köhler et al, 2009), Genetic Disease Diagnosis based on Phenotypes (Chen et al, 2019), and Likelihood Ratio Interpretation of Clinical Abnormalities (LIRICAL) (Robinson et al, 2020) have been implemented. In addition, most patient repositories participating in the Matchmaker Exchange (MME) (Azzariti & Hamosh, 2020), an international collaborative project for matchmaking of unrelated patients through a standardized application programming interface (API), also use HPO to encode patient phenotypes.…”

The GA4GH Phenopacket schema: A computable representation of clinical data for precision medicine

“…The Phenopacket provides a standard input format for these tools that will simplify computational analysis pipelines, especially if the steps in the pipeline include a comparison of the results of multiple tools. Exomiser, 29 LIRICAL, 61 Phen2Gene, 62 and CADA 63 can take Phenopackets as input files, and other analysis tools will soon accept phenotype data in Phenopacket format.…”

Learning phenotypic patterns in genetic diseases by symptom interaction modeling

“…The combined system, which we called PhenoGenius provided the best performance and reached a nearly full match (99.8%, 1682/1686) for all clinical descriptions. We compared PhenoGenius to four recently published algorithms for phenotype-driven gene prioritization: PhenoApt, Phen2Gene, CADA, and LIRICAL (7,(14)(15)(16). Despite using different prioritization methodologies, these four programs demonstrated similar performances in phenotype matching (Figure 5C).…”

“…This work has been partially supported by MIAI@Grenoble Alpes (ANR-19-P3IA-0003). 6 , Nicolas Chatron 7 , Cedric Le Maréchal 8 , Jean-François Taly 9 , Wilfrid Carre 10 , Claire Bardel 11 , Frederic Tran Mau-Them 6 , Marc Planes 12 , Marie-Pierre Audrezet 12 , Laure Raymond 9 , Charles Coutton 13 , Pierre Ray 13 , Veronique Satre 13 , Klaus Dietrich 13 , Isabelle Marey 13 , Françoise Devillard 13 , Radu Harbuz 13 , Florence Amblard 13 , Pauline Le Tanno 13 , Mouna Barat-Houari 14 , Marjolaine Willems 14 , Thomas Guignard 14 , Sylvie Odent 15 , Marie de Tayrac 10 , Damien Sanlaville 7 , Laurence Faivre 6 , Laurent Mesnard 16…”