Imaging Genetics Towards a Refined Diagnosis of Schizophrenia - Jiang, Wenhao; King, Tricia Z.; Turner, Jessica A. (Frontiers in Psychiatry, 2019)
Current diagnoses of schizophrenia and related psychiatric disorders are classified by phenomenological principles and clinical descriptions while ruling out other symptoms and conditions. Specific biomarkers are needed to assist the current diagnostic system. However, complicated gene and environmental factor interactions play an important role in the disease onset and induce great heterogeneity. This unclear etiology and heterogeneity raise the difficulty to induce the common biomarkers to distinguish schizophrenia from the healthy population. Simultaneously, the vast overlap in common symptoms, genetic variations and brain alterations in schizophrenia and related psychiatric disorders indicate the extension of biomarkers only achieved in schizophrenia. This complex situation compels the development of biomarkers based on research findings not only in schizophrenia but also with related disorders that includes behavior as well as imaging genetics. Imaging genetics is a unique methodology to assess the impact of genetic factors on both brain structure and function. More importantly, imaging genetics builds a bridge to understand the behavioral and clinical implications of genetics and neuroimaging. By characterizing and quantifying the brain affected in psychiatric disorders, imaging genetics is contributing to present potential biomarkers for schizophrenia and related disorders. To date, imaging genetics research includes candidate gene analysis, genome-wide association studies, polygenetic risk score analysis, and large scale collaborative studies that have made contributions to the understanding of schizophrenia with the potential to serve as biomarkers. Though limitations such as failing to capture enough genetic information and methodological arguments regarding imaging genetics itself may delay its progress, imaging genetics remains a promising as more aggregative, clustering methods and imaging genetics-compatible clinical assessments are employed in future studies.