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. 2021 Jun;62(6):1293-1305.
doi: 10.1111/epi.16908. Epub 2021 May 5.

Modeling seizures in the Human Phenotype Ontology according to contemporary ILAE concepts makes big phenotypic data tractable

David Lewis-Smith  1   2 , Peter D Galer  3   4   5   6 , Ganna Balagura  7 , Hugh Kearney  8   9 , Shiva Ganesan  3   4   5 , Mahgenn Cosico  3   4 , Margaret O'Brien  3   4 , Priya Vaidiswaran  3   4 , Roland Krause  10 , Colin A Ellis  4   6 , Rhys H Thomas  1   2 , Peter N Robinson  11   12 , Ingo Helbig  3   4   5   6
Affiliations

Modeling seizures in the Human Phenotype Ontology according to contemporary ILAE concepts makes big phenotypic data tractable

David Lewis-Smith et al. Epilepsia. 2021 Jun.

Abstract

Objective: The clinical features of epilepsy determine how it is defined, which in turn guides management. Therefore, consideration of the fundamental clinical entities that comprise an epilepsy is essential in the study of causes, trajectories, and treatment responses. The Human Phenotype Ontology (HPO) is used widely in clinical and research genetics for concise communication and modeling of clinical features, allowing extracted data to be harmonized using logical inference. We sought to redesign the HPO seizure subontology to improve its consistency with current epileptological concepts, supporting the use of large clinical data sets in high-throughput clinical and research genomics.

Methods: We created a new HPO seizure subontology based on the 2017 International League Against Epilepsy (ILAE) Operational Classification of Seizure Types, and integrated concepts of status epilepticus, febrile, reflex, and neonatal seizures at different levels of detail. We compared the HPO seizure subontology prior to, and following, our revision, according to the information that could be inferred about the seizures of 791 individuals from three independent cohorts: 2 previously published and 150 newly recruited individuals. Each cohort's data were provided in a different format and harmonized using the two versions of the HPO.

Results: The new seizure subontology increased the number of descriptive concepts for seizures 5-fold. The number of seizure descriptors that could be annotated to the cohort increased by 40% and the total amount of information about individuals' seizures increased by 38%. The most important qualitative difference was the relationship of focal to bilateral tonic-clonic seizure to generalized-onset and focal-onset seizures.

Significance: We have generated a detailed contemporary conceptual map for harmonization of clinical seizure data, implemented in the official 2020-12-07 HPO release and freely available at hpo.jax.org. This will help to overcome the phenotypic bottleneck in genomics, facilitate reuse of valuable data, and ultimately improve diagnostics and precision treatment of the epilepsies.

Keywords: big data; classification; epilepsy; genetics.

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Conflict of interest statement

CONFLICTS OF INTEREST

H.K. has received speaking honoraria from Biogen, Roche, and Teva, and was funded by Congenica. R.H.T. has received honoraria from Arvelle, Eisai, GW Pharma, Sanofi, UCB Pharma, and Zogenix, and meeting support from Bial, LivaNova, Novartis, and UCB Pharma. I.H. has served as a paid consultant for Biogen. D.L.-S., P.D.G., G.B., S.G., M.C., M.O., P.V., C.A.E., R.K., and P.N.R. have no conflicts of interest to disclose. We confirm that we have read the Journal’s position on issues involved in ethical publication and affirm that this report is consistent with those guidelines.

Figures

FIGURE 1
FIGURE 1
(A) The Human Phenotype Ontology (HPO) can harmonize phenotypic data from large cohorts to identify phenotypic associations with a particular genetic (or other) categorical factor, in this case de novo variants in SCN1A. Data from Galer et al. 2020; p-values and odds ratio obtained using Fisher’s exact test. (B) Once the set of HPO terms associated with this form of epilepsy is known, the clinical features of patients can be translated into HPO terms to assess how closely they match phenotypically. GTCS, generalized tonic-clonic seizure; PSW, polyspike-wave; sz, seizure
FIGURE 2
FIGURE 2
(A) The increase in the number of seizure concepts by which data collected from 791 individuals can be described as a result of automated inference using the Human Phenotype Ontology (HPO) version release 2017–12-12. Green lines indicate an increase in the number of seizure descriptors annotated by inference after translation. The blue line indicates eight individuals whose only HPO term translated from input data was Seizure (HP:0001250), which is insufficient to infer descriptions. (B) Comparison of the three independent cohorts according to the percentage of individuals annotated with each of 16 HPO seizure terms, comprising the 10 most common seizure concepts in each cohort. Note that in this HPO version, Generalized tonic-clonic seizure (HP:0002069) is indifferent to onset. p-values are two-sided from Fisher’s exact test with Holm-Bonferroni adjustment for 16 comparisons; 95% confidence intervals were calculated from the Poisson distribution
FIGURE 3
FIGURE 3
Comparison of the phenotypic information encoded from the same data from 791 individuals using the two seizure subontologies. (A) Green lines indicate an increase, red lines a decrease, and blue lines no difference in the number of seizure descriptors annotated after inference. (B) The total amount of information encoded about each individual’s seizure types according to each Human Phenotype Ontology (HPO) seizure subontology. Numbers of individuals shown correspond to those falling within 2 bit by 2 bit bins, the dashed gray line indicates equality, and the regression line is shown in purple
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