Multi-omics approaches to major psychiatric disorders

Abstract

In recent years, major psychiatric disorders have been intensively researched. Studies have investigated the pathophysiology of these disorders in detail and at various molecular levels with several omics techniques, including genomics, epigenomics, transcriptomics, proteomics, and metabolomics. However, although the results of a single omics study can help shed light on some of the unclear aspects of the biological circuits involved in the pathophysiology of major psychiatric disorders, the complexity of the biological mechanisms underlying these conditions makes it necessary to consider multiple types of omics data and multiple levels of analysis, including various conceptional, methodological, and quality control criteria. Currently, dealing with high-dimensional data and sparse heterogeneous data structures remains one of the biggest challenges to integrating data from multi-omics approaches. The hope is that eventually the development and application of methods to integrate biological and phenotypic data through multi-omics and machine learning-based algorithms may allow early diagnosis of major psychiatric disorders, perhaps even before disease onset, and enable accurate, personalized treatment. In this mini-review, we summarized the main findings of the field by reviewing systematic reviews, meta-analyses, and narrative reviews on the major psychiatric disorders schizophrenia, bipolar disorder, and major depressive disorder.