Research Design: This study is a multi-center cooperative study of the South China Neonatal
Genetic Screening Alliance. The principal investigator ( PI ) and project leader of this
study are Hao Hu, chief physician of pediatrics of the Sixth Affiliated Hospital of Sun
Yat-sen University, who plans to include 123 cooperative units of the South China Neonatal
Genetic Screening Alliance. In this study, 40,000 neonatal genetic screening data and MS / MS
data were retrospectively analyzed through multi-center cooperation. The collection date was
from January 2019 to August 2022.
Through the statistical analysis of neonatal genetic screening data ( 138 genes related to
133 common genetic diseases ), the incidence of common genetic diseases in newborns in China,
the carrying rate of pathogenic variation and the high-frequency variation sites of the
population were clarified, and the epidemiological characteristics of newborns in China were
studied.
Through the statistical analysis of neonatal genetic screening data and MS / MS metabolomics
data ( 11 amino acids and 28 acylcarnitines ), the correlation between gene and metabolism
will be explored, and the pathogenicity of high-frequency VUS mutation sites will be
identified by using protein function artificial intelligence analysis platform and tandem
mass spectrometry metabolite data.
The prediction model of common genetic diseases is constructed by using machine learning
algorithms such as random forest, support vector machine, elastic network and multi-layer
perceptron, so as to realize the accurate diagnosis of common genetic diseases through tandem
mass spectrometry metabolomics data, and expand 2-3 kinds of diseases that can be detected by
MS / MS technology.
Sample size: This study plans to collect genetic screening data ( 138 genes related to 133
common genetic diseases ) and tandem mass spectrometry metabolomics data ( 11 amino acids and
28 acylcarnitines ) of about 40,000 newborns from January 2019 to August 2022 in 123
cooperative units of the South China Neonatal Genetic Screening Alliance.
Data source: The gene sequencing data and MS / MS metabolic data of 40,000 newborns were from
123 cooperative units of the South China Neonatal Gene Screening Alliance.In this study, the
data table established by Microsoft Excel was used. The neonatal gene data and tandem mass
spectrometry of the multi-center cooperative units were transmitted through the Excel data
table. Effective measures will be taken to strictly record, clean and check the data.
Multi-centers ensure the authenticity, accuracy and completeness of the neonatal gene
sequencing data and MS / MS metabolic data provided, and all data and test reports can be
traced. In addition, the data management, pay attention to the confidentiality of the data,
to ensure the privacy of patients and their families.
Informed consent: This study is a retrospective study. Subjects have signed informed consent
from parents or guardians when doing neonatal MS / MS metabolic disease screening or genetic
screening. The informed consent form clearly states that the test data can be used for
scientific research after removing personal privacy information. Therefore, the application
for exemption from informed consent.
Benefits of participating in research: There is no direct economic benefit for all the test
subjects included in this study, but for the positive children included in this study, free
first-generation sequencing verification is provided, and professional genetic counseling and
clinical treatment advice are provided to parents by pediatric clinicians with the consent of
the parents of the children.
Privacy protection measures: All the data of the subjects during the study period will be
entered into the computer for confidential storage and analysis. If necessary, the relevant
institutions may review the records to confirm the authenticity, accuracy and integrity of
the data. The data obtained from the study may also be published in academic journals, but
the names of the subjects will not be published, and the privacy of the subjects will be kept
confidential.
All selected populations do not involve special populations, and patient privacy information
is strictly protected.