Phenotype driven molecular genetic test recommendation for diagnosing pediatric rare disorders

• Published in: NPJ digital medicine Vol. 7; no. 1; pp. 333 - 12
• Main Authors: Chen, Fangyi, Ahimaz, Priyanka, Nguyen, Quan M., Lewis, Rachel, Chung, Wendy K., Ta, Casey N., Szigety, Katherine M., Sheppard, Sarah E., Campbell, Ian M., Wang, Kai, Weng, Chunhua, Liu, Cong
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 21.11.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 692/700/139/1512; 692/700/1720/3187; Biomedicine; Biotechnology; Demographics; Electronic health records; Genetic disorders; Genetic testing; Genetics; Genomes; Genotype & phenotype; Hospitals; Machine learning; Medical diagnosis; Medicine; Medicine & Public Health; Metabolism; Nervous system; Patients; Pediatrics; Rare diseases; Whole genome sequencing
• ISSN: 2398-6352; 2398-6352
• DOI: 10.1038/s41746-024-01331-1

Patients with rare diseases often experience prolonged diagnostic delays. Ordering appropriate genetic tests is crucial yet challenging, especially for general pediatricians without genetic expertise. Recent American College of Medical Genetics (ACMG) guidelines embrace early use of exome sequencing (ES) or genome sequencing (GS) for conditions like congenital anomalies or developmental delays while still recommend gene panels for patients exhibiting strong manifestations of a specific disease. Recognizing the difficulty in navigating these options, we developed a machine learning model trained on 1005 patient records from Columbia University Irving Medical Center to recommend appropriate genetic tests based on the phenotype information. The model achieved a remarkable performance with an AUROC of 0.823 and AUPRC of 0.918, aligning closely with decisions made by genetic specialists, and demonstrated strong generalizability (AUROC:0.77, AUPRC: 0.816) in an external cohort, indicating its potential value for general pediatricians to expedite rare disease diagnosis by enhancing genetic test ordering.