Decoding causal m6A: a bioinformatics roadmap for psychiatric disorders

AI Summary

m6A is a prevalent RNA modification regulating CNS gene expression; its dysregulation associates with psychiatric disorders and suggests epitranscriptome-targeted therapies.
Rigorous bioinformatics are required: reliably calibrate false positives and apply statistical genetics for causal inference between specific m6A sites and disease phenotypes.
Integrate cell-type-specific functional profiling with machine learning to predict clinical biomarkers; approach validated by intersecting m6A effects with psychiatric genetic risk in MDD.

Brief Bioinform. 2026 May 4;27(3):bbag251. doi: 10.1093/bib/bbag251.

ABSTRACT

N 6-methyladenosine (m6A), the most prevalent internal RNA modification, is an emerging key regulator of gene expression in the central nervous system, and its dysregulation is connected to psychiatric disorders. However, disentangling the causal links between specific m6A sites and diseases phenotypes remain challenging. This review presents a comprehensive survey of practical bioinformatics strategies to address it. Our review outlines four analytical themes: (i) the reliable calibration of false-positive signals, (ii) causal inference via statistical genetics, (iii) the acquisition of cell-type-specific functional insights, and (iv) the application of machine learning to predict clinical biomarkers. We validate these analytical strategies through a case study in major depressive disorder, specifically by intersecting m6A effects with psychiatric genetic risk. By streamlining these workflows, we provide a roadmap for formulating testable hypotheses regarding epitranscriptome-targeted therapeutic interventions in psychiatric disorders.

PMID:42184108 | DOI:10.1093/bib/bbag251

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