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Combination of G72 Genetic Variation and G72 Protein Level to Detect Schizophrenia

Machine Learning Approaches

Bibliographic Data

ID15519655
AuthorsEugene Lin (0000-0001-6984-2159, China Medical University), Chieh‐Hsin Lin (0000-0001-6949-8968, Chang Gung University), Yi-Lun Lai (China Medical University), Chiung-Hsien Huang (China Medical University), Yu-Jhen Huang (0000-0003-0519-3350, China Medical University), Hsien‐Yuan Lane (0000-0003-2162-8174, China Medical University, corresponding author)
Year2018
Volume9
Pages566-566
Publication date2018-11-06
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueFrontiers in Psychiatry (JOURNAL)
Journal identifiersISSN: 1664-0640 • E-ISSN: 1664-0640
PublisherFrontiers Media (PUBLISHER • CH)
DOI10.3389/fpsyt.2018.00566
PMID30459659
OpenAlexW2900116592
LanguageEN
References cited3

The D-amino acid oxidase activator ( DAOA , also known as G72 ) gene is a strong schizophrenia susceptibility gene. Higher G72 protein levels have been implicated in patients with schizophrenia. The current study aimed to differentiate patients with schizophrenia from healthy individuals using G72 single nucleotide polymorphisms (SNPs) and G72 protein levels by leveraging computational artificial intelligence and machine learning tools. A total of 149 subjects with 89 patients with schizophrenia and 60 healthy controls were recruited. Two G72 genotypes (including rs1421292 and rs2391191) and G72 protein levels were measured with the peripheral blood. We utilized three machine learning algorithms (including logistic regression, naive Bayes, and C4.5 decision tree) to build the optimal predictive model for distinguishing schizophrenia patients from healthy controls. The naive Bayes model using two factors, including G72 rs1421292 and G72 protein, appeared to be the best model for disease susceptibility (sensitivity = 0.7969, specificity = 0.9372, area under the receiver operating characteristic curve (AUC) = 0.9356). However, a model integrating G72 rs1421292 only slightly increased the discriminative power than a model with G72 protein alone (sensitivity = 0.7941, specificity = 0.9503, AUC = 0.9324). Among the three models with G72 protein alone, the naive Bayes with G72 protein alone had the best specificity (0.9503), while logistic regression with G72 protein alone was the most sensitive (0.8765). The findings remained similar after adjusting for age and gender. This study suggests that G72 protein alone, without incorporating the two G72 SNPs, may have been suitable enough to identify schizophrenia patients. We also recommend applying both naive Bayes and logistic regression models for the best specificity and sensitivity, respectively. Larger-scale studies are warranted to confirm the findings

Biology · Gene · Genotype · Receiver operating characteristic · Single-nucleotide polymorphism · Amino Acid Enzymes and Metabolism · Diet and metabolism studies · Epigenetics and DNA Methylation · Medicine · Genetics · Internal Medicine

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