UNDERSTANDING THE FUNCTIONAL IMPACTS OF GENETIC VARIANTS IN MENTAL DISORDERS

了解遗传变异对精神疾病的功能影响

• 批准号: 9389287
• 负责人: Kai Wang
• 金额: $ 38.15万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-11-19 至 2017-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9389287
• 关键词: Address; Algorithms; Autistic Disorder; Bayesian Modeling; Benchmarking; Bioinformatics; Biological; Candidate Disease Gene; Clinical; Code; Communities; Computer software; Computing Methodologies; DNA; Data; Data Set; Development; Disease; Disease model; Ensure; Epigenetic Process; Family; Gene Expression; Gene Proteins; Generations; Genes; Genetic; Genetic Annotation; Genetic Variation; Genome; Genomics; Individual; Informatics; Internet; Knowledge; Life; Machine Learning; Malignant Neoplasms; Mental disorders; Methods; Modeling; Ontology; Outcome; Pathogenicity; Patient Selection; Patients; Performance; Phenotype; Predisposition; Procedures; Psyche structure; Psychiatry; Reporting; Research Methodology; Research Personnel; Role; Sampling; Schizophrenia; Societies; Software Tools; Source; Surveys; System; Techniques; Testing; Text; Uncertainty; Untranslated RNA; Validation; Variant; base; cohort; design; disease phenotype; disorder risk; experimental study; follow-up; genetic information; genetic variant; genome sequencing; genome wide association study; genomic data; high dimensionality; improved; individualized medicine; knowledge integration; novel; personalized therapeutic; prototype; public health relevance; response; skills; social; success; targeted treatment; tool; treatment strategy; user friendly software; user-friendly; web app

 DESCRIPTION (provided by applicant): In the US, 46% are afflicted with a mental illness at some point of life. The management and treatment of mental disorders pose significant economical and social burden to the society. Many rare and common genetic variants, including SNPs and CNVs, are reported to be associated with mental disorders, yet more remain to be discovered. However, despite the large amount of high-throughput genomics data, there is a lack of integrative methods to systematically prioritize variants that confer susceptibility to mental disorders; additionally, despite the presence of many candidate variants for mental disorders, their disease-contributory mechanism remains elusive, so appropriate functional follow-up experiments cannot be designed. Altogether, these problems resulted in a large gap between the copious amount of data about genetic variation and our understanding of their functional impacts on mental diseases, which ultimately delays the development of targeted therapeutic approaches. To address these problems, we propose to develop a suite of novel bioinformatics approaches: (1) NeuroComplex, an information integration approach that leverages phenotype information and multiple sources of prior biological knowledge to rank genes by their likelihood of contributing to specific phenotypic presentations. (2) integrated MEntal-disorder GEnome Score (iMEGES), which leverages a two-layer strategy to predict the impacts of variants in personal genomes on mental disorders. The first layer uses machine learning algorithm to build variant deleteriousness scores for coding, non-coding and structural variants; the second layer integrates variant scores and NeuroComplex to assign pathogenicity scores. (3) a hierarchical model called CombOmics that incorporates cross-omics information to characterizes how variants confers disease risk, in order to help formulate hypothesis of pathogenicity and facilitate the design of functional follow-up experiments. (4) we will develop user-friendly software tools and web applications that implement all the aforementioned bioinformatics approaches, and continuously release and maintain these tools to ensure the maximum benefits to the scientific community.

 描述（由适用提供）：在美国，有46％的人在生活的某个时候患有精神疾病。精神障碍的管理和治疗对社会造成了巨大的经济和社会焚烧。据报道，包括SNP和CNV在内的许多罕见且常见的遗传变异与精神障碍有关，但仍有更多的发现。但是，尽管有大量的高通量基因组学数据，但缺乏系统地将变体优先考虑的综合方法，该变体会使人们对精神疾病的敏感性进行敏感。此外，尽管存在许多用于精神障碍的候选变体，但它们的疾病贡献机制仍然难以捉摸，因此无法设计适当的功能后续实验。总的来说，这些问题导致有关遗传变异的大量数据与我们对它们对精神疾病的功能影响的理解之间的差距很大，这最终延迟了有针对性的治疗方法的发展。为了解决这些问题，我们建议开发一系列新型的生物信息学方法：（1）神经杂物，一种信息整合方法，一种利用表型信息和先前生物学知识的多种来源来对基因进行对基因的贡献，从而有助于其对特定表型表现的贡献。 （2）综合心理基因组评分（IMEGES），它利用两层策略来预测个人基因组对精神疾病的影响。第一层使用机器学习算法来构建用于编码，非编码和结构变体的变体有害分数；第二层整合变体评分和神经复合体以分配致病性评分。 （3）一种称为Combomics的层次模型，该模型包含跨词的信息，以表征变体如何赋予疾病风险，以帮助提出假设（4）我们将开发用户友好的软件工具和Web应用程序，以实施所有优先级的生物信息知识方法，并连续释放和维护这些工具，以确保这些工具最大程度地释放和维护这些益处，从而为科学社区提供最大的利益。