Rare Mutations and Autism Spectrum Disorders

罕见突变和自闭症谱系障碍

• 批准号: 10530630
• 负责人: Evan Eichler
• 金额: $ 68.19万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-08-01 至 2025-10-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10530630
• 关键词: Affect; Age; Candidate Disease Gene; Child; Clinical; Clinical assessments; Complex; Copy Number Polymorphism; Data; Detection; Disease; Etiology; Event; Exclusion; Family; Family history of; Female; Future; Gene Mutation; Genes; Genetic; Genetic Models; Genetic Predisposition to Disease; Genetic Risk; Genetic Variation; Genome; Genomics; Genotype; Goals; Hereditary Disease; Individual; Inheritance Patterns; Inherited; Large-Scale Sequencing; Maps; Methods; Micro Array Data; Missense Mutation; Modeling; Morbidity - disease rate; Mutation; Nucleotides; Parents; Pathogenicity; Pathway interactions; Patients; Penetrance; Phenotype; Point Mutation; Population Control; Privatization; Recontacts; Recurrence; Resolution; Risk; Risk Factors; Sample Size; Sampling; Single Nucleotide Polymorphism; Technology; Testing; Therapeutic; Validation; Variant; Whole-Genome Shotgun Sequencing; Work; autism spectrum disorder; clinical diagnostics; cohort; de novo mutation; detection sensitivity; dosage; exome; exome sequencing; follow-up; genetic architecture; genetic disorder diagnosis; genetic risk factor; genetic variant; genome sequencing; insight; interest; loss of function mutation; neuropsychiatry; novel; proband; rare variant; research clinical testing; risk variant; sequencing platform; transmission process; variant detection; whole genome

ABSTRACT Sporadic loss-of-function mutations, recurrent missense mutations, and copy number variants (CNVs) contribute significantly to the etiology of autism, but much of the genetic architecture has not yet been understood. Most known pathogenic CNVs are large and the significance of many of these gene mutations is still not known. The goal of this proposal is to significantly increase the yield of high-impact autism mutations by focusing on the discovery of understudied classes of rare variants from whole-genome (n = 35,000 samples) and whole-exome (n = 150,000 samples) sequence data being generated from autism families. This proposal focuses on ultra- rare, gene-disruptive mutations and leverages the additional sensitivity afforded by whole-genome shotgun sequencing data, novel CNV discovery methods, and transmission of ultra-rare inherited mutations to increase yield of pathogenic mutations. Our target will include the discovery and validation of smaller and more complex structural variants (including CNVs) and private gene-disruptive mutations not enriched in de novo mutation but preferentially transmitted to autism children. We will assess the utility of high-fidelity long-read sequencing to discover more complex forms of structural variation that have been missed by standard short-read sequencing by investigating 100 unsolved cases with a higher likelihood of genetic risk. In addition, we propose to select 10 genes with evidence of de novo mutation for further clinical evaluation, phenotypic variability, and comprehensive genetic characterization. This will include five genes where only de novo mutations have been observed compared to five genes where both de novo and inherited mutations have been documented in order to understand carrier phenotypes. This proposal specifically focuses on the application of novel genomic methods, recurrent mutations, and inheritance patterns to discover pathogenic variants in order to develop a more sophisticated model to explain the genetic architecture of autism. As part of this effort, we will quantify and compare the risk of different classes of mutation for autism and investigate transmission disequilibrium differences. The end product of this analysis will be the identification and characterization of new classes of highly penetrant genic mutations that contribute significantly to etiology of autism, providing targets for clinical diagnostics and future therapeutics.

抽象的 零星的功能丧失突变，反复的错义突变和拷贝数变体（CNV）贡献 自闭症的病因很重要，但是尚未理解许多遗传结构。最多 已知的致病性CNV很大，许多这些基因突变的重要性尚不清楚。这 该提案的目标是通过关注于 从全基因组（n = 35,000个样品）和全象征中发现细小的稀有变体类别 （n = 150,000个样本）由自闭症家族产生的序列数据。该提案的重点是 罕见的，基因消失的突变和利用全基因组shot弹枪提供的额外灵敏度 测序数据，新颖的CNV发现方法和超稀有遗传突变的传播以增加 致病突变的产量。我们的目标将包括发现和验证较小，更复杂的 结构性变异（包括CNV）和私有基因干扰性突变不丰富从头突变而是 优先传播给自闭症儿童。我们将评估高保真性长阅读测序的实用性 发现更复杂的结构变化形式，这些形式被标准的短阅读测序遗漏了 通过研究100例未解决的病例，其遗传风险可能性更高。此外，我们建议选择10 具有从头突变的证据，用于进一步的临床评估，表型变异性和全面 遗传特征。这将包括仅观察到从头突变的五个基因 与五个基因相比，已经记录了从头和遗传突变以进行的五个基因 了解载体表型。该建议特别关注新型基因组方法的应用 复发突变和遗传模式，以发现致病性变体，以发展更多 解释自闭症遗传结构的复杂模型。作为这项工作的一部分，我们将量化和 比较自闭症不同类别突变的风险，并研究传播不平衡的风险 差异。该分析的最终产品将是新类的识别和表征 高度渗透的基因突变对自闭症的病因有显着贡献，为临床提供了靶标 诊断和未来治疗学。