Leveraging genome-wide association and clinical data in revealing schizophrenia subgroups.

Schizophrenia (SCZ) has long been recognized as a highly heterogeneous disorder. Patients differed in their clinical manifestations, prognosis, and underlying pathophysiologies. Here we presented and applied a framework for finding subtypes of SCZ utilizing genome-wide association study (GWAS) and clinical data. We postulated that genetic information may help stratify patient into useful subgroups, and incorporation of other clinical information and cognitive profiles will further improve patient subtyping. We conducted cluster analysis in 387 Hong Kong Chinese with SCZ. First we performed 'single-view' clustering using genetic or clinical data alone, then proceeded to 'multi-view' clustering (MVC) accounting for both types of information. We validated clustering results by assessing subgroup differences in various outcomes. We found significant differences in outcomes including treatment response, disease course and symptom severity (Simes overall p-value using MVC = 1.64E-9). Overall speaking, we identified three subgroups with good, intermediate and poor prognosis respectively. MVC generally out-performed single-view methods. The analysis was repeated for different sets of input SNPs, and stratified analysis of male and female patients, and the results remained largely robust. We also found significant enrichment for SCZ loci among the SNPs selected by the cluster algorithm. Numerous selected genes (e.g. NRG1, ERBB4, NRXN1, ANK3) and pathways (e.g. neuregulin-ErbB4 and calcium signaling) were implicated in SCZ or related pathophysiological processes. This is first study to combine both genetic and clinical data for subtyping SCZ, and to employ genome-wide SNP data in cluster analysis of a complex disease. This work points to a new way of GWAS analysis of translational potential.