Genetic polymorphism and neuroanatomical changes in schizophrenia

Năstase, Mihai Gabriel; Vlaicu, Ilinca; Trifu, Simona Corina

doi:10.47162/rjme.63.2.03

Cited by 5 publications

(1 citation statement)

References 177 publications

(188 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Heredity plays an important role in the onset of mental disorders (4), and the heritability of schizophrenia has been estimated to be as high as 80% (5). Genetic information is expressed through genes, and single nucleotide polymorphisms (SNPS) are base-pair variations at speci c positions in the genome(6).…”

Section: Introductionmentioning

confidence: 99%

Identification of a predictive model for schizophrenia based on SNPs in a Chinese population

杨,

姚明,

徐

et al. 2024

Preprint

View full text Add to dashboard Cite

Background Schizophrenia is a devastating mental disease with high heritability. A growing number of susceptibility genes associated with schizophrenia, as well as their corresponding SNPs loci, have been revealed by genome-wide association studies. However, using SNPs as predictors of disease and diagnosis remains difficult. Here, we aimed to uncover susceptibility SNPs in a Chinese population and to construct a prediction model for schizophrenia. Methods A total of 210 participants, including 70 patients with schizophrenia, 70 patients with bipolar disorder, and 70 healthy controls, were enrolled in this study. We estimated 14 SNPs using published risk loci of schizophrenia, and used these SNPs to build a model for predicting schizophrenia via comparison of genotype frequencies and logistic regression. We evaluated the efficacy of the diagnostic model in schizophrenia and control patients using ROC curves and then used the 70 patients with bipolar disorder to evaluate the model’s differential diagnostic efficacy. Results 5 SNPs were selected to construct the model: rs148415900, rs71428218, rs4666990, rs112222723 and rs1716180. Correlation analysis results suggested that, compared with the risk SNP of 0, the risk SNP of 3 was associated with an increased risk of schizophrenia (OR = 13.00, 95%CI:2.35–71.84, P = 0.003). The ROC-AUC of this prediction model for schizophrenia was 0.719 (95% CI: 0.634–0.804), with the greatest sensitivity and specificity being 60% and 80%, respectively. The ROC-AUC of the model in distinguishing between schizophrenia and bipolar disorder was 0.591 (95%CI: 0.497–0.686), with the greatest sensitivity and specificity being 60% and 55.7%, respectively. Conclusions The SNP risk score prediction model had good performance in predicting schizophrenia. To the best of our knowledge, previous studies have not applied SNP-based models to differentiate between cases of schizophrenia and other mental illnesses. It could have several potential clinical applications, including shaping disease diagnosis, treatment, and outcomes.

show abstract

Section: Introductionmentioning

confidence: 99%