Parallel Sequence Profiling of Ion Channels in Epilepsy

描述（由申请人提供）：离子通道基因占人类基因组的1.5%，这些基因的遗传突变会引发多种脑、神经、肌肉和心脏的临床疾病。在大脑中，单基因通道病是罕见的孟德尔特发性癫痫综合征的主要原因（13/14），但它们对常见的散发性癫痫的贡献尚不清楚。高新生突变率和复杂的多基因遗传（“常见病-常见病变异”模型）是离子通道变异在散发性病例中所起作用的两个有吸引力的解释。离子通道除了具有重要的致病作用外，也是大多数抗癫痫药物的主要分子靶点，通道亚基的遗传变异可能独立地促进药物耐药。本项目将离子通道病和特定癫痫表型的基础和临床研究与贝勒人类基因组测序中心的大规模基因测序能力和突变分析资源相结合，以验证对个体癫痫患者中大量通道基因的编码序列进行分析可以揭示决定癫痫易感性和耐药的常见等位基因变异的新突变和模式的一般假设。我们将完成多重引物阵列的开发和优化，该阵列允许在500例具有特定临床癫痫表型的患者和500例种族匹配的对照中对100个候选离子通道基因进行快速和可扩展的平行外显子测序。建立人类离子通道基因变异公共数据库，促进数据共享。这些数据将以两种方式使用。首先，将在哺乳动物表达系统中分析具有预测蛋白编码变异的通道基因多态性子集的生物物理和药理学特性，以确定与癫痫相关的人类离子通道功能基因变异子集。这一清单对于检查与癫痫相关的离子通道的特定病理生理特性有关的模型是必不可少的。其次，100个通道基因的序列将被组装成每个个体的轮廓（他们的“通道型”），并用于测试不同通道型与癫痫表型的统计关联。对50名患者和50名对照组的7个通道基因的所有外显子进行初步分析，发现了新的和以前报道的snp（编码和非编码）和微缺失，验证了数据收集管道的效率。使用机器人处理和自动突变检测算法，我们将扩大基因和患者的数量，以获得解决通道型-表型关联假设的统计能力。本研究确定的关联将解决癫痫复杂遗传学背后的主要假设，加速癫痫个体化临床风险评估的发展，并研究患有常见特发性癫痫的儿童和成人抗癫痫药物耐药性的新机制。

DESCRIPTION (provided by applicant): Ion channel genes represent 1.5% of the human genome, and inherited mutations of these genes elicit a diverse array of clinical disorders of brain, nerve, muscle and heart. In brain, single gene channelopathies are the predominant cause (13/14) of rare mendelian idiopathic epilepsy syndromes, but their contribution to common sporadic epilepsy is unknown. High rates of de novo mutation and complex polygenic inheritance (the "common disease-common variant" model) are two attractive explanations for the role of ion channel variation in sporadic cases. Together with their important pathogenic role, ion channels are also the primary molecular targets of most antiepileptic drugs, and genetic variation in channel subunits may independently contribute to pharmacoresistance. This project combines basic and clinical research on ion channelopathy and specific epilepsy phenotypes with the large scale gene sequencing capacity and mutation analysis resources of the Baylor Human Genome Sequencing Center in order to test the general hypothesis that profiling the coding sequences of large numbers of channel genes in individual epilepsy patients can reveal novel mutations and patterns of common allelic variants that determine epilepsy susceptibility and pharmacoresistance. We will complete the development and optimization of a multiplex primer array that allows rapid and scalable parallel exon sequencing of 100 candidate ion channel genes in 500 patients with specific clinical epilepsy phenotypes and in 500 ethnically-matched controls. A public database of human ion channel gene variation will be generated to facilitate data-sharing. These data will be used in two ways. First, the biophysical and pharmacological properties of a subset of channel gene polymorphisms with predicted protein coding variation will be analyzed in mammalian expression systems in order to define a validated subset of functional gene variants of human ion channels relevant to epilepsy. This list is essential to examine models relating specific pathophysiological properties of ion channels to the patterns associated with epilepsy. Second, the sequence of the 100 channel genes will be assembled into a profile of each individual (their "channotype") and used to test the statistical association of different channotypes with epilepsy phenotypes. Preliminary analysis of all exons of 7 channel genes in 50 patients and 50 controls has detected novel and previously reported SNPs (coding and non-coding) and microdeletions, validating the efficiency of the data collection pipeline. Using robotic processing and automated mutation detection algorithms, we will scale the number of genes and patients to attain the statistical power to address the channotype-phenotype association hypotheses. The associations identified in this study will address a major hypothesis underlying the complex genetics of epilepsy, accelerate development of individualized clinical risk assessments for epilepsy, and examine a novel mechanism of resistance to antiepileptic drugs in children and adults with common idiopathic forms of the disorder.