Developmental and Epileptic Encephalopathies Diagnosed Via Long-read Genome Sequencing

Overview

This study focuses on children with Developmental and Epileptic Encephalopathy (DEE), a severe form of epilepsy that often has a genetic origin. Currently, standard diagnostic tools-known as short-read genome sequencing-fail to provide a diagnosis for over 50% of affected patients because they cannot detect certain complex DNA abnormalities. The purpose of this study is to evaluate the effectiveness of a newer, more advanced technology called Long-read Genome Sequencing (lrWGS). Unlike traditional methods, this technology analyzes very long fragments of DNA, allowing researchers to identify genetic errors that were previously "invisible." The study aims to answer whether Long-read Sequencing can successfully identify the genetic cause of epilepsy in patients who have already received a negative result from standard testing. By finding these missing answers, the research seeks to enable personalized medical treatments, improve genetic counseling for families, and advance our understanding of how these complex neurological conditions develop.

This study evaluates the clinical utility of Long-read Whole Genome Sequencing (lrWGS) as a secondary diagnostic tool for Developmental and Epileptic Encephalopathies (DEEs). While current standard-of-care "short-read" sequencing (srWGS) is effective at identifying small genetic mutations, it often fails to detect complex structural changes, leaving over 50% of patients without a diagnosis. Technically, lrWGS overcomes these limitations by analyzing DNA fragments that are thousands of bases long. This allows researchers to map "dark regions" of the genome and identify Structural Variants (SVs), such as large insertions, deletions, or repeat expansions, which are often the hidden causes of severe epilepsy. By identifying these elusive genetic drivers, the study aims to move beyond a simple diagnosis toward precision medicine. A clear molecular result can directly influence clinical decisions, such as selecting targeted medications, avoiding contraindicated drugs, or determining eligibility for emerging gene therapies. Additionally, the project assesses the feasibility of integrating this technology into routine clinical practice by evaluating bioinformatic complexity and diagnostic turnaround times.