Glucose-6-phosphate dehydrogenase (G6PD) deficiency, an X-linked inherited disease, is one of the most common enzymopathies and affects over 400 million people worldwide. In China at least 21 distinct point mutations have been identified so far. In this study high-resolution melting (HRM) analysis was used to screen for G6PD mutations in 260 unrelated Han Chinese individuals, and the rapidity and reliability of this method was investigated. The mutants were readily differentiated by using HRM analysis, which produced distinct melting curves for each tested mutation. Interestingly, G1388A and G1376T, the two most common variants accounting for 50% to 60% of G6PD deficiency mutations in the Chinese population, could be differentiated in a single reaction. Further, two G6PD mutations not previously reported in the Chinese population were identified in this study. One of these mutations, designated "G6PD Jiangxi G1340T," involved a G1340T substitution in exon 11, predicting a Gly447Val change in the protein. The other mutation involved a C406T substitution in exon 5. The frequencies of the common polymorphism site C1311T/IVS (intervening sequence) XI t93c between patients with G6PD and healthy volunteers were not significantly different. Thus, HRM analysis will be a useful alternative for screening G6PD mutations.

Phenylketonuria (PKU) is a common genetic metabolic disorder that affects the infant's nerve development and manifests as abnormal behavior and developmental delay as the child grows. Currently, a triple-quadrupole mass spectrometer (TQ-MS) is a common high-accuracy clinical PKU screening method. However, there is high false-positive rate associated with this modality, and its reduction can provide a diagnostic and economic benefit to both pediatric patients and health providers. Machine learning methods have the advantage of utilizing high-dimensional and complex features, which can be obtained from the patient's metabolic patterns and interrogated for clinically relevant knowledge. In this study, using TQ-MS screening data of more than 600,000 patients collected at the Newborn Screening Center of Shanghai Children's Hospital, we derived a dataset containing 256 PKU-suspected cases. We then developed a machine learning logistic regression analysis model with the aim to minimize false-positive rates in the results of the initial PKU test. The model attained a 95-100% sensitivity, the specificity was improved 53.14%, and positive predictive value increased from 19.14 to 32.16%. Our study shows that machine learning models may be used as a pediatric diagnosis aid tool to reduce the number of suspected cases and to help eliminate patient recall. Our study can serve as a future reference for the selection and evaluation of computational screening methods.

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