SNP-based prediction of schizophrenia using machine learning

Motivation: Schizophrenia is a complex psychiatric disorder characterized by the deterioration of intellectual processes and emotional responses, affecting ∼0.32% of the global population. The study of single nucleotide polymorphisms (SNPs) associated with schizophrenia is crucial to identifying pathogenic genetic variants and understanding the genetic architecture of this complex disorder. This study aims to demonstrate the feasibility of predicting schizophrenia using an individual’s SNP profile. Results: We used genome-wide association (GWA) schizophrenia data from a case-control study across European-American (EA) and AfricanAmerican (AA) populations, consisting of 4693 participants (46.1% diagnosed with schizophrenia). Machine learning techniques were employed to construct SNP-based predictive models specific to ethnicity-gender groups (EA-F, EA-M, AA-F, and AA-M) where “F” and “M” identify female and male populations, respectively. Feature selection and association analysis were utilized to rank and detect significantly associated SNPs. Model selection was based on stratified five-fold cross-validation. Our EA-F-, EA-M-, AA-F-, and AA-M-specific models achieved classification accuracies (AUC, sensitivity, specificity) of 75.1% (69.2%, 95.4%, 34.2%), 65.4% (74.3%, 73.6%, 58.6%), 68.6% (69.5%, 85.6%, 41.1%), and 73.9% (74.0%, 39.7%, 93.3%), respectively, in independent test sets from the same ethnicity-gender population. The high sensitivity (>70%) of AA-F, EA-F, and EA-M models can make them auxiliary clinical tools to assess the risk of developing schizophrenia disorder in AA-F, EA-F, and EA-M populations.