Multi-omics integration & psychiatric disorders
The integration of multiple omic and clinical data has become possible through the use of multi-omics integration methods and machine learning methods. Despite challenges in developing accurate computational models and software tools for clinical implementation, the use of multi-omics data in the clinical setting offers promising opportunities for accurate diagnosis and targeted therapy. A recently published review outlines the current state of psychiatric multi-omics research and its clinical application. It also addresses the methods, desirable features of decision support tools, and opportunities and pitfalls of multi-omics research in psychiatry.
The study of psychiatric disorders has evolved with advances in technology and large-scale omics data collection, leading to an era of Big Data in psychiatry. International consortia and research groups are collecting and analyzing large-scale genome-wide association studies (GWAS) for various psychiatric disorders. These GWAS are methods that reveal associations between genetic regions (loci) and traits, including diseases, without prior hypotheses. It is well known that genetic variations between individuals can lead to differences in their physical characteristics (phenotypes). This has led to the identification of risk loci for diseases such as schizophrenia, major depressive disorder, and bipolar disorder. Multi-Omics help us understand genetic differences and risk loci.
The integration of multiple omics (MO) and clinical data has become feasible with the use of multi-omics integration methodologies and machine learning methods. Despite challenges in developing accurate computational models and software tools for clinical implementation, the use of multi-omics data in the clinical setting offers promising opportunities for accurate diagnosis and targeted therapy. A recently published review outlines the current state of psychiatric multi-omics research and its clinical application. It also addresses the methods, desirable features of decision support tools, and opportunities and pitfalls of multi-omics research in psychiatry. The article is divided into four parts, including:
an overview of multi-omics integration methodologies,
MO research using human and animal model systems
the desirable features of multi-omics-based decision-making software tools,
and the opportunities and difficulties of MO research in psychiatry.
Multi-omics in psychiatric disorders
The integration of multi-omics data is a key method for the study of mental illness and health. This involves analyzing different types of data (e.g., genomics, transcriptomics, proteomics, and clinical data) in a holistic manner. The goal is to gain a better understanding of the underlying mechanisms of mental illness and health. The integration of MO data is being used to discover biomarkers for diagnosis, treatment, and prognosis and to develop new methods for predicting response to treatment. The application of multi-omics has the potential to improve the accuracy of psychiatric diagnoses and also the effectiveness of treatments. It does this by taking into account the complex interplay between different biological systems. However, translating multi-omics research into clinical practice remains a challenge and is an active area of research.
There are two main approaches to multi-omics integration: multilevel and metadimensional. Using multi-omics data in model systems and combining them with clinical data in a clinical setting can also lead to better diagnosis and treatment of psychiatric disorders. However, to fully realize the potential of multi-omics integration in psychiatry, challenges such as clinical heterogeneity and limited knowledge of biological mechanisms still need to be overcome.
If you want to learn more about the methods, opportunities, and hurdles, don't miss this review!