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Feb 2024

The Evolving Human Phenotype Ontology: Revolutionising Clinical Diagnostics With AION

Welcome to another blog post!

The Human Phenotype Ontology (HPO) is a standardised, structured vocabulary of terms associated with phenotypic abnormalities and clinical features in human diseases1. It provides a comprehensive collection of observable traits and characteristics associated with disease that facilitates effective analysis and interpretation. Here, we will delve into the significance of HPO terms for genomic research and clinical diagnostics and explore the importance of tools like AION that enable users to update patient phenotypes to align with clinical examinations, facilitating deep phenotyping and improving diagnostic accuracy and yield.

The Evolution of The Human Phenotype Ontology

HPO was introduced in a 2008 publication, where the authors outlined it as a tool to phenotypically describe human diseases through a structure that supports computational reasoning to assess phenotype similarities across patients and diseases2. Since then, a strong collaboration formed to expand the HPO with more phenotype annotations and provide it to the community3. As such, the Monarch Initiative emerged as an ecosystem of tools aimed at integrating biological information with computational reasoning for cross-species phenotype comparison, benefiting clinicians, researchers, and patients globally4

The initial iteration of the HPO contained approximately 8,000 terms, which has now been expanded to include over 17,000. The main components of the HPO are the terms describing phenotypic abnormalities, phenotype modifiers (clinical modifiers, frequency), and other contextual terms to describe patients and diseases. Through these components, patients and diseases are being described in increasing detail, and more and more tools are supporting deep phenotyping, integrating clinical data into translational research, rare disease diagnostics, and precision medicine5.

Understanding the Significance of the Human Phenotype Ontology in Genomic Research and Clinical Diagnostics

HPO has emerged as a significant tool for structuring and standardising medically relevant disease-phenotype information and annotations and accurately describing patients. Its benefits are driving its ever-increasing use across research and clinical settings:

  • Standardisation and Precision in Describing Phenotypic Abnormalities: The HPO provides a common language, enabling precise and consistent descriptions of phenotypic traits across different studies and clinical cases. It has now been translated into ten languages (seven comprehensive and three partial translations) thanks to translation efforts published in the HPO International Edition (HPOIE), providing international standardisation of phenotypic terms crucial for consistent clinical diagnosis worldwide6.

  • Improved Disease Diagnosis and Classification: By comparing patient phenotypes with known HPO profiles associated with different disorders, clinicians can more accurately and rapidly diagnose and classify diseases, particularly rare, complex and challenging-to-diagnose diseases7. Thanks to the computational reasoning that the HPO supports, phenotypic profile comparisons can be done with methods superior to filtering.

  • Enhanced Genotype-Phenotype Correlations: The HPO enables researchers to accurately link genetic mutations to the phenotypes they produce, enhancing understanding of the genetic basis of diseases, which can be used across clinical diagnostics, translational research, and genomics.

  • Data Integration and Computational Analysis: HPO's structured nature allows for integration with other bioinformatics databases and tools for more accurate variant interpretation and decision support.

Bioinformatic Tools and The Human Phenotype Ontology

In order to interpret the results of whole genome sequencing (WGS) or whole exome sequencing (WES), it is important to integrate results with robust information on clinical phenotypes8. Nostos Genomics has streamlined this process by integrating HPO terms into their automated genetic variant interpretation solution, AION. AION harnesses a machine-learning model trained on high-quality genetic variant data to assist in variant interpretation for the diagnosis of rare genetic diseases. AION accelerates genomic data interpretation by enabling clinicians to solve clinical cases in minutes, providing comprehensive insights into the classification process for accurate and reliable decision support. 

AION enhances genetic disease diagnosis through HPO overlap identification, facilitating the identification of likely genetic disorders associated with patient variants and clinical phenotypes. Users have the ability to edit patient symptoms in the clinical information of a case, which triggers a new AION analysis with the updated HPO list. Importantly, AION provides full traceability on when and by whom a case was edited. The integration of accurate HPO terms relevant to specific pathologies facilitates highly accurate and rapid insight-driven diagnostics. For example, AION’s phenotype overlap highlight facilitated the quick diagnosis of a newborn patient affected by a congenital gastrointestinal disorder, achieved by the identification of pathogenic variants in the SLC9A3 gene through phenotype similarity9. Rapid and accurate diagnosis is particularly important in neonatal cases like this, where accelerated treatment and patient management decisions are critical10

In addition, the capacity to continuously update the HPO terms assigned to a particular case enables deep phenotyping, which refers to the precise, comprehensive analysis of disease-relevant phenotypic abnormalities and is particularly useful for the diagnosis of rare and complex diseases7. By exploring the phenotypes associated with a potential diagnosis ranked by AION, clinicians can carry out further clinical examinations to check for additional, often subtle phenotypic features. This allows for the identification of unique phenotypic patterns and biomarkers that can distinguish between closely related disorders, improving diagnostic accuracy and yield. Moreover, by capturing the full complexity of an individual’s phenotype, deep phenotyping supports the development of targeted treatment plans11.

Conclusions and Future Perspectives

In summary, the HPO plays a pivotal role in genomic research and clinical diagnostics. Its significance lies in enabling precise, consistent phenotype descriptions and improving genotype-phenotype correlations. We gratefully acknowledge the Monarch Initiative and the HPO collaboration for generating this indispensable, freely available resource.

At Nostos Genomics, we believe it is essential that, as advanced sequencing technologies continue to facilitate more accurate, accessible genome sequencing capabilities, researchers and clinicians continue to use HPOs wherever possible, working towards global consistency and standardisation in descriptions of phenotypic traits and clinically-relevant abnormalities.

To learn more about how Nostos Genomics and our AI-driven variant interpretation platform, AION, can support deep phenotyping for more precise clinical diagnostics, book a free demo with one of our genomics experts.


1. Human Phenotype Ontology. Accessed January 26, 2024.

2. Robinson PN, Köhler S, Bauer S, Seelow D, Horn D, Mundlos S. The Human Phenotype Ontology: A Tool for Annotating and Analyzing Human Hereditary Disease. Am J Hum Genet. 2008;83(5):610-615. doi:10.1016/j.ajhg.2008.09.017

3. Washington NL, Haendel MA, Mungall CJ, Ashburner M, Westerfield M, Lewis SE. Linking Human Diseases to Animal Models Using Ontology-Based Phenotype Annotation. Buetow KH, ed. PLoS Biol. 2009;7(11):e1000247. doi:10.1371/journal.pbio.1000247

4. About | Monarch Initiative. Accessed January 26, 2024.

5. Köhler S, Vasilevsky NA, Engelstad M, et al. The Human Phenotype Ontology in 2017. Nucleic Acids Res. 2017;45(D1):D865-D876. doi:10.1093/nar/gkw1039

6. Gargano MA, Matentzoglu N, Coleman B, et al. The Human Phenotype Ontology in 2024: phenotypes around the world. Nucleic Acids Res. 2024;52(D1):D1333-D1346. doi:10.1093/nar/gkad1005

7. Robinson PN. Deep phenotyping for precision medicine. Hum Mutat. 2012;33(5):777-780. doi:10.1002/humu.22080

8. Tchuisseu-Kwangoua LA, Kamtchum-Tatuene J, Tekendo-Ngongang C, Pengelly RJ, Self J. Bridging the language gap - A call for the wider use of Human phenotype ontology by non-geneticist clinicians when requesting genomic tests. Eur J Med Genet. 2023;66(2):104679. doi:10.1016/j.ejmg.2022.104679

9. Accurate phenotyping and HPO overlap visualization in AION. Accessed February 1, 2024.

10. Reiley J, Botas P, Miller CE, et al. Open-Source Artificial Intelligence System Supports Diagnosis of Mendelian Diseases in Acutely Ill Infants. Children. 2023;10(6):991. doi:10.3390/children10060991

11. Seyhan AA, Carini C. Are innovation and new technologies in precision medicine paving a new era in patients centric care? J Transl Med. 2019;17(1):114. doi:10.1186/s12967-019-1864-9

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