학술논문

The epilepsy phenome/genome project.
Document Type
article
Source
Clinical trials (London, England). 10(4)
Subject
EPGP Collaborative
Humans
Epilepsy
Oligonucleotide Array Sequence Analysis
Retrospective Studies
Genetic Research
Genotype
Phenotype
Research Design
Information Management
Clinical Research
Neurodegenerative
Neurosciences
Brain Disorders
Genetics
2.1 Biological and endogenous factors
Aetiology
Neurological
Statistics
Clinical Sciences
Statistics & Probability
Language
Abstract
BackgroundEpilepsy is a common neurological disorder that affects approximately 50 million people worldwide. Both risk of epilepsy and response to treatment partly depend on genetic factors, and gene identification is a promising approach to target new prediction, treatment, and prevention strategies. However, despite significant progress in the identification of genes causing epilepsy in families with a Mendelian inheritance pattern, there is relatively little known about the genetic factors responsible for common forms of epilepsy and so-called epileptic encephalopathies. Study design The Epilepsy Phenome/Genome Project (EPGP) is a multi-institutional, retrospective phenotype-genotype study designed to gather and analyze detailed phenotypic information and DNA samples on 5250 participants, including probands with specific forms of epilepsy and, in a subset, parents of probands who do not have epilepsy.ResultsEPGP is being executed in four phases: study initiation, pilot, study expansion/establishment, and close-out. This article discusses a number of key challenges and solutions encountered during the first three phases of the project, including those related to (1) study initiation and management, (2) recruitment and phenotyping, and (3) data validation. The study has now enrolled 4223 participants.ConclusionsEPGP has demonstrated the value of organizing a large network into cores with specific roles, managed by a strong Administrative Core that utilizes frequent communication and a collaborative model with tools such as study timelines and performance-payment models. The study also highlights the critical importance of an effective informatics system, highly structured recruitment methods, and expert data review.