The Human Phenotype Ontology: A Tool for Annotating and Analyzing Human Hereditary Disease

Institute for Medical Genetics, Charité-Universitätsmedizin Berlin, 13353 Berlin, Germany.
The American Journal of Human Genetics (Impact Factor: 10.93). 11/2008; 83(5):610-5. DOI: 10.1016/j.ajhg.2008.09.017
Source: PubMed


There are many thousands of hereditary diseases in humans, each of which has a specific combination of phenotypic features, but computational analysis of phenotypic data has been hampered by lack of adequate computational data structures. Therefore, we have developed a Human Phenotype Ontology (HPO) with over 8000 terms representing individual phenotypic anomalies and have annotated all clinical entries in Online Mendelian Inheritance in Man with the terms of the HPO. We show that the HPO is able to capture phenotypic similarities between diseases in a useful and highly significant fashion.

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    • "Recently, several studies have been published that have investigated the expected yield of clinical exome sequencing [Yang et al., 2013; Soden et al., 2014; Yang et al., 2014; Zemojtel et al., 2014]. The phenotypic matching algorithms often made use of the Human Phenotype Ontology [Robinson et al., 2008; Köhler et al., 2009; Köhler et al., 2014]. Several groups have developed algorithms to use HPO analysis as a part of gene prioritization in exome sequencing [Sifrim et al., 2013; Javed et al., 2014; Singleton et al., 2014; Robinson et al., 2014b]. "
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    ABSTRACT: Genomic matchmaking databases allow participants to submit genomic and phenotypic data with the goal of identifying previously uncharacterized disease-associated genes by "matching" to other comparable cases. Current estimates suggest that there are at least 3000 Mendelian disease-associated genes that have not yet been characterized as such, but the true number may be substantially higher. Therefore, Genomic matchmaking databases are addressing a pressing medical need, and it is important to ask how they should be designed and how much data they should strive to contain in order to identify a certain number of these genes. In this work, we argue that genomic matchmaking has similarities to the so called "birthday paradox", which refers to the observation that within a group of just 23 persons, two people will have the same birthday with probability greater than 50%. We develop a series of simulations to provide a rough estimate of the number of cases required and to explore the influence of parameters such as genetic heterogeneity, mode of inheritance, background variation, precision of phenotypic descriptions, disease prevalence, and the accuracy of bioinformatic pathogenicity prediction programs on the performance of genomic matchmaking. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.
    Human Mutation 08/2015; 36(10). DOI:10.1002/humu.22848 · 5.14 Impact Factor
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    • "Subsequently a computational method termed Phenotypic Interpretation of eXomes (PhenIX), that evaluates variants based on population frequency and predicted pathogenicity and then ranks the genes according to variant score and a clinical relevance score, was used to prioritize candidate genes [Zemojtel et al., 2014]. To calculate the clinical relevance score we entered the following human phenotype ontology [Robinson et al., 2008] terms: 2e3 toe cutaneous syndactyly (HP0005709); proximal/ middle symphalangism of 3rd finger (HP:0009482); proximal/ middle symphalangism of 2nd finger (HP:0009579), distal/middle symphalangism of 2nd finger (HP:0009563), short proximal phalanx of 2nd finger (HP:0009597), Aplasia/Hypoplasia of the middle phalanx of the 2nd finger (HP:0009568); Aplasia/Hypoplasia of the middle phalanx of the 3rd finger (HP0009437); Aplasia/Hypoplasia of the middle phalanx of the 4th finger (HP:0009299); Aplasia/ "
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    ABSTRACT: Pfeiffer syndrome (MIM: #101600) is a rare autosomal dominant disorder classically characterized by limb and craniofacial anomalies. It is caused by heterozygous mutations in the fibroblast growth factor receptors types 1 and 2 (FGFR1 and FGFR2). We applied a next generation sequencing (NGS) panel approach comprising all 2877 genes currently known to be causative for one or more Mendelian diseases combined with the phenotype based computational tool PhenIX (Phenotypic Interpretation of eXomes). We report on a patient presenting with multiple anomalies of hands and feet including brachydactyly and symphalangism. No clinical diagnosis could be established based on the clinical findings and testing of several genes associated with brachydactyly and symphalangism failed to identify mutations. Via next generation sequencing (NGS) panel approach we then identified a novel de novo missense FGFR2 mutation affecting an amino acid reported to be mutated in Pfeiffer syndrome. Since our patient shows typical radiological findings of Pfeiffer syndrome in hands and feet but at the same time lacks several characteristic features such as clinical signs of craniosynostosis and prominent eyes we suggest introducing the term "FGFR2 associated phenotypes" for similar cases. Our results highlight the emerging role of combined NGS and phenotype based bioinformatics strategies to establish clinical diagnoses. Copyright © 2015. Published by Elsevier Masson SAS.
    European journal of medical genetics 06/2015; 58(8). DOI:10.1016/j.ejmg.2015.05.007 · 1.47 Impact Factor
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    • "To this end the Human Phenotype Ontology (HPO) project [2] provides a comprehensive and well-structured set of more than 10000 terms (classes) that represent human phenotypic abnormalities annotated to more than 7000 hereditary syndromes listed in OMIM, Orphanet and DECIPHER databases [3]. This resource offers an ontology, that is, a conceptualization of the human phenotypes that can be processed by computational methods, and provides a translational bridge from genome-scale biology to a disease-centered view of human pathobiology [4]. "
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    ABSTRACT: The Human Phenotype Ontology (HPO) provides a conceptualization of phenotype information and a tool for the computational analysis of human diseases. It covers a wide range of phenotypic abnormalities encountered in human diseases and its terms (classes) are structured according to a directed acyclic graph. In this context the prediction of the phenotypic abnormalities associated to human genes is a key tool to stratify patients into disease subclasses that share a common biological or pathophisiological basis. Methods are being developed to predict the HPO terms that are associated for a given disease or disease gene, but most such methods adopt a simple ”flat” approach, that is they do not take into account the hierarchical relationships of the HPO, thus loosing important a priori information about HPO terms. In this contribution we propose a novel Hierarchical Top-Down (HTD) algorithm that associates a specific learner to each HPO term and then corrects the predictions according to the hierarchical structure of the underlying DAG. Genome-wide experimental results relative to a complex HPO DAG including more than 4000 HPO terms show that the proposed hierarchical-aware approach significantly improves predictions obtained with flat methods, especially in terms of precision/recall results.
    Third International Work-Conference on Bioinformatics and Biomedical Engineering - IWBBIO 2015, Granada, Spain; 04/2015
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