Integration of Long-Read Sequencing with Multi-Omics Data to Identify Hidden Causal Variants

Genome-wide association studies (GWAS) have identified numerous variants linked to disease, but their focus on single-nucleotide polymorphisms (SNPs) often overlooks structural variants (SVs) that may be causal. Long-read sequencing (LRS) enables superior detection of SVs, including insertions, deletions, and repeat expansions, which are difficult to resolve with short-read sequencing. This session will be an overview of the benefits of long-read sequencing to identify complex variants and explore the integration of multi-omics data to uncover how these variants are having an effect on gene regulation and disease. We will highlight advances in SV-QTL mapping, disease risk prediction, and regulatory element analysis. Speakers will present findings from large-scale cohorts, including neurodegenerative disease studies and population-scale sequencing efforts, demonstrating how LRS enhances variant interpretation. The session will also discuss analytical challenges, emerging technologies, and the role of LRS in expanding genomic discovery across diverse populations.

Learning Objectives

*    Evaluate how long-read sequencing improves structural variant detection and its impact on molecular trait associations in neurodegenerative diseases.
*    Examine the role of tandem repeat expansions in human genetic variation and how long-read sequencing enhances their characterization.
*    Assess the benefits of long-read sequencing in large-scale population studies for identifying medically relevant structural variants and complex loci.