Computational analysis of neurodevelopmental phenotypes: Harmonization empowers clinical discovery.
Hum Mutat
; 43(11): 1642-1658, 2022 11.
Article
em En
| MEDLINE
| ID: mdl-35460582
ABSTRACT
Making a specific diagnosis in neurodevelopmental disorders is traditionally based on recognizing clinical features of a distinct syndrome, which guides testing of its possible genetic etiologies. Scalable frameworks for genomic diagnostics, however, have struggled to integrate meaningful measurements of clinical phenotypic features. While standardization has enabled generation and interpretation of genomic data for clinical diagnostics at unprecedented scale, making the equivalent breakthrough for clinical data has proven challenging. However, increasingly clinical features are being recorded using controlled dictionaries with machine readable formats such as the Human Phenotype Ontology (HPO), which greatly facilitates their use in the diagnostic space. Improving the tractability of large-scale clinical information will present new opportunities to inform genomic research and diagnostics from a clinical perspective. Here, we describe novel approaches for computational phenotyping to harmonize clinical features, improve data translation through revising domain-specific dictionaries, quantify phenotypic features, and determine clinical relatedness. We demonstrate how these concepts can be applied to longitudinal phenotypic information, which represents a critical element of developmental disorders and pediatric conditions. Finally, we expand our discussion to clinical data derived from electronic medical records, a largely untapped resource of deep clinical information with distinct strengths and weaknesses.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Genômica
/
Registros Eletrônicos de Saúde
Tipo de estudo:
Qualitative_research
Limite:
Child
/
Humans
Idioma:
En
Revista:
Hum Mutat
Assunto da revista:
GENETICA MEDICA
Ano de publicação:
2022
Tipo de documento:
Article
País de afiliação:
Reino Unido