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. 2021 Jan 8;49(D1):D1207-D1217.
doi: 10.1093/nar/gkaa1043.

The Human Phenotype Ontology in 2021

Sebastian Köhler  1   2 , Michael Gargano  2   3 , Nicolas Matentzoglu  2   4   5 , Leigh C Carmody  2   3 , David Lewis-Smith  6   7 , Nicole A Vasilevsky  2   8 , Daniel Danis, Ganna Balagura  9   10 , Gareth Baynam  11   12 , Amy M Brower  13 , Tiffany J Callahan  14 , Christopher G Chute  15 , Johanna L Est  16 , Peter D Galer  17   18 , Shiva Ganesan  17   18 , Matthias Griese  16   19 , Matthias Haimel  20   21 , Julia Pazmandi  20   21   22 , Marc Hanauer  23 , Nomi L Harris  2   24 , Michael J Hartnett  13 , Maximilian Hastreiter  16 , Fabian Hauck  16   25 , Yongqun He  26 , Tim Jeske  16 , Hugh Kearney  27 , Gerhard Kindle  28   29 , Christoph Klein  16 , Katrin Knoflach  16   19 , Roland Krause  30 , David Lagorce  23 , Julie A McMurry  2   31 , Jillian A Miller  13 , Monica C Munoz-Torres  2   31 , Rebecca L Peters  13 , Christina K Rapp  16   19 , Ana M Rath  23 , Shahmir A Rind  32   33 , Avi Z Rosenberg  34 , Michael M Segal  35 , Markus G Seidel  36 , Damian Smedley  37 , Tomer Talmy  38   39 , Yarlalu Thomas  40 , Samuel A Wiafe  41 , Julie Xian  17   42 , Zafer Yüksel  43 , Ingo Helbig  44   45 , Christopher J Mungall  2   24 , Melissa A Haendel  2   8   31 , Peter N Robinson  2   3   22
Affiliations

The Human Phenotype Ontology in 2021

Sebastian Köhler et al. Nucleic Acids Res. .

Abstract

The Human Phenotype Ontology (HPO, https://hpo.jax.org) was launched in 2008 to provide a comprehensive logical standard to describe and computationally analyze phenotypic abnormalities found in human disease. The HPO is now a worldwide standard for phenotype exchange. The HPO has grown steadily since its inception due to considerable contributions from clinical experts and researchers from a diverse range of disciplines. Here, we present recent major extensions of the HPO for neurology, nephrology, immunology, pulmonology, newborn screening, and other areas. For example, the seizure subontology now reflects the International League Against Epilepsy (ILAE) guidelines and these enhancements have already shown clinical validity. We present new efforts to harmonize computational definitions of phenotypic abnormalities across the HPO and multiple phenotype ontologies used for animal models of disease. These efforts will benefit software such as Exomiser by improving the accuracy and scope of cross-species phenotype matching. The computational modeling strategy used by the HPO to define disease entities and phenotypic features and distinguish between them is explained in detail.We also report on recent efforts to translate the HPO into indigenous languages. Finally, we summarize recent advances in the use of HPO in electronic health record systems.

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Figures

Figure 1.
Figure 1.
HPO terms organized by organ system. (A) Counts for top-level phenotype terms (direct descendants of Phenotypic abnormality (HP:0000118) are shown. Counts of terms added to the ontology after the previous article in this series (19) are shown in dark blue (added between 25 July 2018 and 18 August 2020). (B) Examples of new terms added 2018–2020 and their parent terms, for selected organ systems. (C) An example text definition and synonyms for a new term.
Figure 2.
Figure 2.
Annotations. Disease annotations using HPO terms organized by organ system. (A) Annotation counts for top-level phenotype terms (direct descendants of Phenotypic abnormality HP:0000118) are shown. Counts of annotations added to the ontology after the previous article in this series (19) are shown in dark blue (added between 25 July 2018 and 18 August 2020). Short forms are used to indicate the top level terms; for instance, ‘Ear’ indicates Abnormality of the ear (HP:0000598). (B) Example new annotation.
Figure 3.
Figure 3.
HPO-based analyses demonstrate the clinical features associated with diagnostic variants in SCN1A in published cohorts with developmental and epileptic encephalopathies of various known, or unknown but presumed genetic, etiologies. Fisher's exact test p-value for each term indicates the significance of the association between the HPO term and the presence of a diagnostic SCN1A variant in the cohort. (A) The frequency of HPO terms in SCN1A variant carriers versus non-carriers regardless of age. (B) The same data presented to demonstrate the conceptual relationships between associated features within the structure of the HPO. (A) and (B) modified from (24) with only a selection of terms labeled for legibility.
Figure 4.
Figure 4.
(A) The number of seizure terms applicable to the same clinical data from 82 individuals, and (B) the total information content of seizure terms of the same individuals according to the new and previous HPO seizure subontologies, where the information content of each term is equal to the negative logarithm of the proportion of individuals annotated with the term (Lewis-Smith et al., manuscript in preparation).
Figure 5.
Figure 5.
One major goal of KPMP is to refine classification of kidney diseases in molecular, cellular, and phenotypic terms and thereby identify novel targeted therapies. The kidney-related HPO terms are being used in multiple ways in KPMP. For example, KPMP has used the HPO terms for clinical and pathological phenotype annotations, integrative Kidney Tissue Atlas Ontology (KTAO) (39) development, and systematic data integration software development.
Figure 6.
Figure 6.
Proportions of terms defined in the HPO, the Mammalian Phenotype Ontology (MP) (48), the Drosophila Phenotype Ontology (DPO) (49), the Worm Phenotype Ontology (WPO) (50), the Xenopus Phenotype Ontology (XPO) (51) and the Zebrafish Phenotype Ontology (ZP) (52).
Figure 7.
Figure 7.
Analysis of time-stamped EHRs of children with epilepsy demonstrates the association of HPO terms with diagnostic SCN1A variants at different ages (modified from (23) with only a selection of terms labeled for legibility).
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References

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