Schizophrenia is a complex disease predisposed by genes, environment and their interaction. Its diagnosis is mainly based on clinic observations with its treatment following a trial-and-error manner. Both linkage studies and following association studies have identified some genes and genomic regions which gain insight into pathophysiological foundation of the disease. However, low replication rate of detected variation between/within different populations prevented these findings from clinical application in the diagnosis and the precise treatment of disease. With introduction of endophenotypes and extended endpphenotypes, multiple disease-related traits such as neurocognitive deficits and neuroimaging alterations enable a further refinement of phenotypes used in genetic and genomic studies of schizophrenia. Here, the authors discuss the several endophenotypes emerging from their previous studies and the methods which could incorporate both the dichotomous variable of diagnosis and quantitative traits into genetic/genomic studies of schizophrenia. Furthermore, the authors demonstrated some alternative methodologies utilizing the big data generated from recent multi-sites cohort studies.