Characterizing the genetic systems of autism through multi-disease analysis

通过多种疾病分析表征自闭症遗传系统

• 批准号: 8527985
• 负责人: Dennis Paul Wall
• 金额: $ 12.03万
• 依托单位: HARVARD MEDICAL SCHOOL
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-03-05 至 2014-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8527985
• 关键词: Address; Algorithms; Autistic Disorder; Behavior; Behavioral; Binding; Bioinformatics; Biological Process; Boston; Breathing; Candidate Disease Gene; Cells; Characteristics; Communities; Companions; Complex; Data; Databases; Development; Disease; Ensure; Foundations; Genes; Genetic; Genetic Predisposition to Disease; Genetic Variation; Genomics; Genotype; Housing; Imagery; Individual; Intention; Internet; Investigation; Knowledge; Light; Link; Maps; Measurement; Medical Libraries; Methods; Mining; Molecular; Molecular Profiling; National Institute of Neurological Disorders and Stroke; Navigation System; Online Systems; Pathway Analysis; Pediatric Hospitals; Phenotype; Phylogenetic Analysis; Pilot Projects; Positioning Attribute; Proteins; Publishing; Research; Research Personnel; Resources; Role; Sampling; Site; Solutions; Source; System; Testing; Text; Therapeutic Intervention; Trust; Update; Variant; Vertebral column; Weight; Work; abstracting; base; comparative; data integration; gene interaction; genetic analysis; genetic variant; genome-wide; grasp; improved; mRNA Expression; medical schools; member; nervous system disorder; open source; protein protein interaction; public health relevance; relational database; research study; text searching; tool; trend; web site

DESCRIPTION (provided by applicant): Autism is a complex disorder with a wide spectrum of phenotypes. Although it is clearly heritable, the molecular agents responsible remain elusive. More than 100 genes have been tied to Autism, each of which is involved in numerous different biological processes and in a variety of different molecular interactions. No single researcher can completely grasp the complexity of this Autism gene space, and perhaps for this reason, few genes have emerged as promising markers or targets for therapeutic intervention. Our plan is provide a way to grasp this complexity by shifting the focus from single genes to the entire genetic system of Autism. Thus, we will build the complete network of molecular interactions for all Autism candidate genes using bioinformatic methods that integrate multiple sources of genomic and bibliomic information. While this network will be a powerful enabler of new discoveries in Autism, it will not be enough to fully grasp the genetic underpinnings of the various behaviors indicative of the disorder. Hope for that however lies in the behavioral similarities between Autism and numerous other neurological disorders. These behavioral similarities suggest that there are common molecular mechanisms that if understood could help provide a clearer genotype-phenotype map of the Autism spectrum. Our plan is to capitalize on these similarities by conducting a comprehensive comparative analysis of the Autism network with the networks of more than 400 neurological disorders. Our work will result in a systems level view of Autism and its most similar neurological disorders that will not only help to see emergent trends that clarify the genetic basis of the spectrum, but will also help to prioritize known Autism candidates and reveal new candidates worthy of investigation. All of our work will be made freely accessible in a web-based tool that allows complete navigation through the Autism network and the networks of all other related neurological disorders. Wall & Kohane Abstract 1 PUBLIC HEALTH RELEVANCE: The genetic component of Autism remains unknown, but current research indicates that it is most likely the result of combined effects of many different genetic variants in possibly hundreds of genes. Grasping the complexity of this genetic land scape is a significant challenge for Autism researchers, and requires sophisticated bioinformatic solutions that are readily accessible to all members of the research community. We propose to build a web-based system called Autworks that is at once an up-to-date central clearing house for all information relevant to the genetic component of Autism and a powerful research tool that allows researchers to view the genetic component of Autism as a network and in light of research results on related neurological disorders.

描述（由申请人提供）：自闭症是一种复杂的疾病，具有广泛的表型。 虽然它显然是可遗传的，但负责的分子因子仍然难以捉摸。 超过100个基因与自闭症有关，每个基因都涉及许多不同的生物过程和各种不同的分子相互作用。 没有一个研究人员能够完全掌握自闭症基因空间的复杂性，也许正是因为这个原因，很少有基因成为有希望的标记或治疗干预的靶点。 我们的计划是通过将焦点从单个基因转移到自闭症的整个遗传系统来提供一种掌握这种复杂性的方法。 因此，我们将建立完整的网络分子相互作用的所有自闭症候选基因使用生物信息学方法，整合多个来源的基因组和书目信息。 虽然这个网络将成为自闭症新发现的有力推动者，但它不足以完全掌握指示这种疾病的各种行为的遗传基础。 然而，希望在于自闭症和许多其他神经系统疾病之间的行为相似性。 这些行为相似性表明，如果理解这些共同的分子机制，可以帮助提供更清晰的自闭症谱系的基因型-表型图。 我们的计划是利用这些相似性，对自闭症网络与400多种神经系统疾病的网络进行全面的比较分析。 我们的工作将导致自闭症及其最相似的神经系统疾病的系统水平视图，这不仅有助于看到澄清谱系遗传基础的新兴趋势，而且还有助于优先考虑已知的自闭症候选人，并揭示值得研究的新候选人。 我们所有的工作都将在一个基于网络的工具中免费访问，该工具允许通过自闭症网络和所有其他相关神经系统疾病的网络进行完整的导航。 Wall & Kohane摘要1 公共卫生相关性：自闭症的遗传成分仍然未知，但目前的研究表明，它很可能是数百个基因中许多不同遗传变异的综合影响的结果。 掌握这种遗传景观的复杂性对自闭症研究人员来说是一个重大挑战，需要复杂的生物信息学解决方案，研究社区的所有成员都可以随时访问。 我们建议建立一个名为Autworks的基于网络的系统，该系统既是一个最新的中央信息交换所，用于与自闭症遗传成分相关的所有信息，也是一个强大的研究工具，使研究人员能够将自闭症的遗传成分视为一个网络，并根据相关神经系统疾病的研究结果。