Limitations and opportunities in multi-omics integration for neurodevelopmental, neurodegenerative and psychiatric disorders: A systematic review

Clinical Snapshot

Key Findings

• Effect Size: Not applicable (descriptive review)

• Primary Outcome: Current state of multi-omics integration in neurological research

• Nnt Or Sensitivity: 156 studies included; most common pairings: epigenomics-transcriptomics and metagenomics-metabolomics

Clinical Application

Limited direct clinical applicability; primarily identifies research infrastructure needs and computational challenges Relevant for Australian research institutions and clinical research networks; highlights need for collaborative frameworks that could align with NHMRC research priorities Researchers and clinicians working with neurodevelopmental, neurodegenerative, and psychiatric disorders

Abstract

Recent advances in high-throughput technologies have led to an increased generation of biological data across genomics, transcriptomics, proteomics, epigenomics, and metabolomics. However, a major challenge remains: effectively integrating these multi-omics datasets to allow a more holistic understanding of the complex, interconnected mechanisms underlying human diseases. Neurodevelopmental, neurodegenerative, and psychiatric disorders are particularly multifactorial and heterogeneous, making them candidates for multi-omics approaches. In this context, this systematic review assesses the current state of multi-omics integration in neurological research. Records retrieved from five major databases were processed, and 156 studies were included for further analysis. The most frequently studied conditions were Alzheimer's Disease, Depressive Disorder and Parkinson's Disease, with epigenomics-transcriptomics and metagenomics-metabolomics emerging as the most common omics pairings. The field remains dominated by studies integrating pairs of omics layers. Only a limited number of computational tools are currently being applied to the integration of more than two omics layers, highlighting a gap in comprehensive multi-omics modeling. Despite progress, key challenges persist, including data accessibility and the need for standardized frameworks to allow cross-study comparisons. Moreover, most computational findings lack experimental validation in wet-laboratory settings. Future research should address these challenges, develop scalable algorithms for integrating multi-omics data, and leverage large, open-access datasets. Integrating computational predictions with experimental validation could help researchers prioritize high-confidence biomarkers relevant to clinical applications. Collaborative efforts among bioinformaticians, clinicians, and experimentalists will be essential to translating these advances into clinically actionable solutions.

References

1. 1.Behrens, L. M. P., Fernandes, G. D. S., Gonçalves, G. F., Nunes, F. V. M., Weimer, R. D., Moreira, J. C. F., & Dorn, M. (2026). Limitations and opportunities in multi-omics integration for neurodevelopmental, neurodegenerative and psychiatric disorders: A systematic review. Neuroscience. https://doi.org/10.1016/j.neuroscience.2026.01.019