The use of single-cell and spatial omics to study copy number variants

Abstract

Copy number variants (CNVs) are structural genomic rearrangements that alter the number of gene copies in genome. Some CNVs are highly penetrant for psychiatric disorders, where the total CNV impact to a neuropsychiatric phenotype is based on potential contribution from every gene it encompasses. However, it can be challenging to associate the typically numerous genes within the CNV to a mechanistic understanding of how brain dysfunction arises. Thus, open questions in CNV research are: how can the genes driving a phenotype be identified from all genes in a CNV and how can molecular mechanisms that lead to brain dysfunction with numerous potential candidate genes be determined? Until recently, these questions could be addressed only by highly laborious experimental setups that include screening individual and combinatorial knockouts of genes within a CNV. With the advance of single-cell and spatial omics, published high-resolution transcriptional data in space and time can help predict those genes that have the highest potential to drive a phenotype and thus pave the way for more efficient studies from genotype to phenotype. In this review, we discuss current progress in single-cell and spatial omics and propose a strategy for implementing these technologies in CNV research.