Date of Release: July 1, 2025
Expiration Date: June 30, 2027
Credits offered: CME
Estimate time of completion: 1.5 hours
Title: Novel Splice-Driven Mechanisms of Human Genetic Diseases Unveiled by Long-Read RNA-Sequencing
Description
Back by popular demand! This recorded session from the 2025 ACMG Annual Clinical Genetics Meeting returns as a complimentary live webinar, featuring the original faculty for live Q&A.
Alternative splicing has long been associated with disease mechanisms in different disease, cancer, cardiovascular, pulmonary, neurological and Mendelian disorders, but the application of long read sequencing technologies is a relatively new area of research. In recent years, the reduced cost of long read sequencing, as well as the rapid development of long read-specific bioinformatics tools has made possible the studies that will be highlighted by experts in this field during this session. Long read sequencing offers the capability to sequence full-length mRNA transcripts to more directly link splicing quantitative trait loci (sQTLs) to disease-relevant protein alterations, in other words, a more accurate and efficient connection from DNA to protein, through context-specific transcript expression knowledge. Long read sequencing approaches allow for quantification of disease-associated isoforms and prediction of encoded proteins, which provides a path towards mechanistic understanding of disease. In this session, we will present the latest research that demonstrates how long read RNA-seq enables discovery and contextualization of complex alternative splicing contributing to disease pathophysiology. High-throughput long-read RNA-seq enables identification of thousands of novel isoforms that are entirely absent from existing annotations (GENCODE, RefSeq, etc.). We show how the resulting full-length transcript information clarifies the splicing effect for specific variants identified through short-read based sQTL analysis and reveals new sQTLs that were difficult to identify with only short read sequencing data. Further, with the ability to catalog the entire diversity of full-length transcript isoforms in genes known to have complex splicing, we are now able to query whether certain diseases express dominant isoforms across all patients, or whether the expressions are driven by patient-specific isoform expression. In all cases, speakers in this session will highlight the process by which long read RNA-seq identifies novel isoforms that could be implicated in disease progression, including disease subtype specific and patient-specific isoforms. The first talk gives a higher-level overview of how long-read RNA-seq datasets can be used to bring new insight into human molecular genetics (e.g., addressing incomplete transcript annotation, linking sQTLs to full-length isoforms, more direct links between DNA lesions and the protein alterations). This talk will help the audience members understand how the individual talks tie into the theme of the overall session.
Target Audience
This activity is designed for clinical geneticists, genetic counselors, laboratory professionals, and other healthcare providers involved in the diagnosis and management of genetic conditions. The content is also relevant to researchers and healthcare professionals seeking to stay up to date on emerging advancements and best practices in genetic conditions.
Learning Objectives
At the conclusion of this session, participants should be able to:
- Define situations in which short-read or long-read RNA sequencing data is appropriate for various clinical questions
- Discriminate between the types of molecular information derivable from RNA-sequencing versus proteomics data
- Recognize that long read RNA-seq identifies transcripts of disease-associated genes which are not in annotation
- Recognize that for some genes the MANE transcript is not the major transcript in the disease-relevant tissue
- Recognize that transcript mis-annotation has implications for variant interpretation in rare or complex disease setting
- Recognize that transcript mis-annotation has implications for the development of RNA targeting therapies