Phenotype based prediction of exome sequencing outcome using machine learning for neurodevelopmental disorders

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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.