Phenotype based prediction of exome sequencing outcome using machine learning for neurodevelopmental disorders.
Genet Med
; 24(3): 645-653, 2022 03.
Article
em En
| MEDLINE
| ID: mdl-34906484
ABSTRACT
PURPOSE:
Although the introduction of exome sequencing (ES) has led to the diagnosis of a significant portion of patients with neurodevelopmental disorders (NDDs), the diagnostic yield in actual clinical practice has remained stable at approximately 30%. We hypothesized that improving the selection of patients to test on the basis of their phenotypic presentation will increase diagnostic yield and therefore reduce unnecessary genetic testing.METHODS:
We tested 4 machine learning methods and developed PredWES from these a statistical model predicting the probability of a positive ES result solely on the basis of the phenotype of the patient.RESULTS:
We first trained the tool on 1663 patients with NDDs and subsequently showed that diagnostic ES on the top 10% of patients with the highest probability of a positive ES result would provide a diagnostic yield of 56%, leading to a notable 114% increase. Inspection of our model revealed that for patients with NDDs, comorbid abnormal (lower) muscle tone and microcephaly positively correlated with a conclusive ES diagnosis, whereas autism was negatively associated with a molecular diagnosis.CONCLUSION:
In conclusion, PredWES allows prioritizing patients with NDDs eligible for diagnostic ES on the basis of their phenotypic presentation to increase the diagnostic yield, making a more efficient use of health care resources.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Exoma
/
Transtornos do Neurodesenvolvimento
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
/
Risk_factors_studies
Limite:
Humans
Idioma:
En
Revista:
Genet Med
Assunto da revista:
GENETICA MEDICA
Ano de publicação:
2022
Tipo de documento:
Article