Detection and analysis of complex structural variation in human genomes across populations and in brains of donors with psychiatric disorders.

Complex structural variations (cxSVs) are often overlooked in genome analyses due to detection challenges. We developed ARC-SV, a probabilistic and machine-learning-based method that enables accurate detection and reconstruction of cxSVs from standard datasets. By applying ARC-SV across 4,262 genomes representing all continental populations, we identified cxSVs as a significant source of natural human genetic variation. Rare cxSVs have a propensity to occur in neural genes and loci that underwent rapid human-specific evolution, including those regulating corticogenesis. By performing single-nucleus multiomics in postmortem brains, we discovered cxSVs associated with differential gene expression and chromatin accessibility across various brain regions and cell types. Additionally, cxSVs detected in brains of psychiatric cases are enriched for linkage with psychiatric GWAS risk alleles detected in the same brains. Furthermore, our analysis revealed significantly decreased brain-region- and cell-type-specific expression of cxSV genes, specifically for psychiatric cases, implicating cxSVs in the molecular etiology of major neuropsychiatric disorders. [Display omitted] • ARC-SV uses machine learning on pangenomes to detect cxSVs with high accuracy • Rare cxSVs in the human population are enriched in neural genes • Rare cxSVs are enriched in loci that underwent rapid human-specific evolution • cxSVs contribute to the genetic architecture of major psychiatric disorders ARC-SV enables highly accurate detection and characterization of localized complex rearrangements of multiple DNA segments. Applying ARC-SV across human populations and brain cohorts uncovers connections between complex structural variantion and human-specific evolution, neural genes, and major psychiatric disorders. [ABSTRACT FROM AUTHOR]