Bruno Takao Real Karia, Vanessa Ota, Renato Polimanti, Marcos Leite Santoro, Sintia Belangero, Carolina Muniz Carvalho
BACKGROUND: The comprehensive elucidation of genomic relevance for mental disorder was, until recently, constrained by the limitations of microarrays or short-read sequencing (SRS). Long-read sequencing (LRS) provides access to complex classes of genetic variation and genomic architecture that are largely obscured in SRS approaches, making it a powerful tool for uncovering missing heritability and mechanistic insights in mental disorders.
OBJECTIVE: We synthesize current evidence on the application of LRS technologies across genomic, transcriptomic, and methylomic studies of mental disorders. The systematic review was conducted in accordance with PRISMA guidelines.
RESULTS AND DISCUSSION: The literature search yielded 2,166 articles, and 14 were included in the review after applying inclusion and exclusion criteria. From a genomic perspective, LRS consistently outperformed SRS, increasing structural variant (SV) and de novo mutation (DNM) detection, particularly resolving medium-sized SVs and large inversions in repetitive regions. Transcriptomic studies established LRS as essential for achieving full-length transcript resolution, uncovering substantial novel isoform diversity and widespread alternative splicing events in the human neocortex. Crucially, LRS demonstrated that psychiatric risk is often conferred by dysregulated alternative splicing leading to isoform-specific imbalances, rather than altered total gene expression. At the epigenomic level, LRS enabled the haplotype-specific mapping of Allele-Specific Methylation within genome-wide significant loci, linking genetic risk to epigenetic dysregulation. Overall, LRS represents a significant technological advancement, providing the high-resolution molecular detail necessary to begin characterizing the complex relationships between genetic variation, splicing dynamics, and epigenetic modifications, thereby helping to contextualize psychiatric risk associations for future translational research.