SNP-based prediction of schizophrenia using machine learning

Ramazanova Z. Matkarimov B. Nabavi S. Crape B. Zollanvari A.
2026 Oxford University Press

Bioinformatics Advances
2026 #6 Issue 1

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.

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Department of Electrical and Computer Engineering, School of Engineering and Digital Sciences, Nazarbayev University, 53 Kabanbay Batyr Avenue, Astana, 010000, Kazakhstan
Center for Life Sciences, National Laboratory Astana, Nazarbayev University, 53 Kabanbay Batyr Avenue, Astana, 010000, Kazakhstan
Department of Artificial Intelligence Technologies, L.N. Gumilyov Eurasian National University, 2 K. Satbayev street,Astana, 010008, Kazakhstan
Department of Computer Science and Engineering, University of Connecticut, Storrs, 06268, CT, United States
Department of Biomedical Sciences, School of Medicine, Nazarbayev University, Astana, 020000, Kazakhstan
Department of Electrical and Computer Engineering, Utah Valley University, Orem, 84058, UT, United States

Department of Electrical and Computer Engineering
Center for Life Sciences
Department of Artificial Intelligence Technologies
Department of Computer Science and Engineering
Department of Biomedical Sciences
Department of Electrical and Computer Engineering

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