Computational methods for elucidating the hidden contributions of Structural Variants to complex diseases

Structural variants (SVs) are complex genetic rearrangements of medium to large size (>50 bp) that overall impact more base-pairs of the genome than any other type of genetic variants. These variants are implicated in many diseases, such as neurodevelopmental disorders (NDDs) and cancers. However, our understanding of their contribution to complex diseases remains incomplete. The large-scale studies have mostly focused on non-repetitive regions of genome and coding segments, overlooking potentially relevant areas outside these regions. These limitations are the result of lack of ability to accurately predict and genotype SVs in complex and repetitive regions of the genome, and the complexity of interpreting the functional impact of non-coding SVs. One of the primary objectives of my research is to study the hidden contribution of SVs to complex disorders by addressing these and other shortcomings in our current analysis. Despite the recent advances in computational methods using whole-genome sequencing (WGS) data, accurately predicting and genotyping SVs in repetitive regions of the genome, such as segmental duplications, remains challenging. Even with long-read WGS data, state-of-the-art SV callers are still unable to detect a significant fraction of the SVs in these hard-to-call regions, as demonstrated by the analysis of T2T-CHM13 data. Approximately 15% of the genome comprises regions that are difficult to accurately call variants, and our analysis of the T2T-CHM13 and HG002 assemblies suggests that these regions contain a significant high proportion of SVs. In addition, studying SVs in diseases also requires specialized novel methods, for accurate detection of de novo or somatic SVs. Development of these methods will open the door for comprehensive study of the contribution of SVs in hard-to-call genomic regions to complex disorders. Another major limitation of current studies of SVs in complex disorders is due to challenges in our ability to interpret non-coding SVs. It is hypothesized that non-coding SVs can contribute to complex disorders through a variety of mechanisms. One major such mechanism is the ability of non-coding SVs to disrupt transcriptional regulation. For example, this can occur through changes in the 3D genome architecture, which subsequently modify enhancer-gene interactions and result in ectopic gene expression. Thus, there is a need for development of accurate methods for predicting the impact of non-coding SVs on transcriptional regulation and cell-type specific gene-enhancer interactions. Finally, development of these tools will result in much needed comprehensive investigation of non-coding SVs observed in large-scale complex disorder studies for their impact on transcriptional regulation landscape, 3D genome structure and enhancer-gene interaction. The overall objectives of this proposal are as follows: 1. Dissecting contribution of SVs in hard-to-call genomic regions to complex disorders: Our first objective focuses on deciphering the role of SVs in previously inaccessible and hard-to-call regions of the genome. We will develop innovative methods to enhance the detection and genotyping of SVs, including both de novo and somatic variants, in these regions. We will also leverage these tools to construct a comprehensive catalog of SVs in these regions, utilizing an expanding collection of long-read WGS data from both normal and disease samples. Finally, we will quantify and explore the contribution of SVs in these regions to complex disorders, including autism and cancer. 2. Studying the role of non-coding SVs in complex disorders: Our second objective is to study impact of non-coding SVs to complex disorders. It is hypothesized that certain non-coding SVs can contribute to complex disorders by reshaping the gene regulation landscape. This can involve disrupting 3D genome architecture, altering gene-enhancer interactions, and driving ectopic gene expression. As part of this project we Project Number: 1R35GM161245-01 | Fiscal Year: 2026 | NIH Institute/Center: National Institute of General Medical Sciences (NIGMS) | Principal Investigator: Fereydoun Hormozdiari | Institution: UNIVERSITY OF CALIFORNIA AT DAVIS, DAVIS, CA | Award Amount: $391,526 | Activity Code: R35 | Study Section: Maximizing Investigators' Research Award A Study Section[MRAA] View on NIH RePORTER: https://reporter.nih.gov/project-details/11259225