  Subtyping neuropsychiatric disorders like schizophrenia is essential for
improving the diagnosis and treatment of complex diseases. Subtyping
schizophrenia is challenging because it is polygenic and genetically
heterogeneous, rendering the standard symptom-based diagnosis often unreliable
and unrepeatable. We developed a novel network-based machine-learning approach,
netMoST, to subtyping psychiatric disorders. NetMoST identifies polygenic risk
SNP-allele modules from genome-wide genotyping data as polygenic haplotype
biomarkers (PHBs) for disease subtyping. We applied netMoST to subtype a cohort
of schizophrenia subjects into three distinct biotypes with differentiable
genetic, neuroimaging and functional characteristics. The PHBs of the first
biotype (36.9% of all patients) were related to neurodevelopment and cognition,
the PHBs of the second biotype (28.4%) were enriched for neuroimmune functions,
and the PHBs of the third biotype (34.7%) were associated with the transport of
calcium ions and neurotransmitters. Neuroimaging patterns provided additional
support to the new biotypes, with unique regional homogeneity (ReHo) patterns
observed in the brains of each biotype compared with healthy controls. Our
findings demonstrated netMoST's capability for uncovering novel biotypes of
complex diseases such as schizophrenia. The results also showed the power of
exploring polygenic allelic patterns that transcend the conventional GWAS
approaches.
