2026 Sequencing Strategies for Population and Cancer Epidemiology Studies (SeqSPACE) : Submission #6

Submission information
Submission Number: 6
Submission ID: 182394
Submission UUID: d89dabcb-d904-45e9-ab2b-5f0be5accf7e
Submission URI: /egrp/seqspaceabstracts

Created: Wed, 06/03/2026 - 13:04
Completed: Wed, 06/03/2026 - 13:04
Changed: Wed, 06/03/2026 - 13:04

Remote IP address: 10.208.28.77
Submitted by: Anonymous
Language: English

Is draft: No
First Name Taylor
Middle Initial
Last Name Head
Degree(s) Ph.D., MSPH
Position/Title/Career Status Postdoctoral Fellow
Organization University of Texas MD Anderson Cancer Center
Email sthead@mdanderson.org
Abstract Title Leveraging long-read RNA-seq for isoform-level regulatory discovery and TWAS fine-mapping in breast cancer
Abstract Summary Most expression quantitative trait locus (eQTL) and transcriptome-wide association study (TWAS) analyses rely on large, tissue-agnostic transcript annotations. These overlook tissue-specific isoform usage and can obscure the true regulatory mechanisms underlying disease-associated loci. Through long-read (LR) RNA-sequencing, we can directly observe full-length transcripts and define tissue-relevant isoforms. However, direct integration of de novo long-read annotations into genetic studies remains challenging due to limited sample sizes with incomplete transcript capture. To address this, we first evaluated how transcript annotation influences regulatory discovery in breast cancer. We quantified gene- and isoform-level expression in breast tumor (TCGA), healthy breast tissue, and cultured fibroblasts using standard GENCODE annotations, tissue-specific LR-derived annotations, and combined annotations. Across tissues, most eGenes were concordant between annotations, but approximately one-third of lead cis-eQTLs for shared eGenes differed. Isoform-level regulatory discovery was substantially more annotation-dependent: in healthy breast tissue, 46% of eIsoforms identified using LR-informed annotations were unique despite 93.7% being present in GENCODE. Although combined annotations expanded the transcript catalog by only 0.6–7.6%, 69% of significant isoform-trait associations were specific to a single annotation. These analyses uncovered candidate regulatory isoforms at established breast cancer risk loci that were missed using conventional transcriptome annotations. Motivated by this work, we next developed a Bayesian isoform fine-mapping framework using isoform-specific priors derived from LR evidence while accounting for structural similarity among transcripts. The model prioritizes tissue-relevant isoforms and reduces spurious prioritization of structurally redundant transcripts. In simulations, the approach improves power while maintaining appropriate type I error relative to existing TWAS fine-mapping methods. Together, these results demonstrate how LR data can be incorporated into regulatory mapping and TWAS fine-mapping to improve prioritization of candidate causal isoforms underlying disease-associated loci.