Porras, L. M. | Search bioRxiv

bioRxiv2026

3D-genome impacts of TAD-boundary-disrupting ultra-rare structural variants identified in schizophrenia

Schizophrenia is a complex and multifactorial brain disorder that impacts ~1% of the U.S. population. The genetic architecture of schizophrenia includes 10 large rare structural variants (SVs) ~10 exonic variants, and over 250 common variants. Previously we combined whole genome sequencing (WGS) with brain-specific epigenomic data and found an excess of ultrarare SVs in schizophrenia localizing to the boundaries of topologically associated domains (TADs). Alterations in TAD boundaries (TAD-b) may lead to disarrangement of enhancer-promoter contacts and dysregulation of gene expression and have been associated with several human diseases. Our study sought to functionally characterize two of these TAD-b SVs using chromatin conformation capture (e.g., Hi-C) and RNA sequencing, to unveil mechanistic insight into the effects of these variants on 3D-genome organization and gene expression. To achieve this, we genetically engineered these two TAD-b SVs in isogenic induced pluripotent stem cells (iPSCs) using CRISPR/Cas9. iPSCs were differentiated into neuronal precursor cells, excitatory glutamatergic neurons, and medium spiny neurons. We found that these TAD-boundary-disrupting SVs could reshape the 3D-genome and transcriptional networks in a cell-type-specific manner.

genomics↗

bioRxiv2025

MarkerMatch: A Proximity-Based Probe-Matching Algorithm for Joint Analysis of Copy-Number Variants from Different Genotyping Arrays

MotivationCopy-number variants (CNVs) are a form of genetic structural variation with increasing importance in complex human disorders. Both DNA sequencing and microarray data can be used to call CNVs, which can be used in association tests, such as association between CNV number and disease status. Unlike genotypes, CNV detection in microarrays requires the use of observed intensity signals at each probe, which limits the imputability for analyses that span multiple array types. Thus far, a consensus set of probes (the intersection encompassing the probes that occur in common on all arrays) has been used to circumvent the problem of differing array-specific sensitivities. This has, however, led to excessive reduction in overall sensitivity of CNV calls as arrays can have an undesirably low overlap of probe sets. To overcome this limitation, we developed MarkerMatch, a proximity-based algorithm that matches probes across different genotyping microarrays to maximize the number of probes considered in the CNV calling algorithm, thereby increasing the resolution and sensitivity while preserving precision. ResultsBy analyzing CNV calls from 4,906 individuals genotyped across three different arrays (Global Screening Array, Omni2.5 array, and Omni Express Exome array), we show that the MarkerMatch approach improves sensitivity by increasing the density of probes available for CNV calling while maintaining precision or improving it relative to the current practice (e.g., use of consensus probes only). We further demonstrate that MarkerMatch exceeds the output from current practice in terms of F1 score, Fowlkes-Mallows index, and Jaccard index. We also optimize MarkerMatch parameters, DMAX and Method, and find an optimal DMAX setting at 10kb, with no clear optimal candidate based on Method, indicating that parameters for this metric should be determined on a use case basis.

bioinformatics↗